repo_id
stringlengths 15
89
| file_path
stringlengths 27
180
| content
stringlengths 1
2.23M
| __index_level_0__
int64 0
0
|
|---|---|---|---|
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/camembert/__init__.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
"configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_camembert"] = ["CamembertTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_camembert_fast"] = ["CamembertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_camembert"] = [
"CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"CamembertForCausalLM",
"CamembertForMaskedLM",
"CamembertForMultipleChoice",
"CamembertForQuestionAnswering",
"CamembertForSequenceClassification",
"CamembertForTokenClassification",
"CamembertModel",
"CamembertPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_camembert"] = [
"TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFCamembertForCausalLM",
"TFCamembertForMaskedLM",
"TFCamembertForMultipleChoice",
"TFCamembertForQuestionAnswering",
"TFCamembertForSequenceClassification",
"TFCamembertForTokenClassification",
"TFCamembertModel",
"TFCamembertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_camembert import CamembertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_camembert_fast import CamembertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_camembert import (
CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
CamembertForCausalLM,
CamembertForMaskedLM,
CamembertForMultipleChoice,
CamembertForQuestionAnswering,
CamembertForSequenceClassification,
CamembertForTokenClassification,
CamembertModel,
CamembertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_camembert import (
TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCamembertForCausalLM,
TFCamembertForMaskedLM,
TFCamembertForMultipleChoice,
TFCamembertForQuestionAnswering,
TFCamembertForSequenceClassification,
TFCamembertForTokenClassification,
TFCamembertModel,
TFCamembertPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/camembert/modeling_camembert.py | # coding=utf-8
# Copyright 2019 Inria, Facebook AI Research and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch CamemBERT model."""
import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN, gelu
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
MaskedLMOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_camembert import CamembertConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "camembert-base"
_CONFIG_FOR_DOC = "CamembertConfig"
CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"camembert-base",
"Musixmatch/umberto-commoncrawl-cased-v1",
"Musixmatch/umberto-wikipedia-uncased-v1",
# See all CamemBERT models at https://huggingface.co/models?filter=camembert
]
CAMEMBERT_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`CamembertConfig`]): Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
# Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings with Roberta->Camembert
class CamembertEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.register_buffer(
"token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False
)
# End copy
self.padding_idx = config.pad_token_id
self.position_embeddings = nn.Embedding(
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
)
def forward(
self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
):
if position_ids is None:
if input_ids is not None:
# Create the position ids from the input token ids. Any padded tokens remain padded.
position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
else:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
"""
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
"""
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
)
return position_ids.unsqueeze(0).expand(input_shape)
# Copied from transformers.models.roberta.modeling_roberta.RobertaSelfAttention with Roberta->Camembert
class CamembertSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(
config, "position_embedding_type", "absolute"
)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
use_cache = past_key_value is not None
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
query_length, key_length = query_layer.shape[2], key_layer.shape[2]
if use_cache:
position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
-1, 1
)
else:
position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in CamembertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
# Copied from transformers.models.roberta.modeling_roberta.RobertaSelfOutput with Roberta->Camembert
class CamembertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.roberta.modeling_roberta.RobertaAttention with Roberta->Camembert
class CamembertAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = CamembertSelfAttention(config, position_embedding_type=position_embedding_type)
self.output = CamembertSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Roberta->Camembert
class CamembertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->Roberta->Camembert
class CamembertOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.roberta.modeling_roberta.RobertaLayer with Roberta->Camembert
class CamembertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = CamembertAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
self.crossattention = CamembertAttention(config, position_embedding_type="absolute")
self.intermediate = CamembertIntermediate(config)
self.output = CamembertOutput(config)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and encoder_hidden_states is not None:
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
" by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
cross_attn_past_key_value,
output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
# Copied from transformers.models.roberta.modeling_roberta.RobertaEncoder with Roberta->Camembert
class CamembertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([CamembertLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_values[i] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertPooler
class CamembertPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
class CamembertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = CamembertConfig
base_model_prefix = "roberta"
supports_gradient_checkpointing = True
# Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
CAMEMBERT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
# Copied from transformers.models.roberta.modeling_roberta.RobertaClassificationHead with Roberta->Camembert
class CamembertClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, features, **kwargs):
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
# Copied from transformers.models.roberta.modeling_roberta.RobertaLMHead with Roberta->Camembert
class CamembertLMHead(nn.Module):
"""Camembert Head for masked language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
self.decoder.bias = self.bias
def forward(self, features, **kwargs):
x = self.dense(features)
x = gelu(x)
x = self.layer_norm(x)
# project back to size of vocabulary with bias
x = self.decoder(x)
return x
def _tie_weights(self):
# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
# For accelerate compatibility and to not break backward compatibility
if self.decoder.bias.device.type == "meta":
self.decoder.bias = self.bias
else:
self.bias = self.decoder.bias
@add_start_docstrings(
"The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.",
CAMEMBERT_START_DOCSTRING,
)
class CamembertModel(CamembertPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in *Attention is
all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz
Kaiser and Illia Polosukhin.
To behave as a decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to
`True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
.. _*Attention is all you need*: https://arxiv.org/abs/1706.03762
"""
_no_split_modules = []
# Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Camembert
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = CamembertEmbeddings(config)
self.encoder = CamembertEncoder(config)
self.pooler = CamembertPooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
# Copied from transformers.models.bert.modeling_bert.BertModel.forward
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
@add_start_docstrings(
"""CamemBERT Model with a `language modeling` head on top.""",
CAMEMBERT_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM with Roberta->Camembert, ROBERTA->CAMEMBERT
class CamembertForMaskedLM(CamembertPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `CamembertForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
self.roberta = CamembertModel(config, add_pooling_layer=False)
self.lm_head = CamembertLMHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.lm_head.decoder
def set_output_embeddings(self, new_embeddings):
self.lm_head.decoder = new_embeddings
@add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
mask="<mask>",
expected_output="' Paris'",
expected_loss=0.1,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
kwargs (`Dict[str, any]`, optional, defaults to *{}*):
Used to hide legacy arguments that have been deprecated.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
masked_lm_loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(prediction_scores.device)
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
""",
CAMEMBERT_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification with Roberta->Camembert, ROBERTA->CAMEMBERT
class CamembertForSequenceClassification(CamembertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.roberta = CamembertModel(config, add_pooling_layer=False)
self.classifier = CamembertClassificationHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="cardiffnlp/twitter-roberta-base-emotion",
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="'optimism'",
expected_loss=0.08,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
CamemBERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
CAMEMBERT_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_roberta.RobertaForMultipleChoice with Roberta->Camembert, ROBERTA->CAMEMBERT
class CamembertForMultipleChoice(CamembertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.roberta = CamembertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(
CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
flat_inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.roberta(
flat_input_ids,
position_ids=flat_position_ids,
token_type_ids=flat_token_type_ids,
attention_mask=flat_attention_mask,
head_mask=head_mask,
inputs_embeds=flat_inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(reshaped_logits.device)
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
CamemBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks.
""",
CAMEMBERT_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_roberta.RobertaForTokenClassification with Roberta->Camembert, ROBERTA->CAMEMBERT
class CamembertForTokenClassification(CamembertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta = CamembertModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="Jean-Baptiste/roberta-large-ner-english",
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']",
expected_loss=0.01,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
CamemBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`
""",
CAMEMBERT_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_roberta.RobertaForQuestionAnswering with Roberta->Camembert, ROBERTA->CAMEMBERT
class CamembertForQuestionAnswering(CamembertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta = CamembertModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="deepset/roberta-base-squad2",
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="' puppet'",
expected_loss=0.86,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""CamemBERT Model with a `language modeling` head on top for CLM fine-tuning.""", CAMEMBERT_START_DOCSTRING
)
# Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM with Roberta->Camembert, ROBERTA->CAMEMBERT, roberta-base->camembert-base
class CamembertForCausalLM(CamembertPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `CamembertLMHeadModel` as a standalone, add `is_decoder=True.`")
self.roberta = CamembertModel(config, add_pooling_layer=False)
self.lm_head = CamembertLMHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.lm_head.decoder
def set_output_embeddings(self, new_embeddings):
self.lm_head.decoder = new_embeddings
@add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
past_key_values: Tuple[Tuple[torch.FloatTensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, CamembertForCausalLM, AutoConfig
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("camembert-base")
>>> config = AutoConfig.from_pretrained("camembert-base")
>>> config.is_decoder = True
>>> model = CamembertForCausalLM.from_pretrained("camembert-base", config=config)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
lm_loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(prediction_scores.device)
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((lm_loss,) + output) if lm_loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=lm_loss,
logits=prediction_scores,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs):
input_shape = input_ids.shape
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_shape)
# cut decoder_input_ids if past_key_values is used
if past_key_values is not None:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_length:]
return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values}
def _reorder_cache(self, past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
# Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`.
Args:
x: torch.Tensor x:
Returns: torch.Tensor
"""
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
mask = input_ids.ne(padding_idx).int()
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
return incremental_indices.long() + padding_idx
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/pvt/convert_pvt_to_pytorch.py | # coding=utf-8
# Copyright 2023 Authors: Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan,
# Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and The HuggingFace Inc. team.
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert Pvt checkpoints from the original library."""
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import PvtConfig, PvtForImageClassification, PvtImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
def create_rename_keys(config):
rename_keys = []
for i in range(config.num_encoder_blocks):
# Remane embedings' paramters
rename_keys.append((f"pos_embed{i + 1}", f"pvt.encoder.patch_embeddings.{i}.position_embeddings"))
rename_keys.append((f"patch_embed{i + 1}.proj.weight", f"pvt.encoder.patch_embeddings.{i}.projection.weight"))
rename_keys.append((f"patch_embed{i + 1}.proj.bias", f"pvt.encoder.patch_embeddings.{i}.projection.bias"))
rename_keys.append((f"patch_embed{i + 1}.norm.weight", f"pvt.encoder.patch_embeddings.{i}.layer_norm.weight"))
rename_keys.append((f"patch_embed{i + 1}.norm.bias", f"pvt.encoder.patch_embeddings.{i}.layer_norm.bias"))
for j in range(config.depths[i]):
# Rename blocks' parameters
rename_keys.append(
(f"block{i + 1}.{j}.attn.q.weight", f"pvt.encoder.block.{i}.{j}.attention.self.query.weight")
)
rename_keys.append(
(f"block{i + 1}.{j}.attn.q.bias", f"pvt.encoder.block.{i}.{j}.attention.self.query.bias")
)
rename_keys.append(
(f"block{i + 1}.{j}.attn.kv.weight", f"pvt.encoder.block.{i}.{j}.attention.self.kv.weight")
)
rename_keys.append((f"block{i + 1}.{j}.attn.kv.bias", f"pvt.encoder.block.{i}.{j}.attention.self.kv.bias"))
if config.sequence_reduction_ratios[i] > 1:
rename_keys.append(
(
f"block{i + 1}.{j}.attn.norm.weight",
f"pvt.encoder.block.{i}.{j}.attention.self.layer_norm.weight",
)
)
rename_keys.append(
(f"block{i + 1}.{j}.attn.norm.bias", f"pvt.encoder.block.{i}.{j}.attention.self.layer_norm.bias")
)
rename_keys.append(
(
f"block{i + 1}.{j}.attn.sr.weight",
f"pvt.encoder.block.{i}.{j}.attention.self.sequence_reduction.weight",
)
)
rename_keys.append(
(
f"block{i + 1}.{j}.attn.sr.bias",
f"pvt.encoder.block.{i}.{j}.attention.self.sequence_reduction.bias",
)
)
rename_keys.append(
(f"block{i + 1}.{j}.attn.proj.weight", f"pvt.encoder.block.{i}.{j}.attention.output.dense.weight")
)
rename_keys.append(
(f"block{i + 1}.{j}.attn.proj.bias", f"pvt.encoder.block.{i}.{j}.attention.output.dense.bias")
)
rename_keys.append((f"block{i + 1}.{j}.norm1.weight", f"pvt.encoder.block.{i}.{j}.layer_norm_1.weight"))
rename_keys.append((f"block{i + 1}.{j}.norm1.bias", f"pvt.encoder.block.{i}.{j}.layer_norm_1.bias"))
rename_keys.append((f"block{i + 1}.{j}.norm2.weight", f"pvt.encoder.block.{i}.{j}.layer_norm_2.weight"))
rename_keys.append((f"block{i + 1}.{j}.norm2.bias", f"pvt.encoder.block.{i}.{j}.layer_norm_2.bias"))
rename_keys.append((f"block{i + 1}.{j}.mlp.fc1.weight", f"pvt.encoder.block.{i}.{j}.mlp.dense1.weight"))
rename_keys.append((f"block{i + 1}.{j}.mlp.fc1.bias", f"pvt.encoder.block.{i}.{j}.mlp.dense1.bias"))
rename_keys.append((f"block{i + 1}.{j}.mlp.fc2.weight", f"pvt.encoder.block.{i}.{j}.mlp.dense2.weight"))
rename_keys.append((f"block{i + 1}.{j}.mlp.fc2.bias", f"pvt.encoder.block.{i}.{j}.mlp.dense2.bias"))
# Rename cls token
rename_keys.extend(
[
("cls_token", "pvt.encoder.patch_embeddings.3.cls_token"),
]
)
# Rename norm layer and classifier layer
rename_keys.extend(
[
("norm.weight", "pvt.encoder.layer_norm.weight"),
("norm.bias", "pvt.encoder.layer_norm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
]
)
return rename_keys
# we split up the matrix of each encoder layer into queries, keys and values
def read_in_k_v(state_dict, config):
# for each of the encoder blocks:
for i in range(config.num_encoder_blocks):
for j in range(config.depths[i]):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
kv_weight = state_dict.pop(f"pvt.encoder.block.{i}.{j}.attention.self.kv.weight")
kv_bias = state_dict.pop(f"pvt.encoder.block.{i}.{j}.attention.self.kv.bias")
# next, add keys and values (in that order) to the state dict
state_dict[f"pvt.encoder.block.{i}.{j}.attention.self.key.weight"] = kv_weight[: config.hidden_sizes[i], :]
state_dict[f"pvt.encoder.block.{i}.{j}.attention.self.key.bias"] = kv_bias[: config.hidden_sizes[i]]
state_dict[f"pvt.encoder.block.{i}.{j}.attention.self.value.weight"] = kv_weight[
config.hidden_sizes[i] :, :
]
state_dict[f"pvt.encoder.block.{i}.{j}.attention.self.value.bias"] = kv_bias[config.hidden_sizes[i] :]
def rename_key(dct, old, new):
val = dct.pop(old)
dct[new] = val
# We will verify our results on an image of cute cats
def prepare_img():
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
im = Image.open(requests.get(url, stream=True).raw)
return im
@torch.no_grad()
def convert_pvt_checkpoint(pvt_size, pvt_checkpoint, pytorch_dump_folder_path):
"""
Copy/paste/tweak model's weights to our PVT structure.
"""
# define default Pvt configuration
if pvt_size == "tiny":
config_path = "Zetatech/pvt-tiny-224"
elif pvt_size == "small":
config_path = "Zetatech/pvt-small-224"
elif pvt_size == "medium":
config_path = "Zetatech/pvt-medium-224"
elif pvt_size == "large":
config_path = "Zetatech/pvt-large-224"
else:
raise ValueError(f"Available model's size: 'tiny', 'small', 'medium', 'large', but " f"'{pvt_size}' was given")
config = PvtConfig(name_or_path=config_path)
# load original model from https://github.com/whai362/PVT
state_dict = torch.load(pvt_checkpoint, map_location="cpu")
rename_keys = create_rename_keys(config)
for src, dest in rename_keys:
rename_key(state_dict, src, dest)
read_in_k_v(state_dict, config)
# load HuggingFace model
model = PvtForImageClassification(config).eval()
model.load_state_dict(state_dict)
# Check outputs on an image, prepared by PVTFeatureExtractor
image_processor = PvtImageProcessor(size=config.image_size)
encoding = image_processor(images=prepare_img(), return_tensors="pt")
pixel_values = encoding["pixel_values"]
outputs = model(pixel_values)
logits = outputs.logits.detach().cpu()
if pvt_size == "tiny":
expected_slice_logits = torch.tensor([-1.4192, -1.9158, -0.9702])
elif pvt_size == "small":
expected_slice_logits = torch.tensor([0.4353, -0.1960, -0.2373])
elif pvt_size == "medium":
expected_slice_logits = torch.tensor([-0.2914, -0.2231, 0.0321])
elif pvt_size == "large":
expected_slice_logits = torch.tensor([0.3740, -0.7739, -0.4214])
else:
raise ValueError(f"Available model's size: 'tiny', 'small', 'medium', 'large', but " f"'{pvt_size}' was given")
assert torch.allclose(logits[0, :3], expected_slice_logits, atol=1e-4)
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
print(f"Saving model pytorch_model.bin to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
print(f"Saving image processor to {pytorch_dump_folder_path}")
image_processor.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--pvt_size",
default="tiny",
type=str,
help="Size of the PVT pretrained model you'd like to convert.",
)
parser.add_argument(
"--pvt_checkpoint",
default="pvt_tiny.pth",
type=str,
help="Checkpoint of the PVT pretrained model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
args = parser.parse_args()
convert_pvt_checkpoint(args.pvt_size, args.pvt_checkpoint, args.pytorch_dump_folder_path)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/pvt/configuration_pvt.py | # coding=utf-8
# Copyright 2023 Authors: Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan,
# Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and The HuggingFace Inc. team.
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Pvt model configuration"""
from collections import OrderedDict
from typing import Callable, List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
PVT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"pvt-tiny-224": "https://huggingface.co/Zetatech/pvt-tiny-224",
# See all PVT models at https://huggingface.co/models?filter=pvt
}
class PvtConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PvtModel`]. It is used to instantiate an Pvt
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Pvt
[Xrenya/pvt-tiny-224](https://huggingface.co/Xrenya/pvt-tiny-224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
image_size (`int`, *optional*, defaults to 224):
The input image size
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
num_encoder_blocks (`int`, *optional*, defaults to 4):
The number of encoder blocks (i.e. stages in the Mix Transformer encoder).
depths (`List[int]`, *optional*, defaults to `[2, 2, 2, 2]`):
The number of layers in each encoder block.
sequence_reduction_ratios (`List[int]`, *optional*, defaults to `[8, 4, 2, 1]`):
Sequence reduction ratios in each encoder block.
hidden_sizes (`List[int]`, *optional*, defaults to `[64, 128, 320, 512]`):
Dimension of each of the encoder blocks.
patch_sizes (`List[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
Patch size before each encoder block.
strides (`List[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
Stride before each encoder block.
num_attention_heads (`List[int]`, *optional*, defaults to `[1, 2, 5, 8]`):
Number of attention heads for each attention layer in each block of the Transformer encoder.
mlp_ratios (`List[int]`, *optional*, defaults to `[8, 8, 4, 4]`):
Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
encoder blocks.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
drop_path_rate (`float`, *optional*, defaults to 0.0):
The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether or not a learnable bias should be added to the queries, keys and values.
num_labels ('int', *optional*, defaults to 1000):
The number of classes.
Example:
```python
>>> from transformers import PvtModel, PvtConfig
>>> # Initializing a PVT Xrenya/pvt-tiny-224 style configuration
>>> configuration = PvtConfig()
>>> # Initializing a model from the Xrenya/pvt-tiny-224 style configuration
>>> model = PvtModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "pvt"
def __init__(
self,
image_size: int = 224,
num_channels: int = 3,
num_encoder_blocks: int = 4,
depths: List[int] = [2, 2, 2, 2],
sequence_reduction_ratios: List[int] = [8, 4, 2, 1],
hidden_sizes: List[int] = [64, 128, 320, 512],
patch_sizes: List[int] = [4, 2, 2, 2],
strides: List[int] = [4, 2, 2, 2],
num_attention_heads: List[int] = [1, 2, 5, 8],
mlp_ratios: List[int] = [8, 8, 4, 4],
hidden_act: Mapping[str, Callable] = "gelu",
hidden_dropout_prob: float = 0.0,
attention_probs_dropout_prob: float = 0.0,
initializer_range: float = 0.02,
drop_path_rate: float = 0.0,
layer_norm_eps: float = 1e-6,
qkv_bias: bool = True,
num_labels: int = 1000,
**kwargs,
):
super().__init__(**kwargs)
self.image_size = image_size
self.num_channels = num_channels
self.num_encoder_blocks = num_encoder_blocks
self.depths = depths
self.sequence_reduction_ratios = sequence_reduction_ratios
self.hidden_sizes = hidden_sizes
self.patch_sizes = patch_sizes
self.strides = strides
self.mlp_ratios = mlp_ratios
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.drop_path_rate = drop_path_rate
self.layer_norm_eps = layer_norm_eps
self.num_labels = num_labels
self.qkv_bias = qkv_bias
class PvtOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
]
)
@property
def atol_for_validation(self) -> float:
return 1e-4
@property
def default_onnx_opset(self) -> int:
return 12
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/pvt/modeling_pvt.py | # coding=utf-8
# Copyright 2023 Authors: Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan,
# Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and The HuggingFace Inc. team.
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch PVT model."""
import collections
import math
from typing import Iterable, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
)
from .configuration_pvt import PvtConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "PvtConfig"
_CHECKPOINT_FOR_DOC = "Zetatech/pvt-tiny-224"
_EXPECTED_OUTPUT_SHAPE = [1, 50, 512]
_IMAGE_CLASS_CHECKPOINT = "Zetatech/pvt-tiny-224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
PVT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"Zetatech/pvt-tiny-224"
# See all PVT models at https://huggingface.co/models?filter=pvt
]
# Copied from transformers.models.beit.modeling_beit.drop_path
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
"""
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
argument.
"""
if drop_prob == 0.0 or not training:
return input
keep_prob = 1 - drop_prob
shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
random_tensor.floor_() # binarize
output = input.div(keep_prob) * random_tensor
return output
# Copied from transformers.models.convnext.modeling_convnext.ConvNextDropPath with ConvNext->Pvt
class PvtDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
return drop_path(hidden_states, self.drop_prob, self.training)
def extra_repr(self) -> str:
return "p={}".format(self.drop_prob)
class PvtPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(
self,
config: PvtConfig,
image_size: Union[int, Iterable[int]],
patch_size: Union[int, Iterable[int]],
stride: int,
num_channels: int,
hidden_size: int,
cls_token: bool = False,
):
super().__init__()
self.config = config
image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.num_patches = num_patches
self.position_embeddings = nn.Parameter(
torch.randn(1, num_patches + 1 if cls_token else num_patches, hidden_size)
)
self.cls_token = nn.Parameter(torch.zeros(1, 1, hidden_size)) if cls_token else None
self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=stride, stride=patch_size)
self.layer_norm = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(p=config.hidden_dropout_prob)
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
num_patches = height * width
if num_patches == self.config.image_size * self.config.image_size:
return self.position_embeddings
embeddings = embeddings.reshape(1, height, width, -1).permute(0, 3, 1, 2)
interpolated_embeddings = F.interpolate(embeddings, size=(height, width), mode="bilinear")
interpolated_embeddings = interpolated_embeddings.reshape(1, -1, height * width).permute(0, 2, 1)
return interpolated_embeddings
def forward(self, pixel_values: torch.Tensor) -> Tuple[torch.Tensor, int, int]:
batch_size, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
patch_embed = self.projection(pixel_values)
*_, height, width = patch_embed.shape
patch_embed = patch_embed.flatten(2).transpose(1, 2)
embeddings = self.layer_norm(patch_embed)
if self.cls_token is not None:
cls_token = self.cls_token.expand(batch_size, -1, -1)
embeddings = torch.cat((cls_token, embeddings), dim=1)
position_embeddings = self.interpolate_pos_encoding(self.position_embeddings[:, 1:], height, width)
position_embeddings = torch.cat((self.position_embeddings[:, :1], position_embeddings), dim=1)
else:
position_embeddings = self.interpolate_pos_encoding(self.position_embeddings, height, width)
embeddings = self.dropout(embeddings + position_embeddings)
return embeddings, height, width
class PvtSelfOutput(nn.Module):
def __init__(self, config: PvtConfig, hidden_size: int):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class PvtEfficientSelfAttention(nn.Module):
"""Efficient self-attention mechanism with reduction of the sequence [PvT paper](https://arxiv.org/abs/2102.12122)."""
def __init__(
self, config: PvtConfig, hidden_size: int, num_attention_heads: int, sequences_reduction_ratio: float
):
super().__init__()
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention "
f"heads ({self.num_attention_heads})"
)
self.attention_head_size = int(self.hidden_size / self.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(self.hidden_size, self.all_head_size, bias=config.qkv_bias)
self.key = nn.Linear(self.hidden_size, self.all_head_size, bias=config.qkv_bias)
self.value = nn.Linear(self.hidden_size, self.all_head_size, bias=config.qkv_bias)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.sequences_reduction_ratio = sequences_reduction_ratio
if sequences_reduction_ratio > 1:
self.sequence_reduction = nn.Conv2d(
hidden_size, hidden_size, kernel_size=sequences_reduction_ratio, stride=sequences_reduction_ratio
)
self.layer_norm = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
def transpose_for_scores(self, hidden_states: int) -> torch.Tensor:
new_shape = hidden_states.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
hidden_states = hidden_states.view(new_shape)
return hidden_states.permute(0, 2, 1, 3)
def forward(
self,
hidden_states: torch.Tensor,
height: int,
width: int,
output_attentions: bool = False,
) -> Tuple[torch.Tensor]:
query_layer = self.transpose_for_scores(self.query(hidden_states))
if self.sequences_reduction_ratio > 1:
batch_size, seq_len, num_channels = hidden_states.shape
# Reshape to (batch_size, num_channels, height, width)
hidden_states = hidden_states.permute(0, 2, 1).reshape(batch_size, num_channels, height, width)
# Apply sequence reduction
hidden_states = self.sequence_reduction(hidden_states)
# Reshape back to (batch_size, seq_len, num_channels)
hidden_states = hidden_states.reshape(batch_size, num_channels, -1).permute(0, 2, 1)
hidden_states = self.layer_norm(hidden_states)
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
class PvtAttention(nn.Module):
def __init__(
self, config: PvtConfig, hidden_size: int, num_attention_heads: int, sequences_reduction_ratio: float
):
super().__init__()
self.self = PvtEfficientSelfAttention(
config,
hidden_size=hidden_size,
num_attention_heads=num_attention_heads,
sequences_reduction_ratio=sequences_reduction_ratio,
)
self.output = PvtSelfOutput(config, hidden_size=hidden_size)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self, hidden_states: torch.Tensor, height: int, width: int, output_attentions: bool = False
) -> Tuple[torch.Tensor]:
self_outputs = self.self(hidden_states, height, width, output_attentions)
attention_output = self.output(self_outputs[0])
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
class PvtFFN(nn.Module):
def __init__(
self,
config: PvtConfig,
in_features: int,
hidden_features: Optional[int] = None,
out_features: Optional[int] = None,
):
super().__init__()
out_features = out_features if out_features is not None else in_features
self.dense1 = nn.Linear(in_features, hidden_features)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
self.dense2 = nn.Linear(hidden_features, out_features)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense1(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.dense2(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class PvtLayer(nn.Module):
def __init__(
self,
config: PvtConfig,
hidden_size: int,
num_attention_heads: int,
drop_path: float,
sequences_reduction_ratio: float,
mlp_ratio: float,
):
super().__init__()
self.layer_norm_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
self.attention = PvtAttention(
config=config,
hidden_size=hidden_size,
num_attention_heads=num_attention_heads,
sequences_reduction_ratio=sequences_reduction_ratio,
)
self.drop_path = PvtDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.layer_norm_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
mlp_hidden_size = int(hidden_size * mlp_ratio)
self.mlp = PvtFFN(config=config, in_features=hidden_size, hidden_features=mlp_hidden_size)
def forward(self, hidden_states: torch.Tensor, height: int, width: int, output_attentions: bool = False):
self_attention_outputs = self.attention(
hidden_states=self.layer_norm_1(hidden_states),
height=height,
width=width,
output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:]
attention_output = self.drop_path(attention_output)
hidden_states = attention_output + hidden_states
mlp_output = self.mlp(self.layer_norm_2(hidden_states))
mlp_output = self.drop_path(mlp_output)
layer_output = hidden_states + mlp_output
outputs = (layer_output,) + outputs
return outputs
class PvtEncoder(nn.Module):
def __init__(self, config: PvtConfig):
super().__init__()
self.config = config
# stochastic depth decay rule
drop_path_decays = torch.linspace(0, config.drop_path_rate, sum(config.depths)).tolist()
# patch embeddings
embeddings = []
for i in range(config.num_encoder_blocks):
embeddings.append(
PvtPatchEmbeddings(
config=config,
image_size=config.image_size if i == 0 else self.config.image_size // (2 ** (i + 1)),
patch_size=config.patch_sizes[i],
stride=config.strides[i],
num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1],
hidden_size=config.hidden_sizes[i],
cls_token=i == config.num_encoder_blocks - 1,
)
)
self.patch_embeddings = nn.ModuleList(embeddings)
# Transformer blocks
blocks = []
cur = 0
for i in range(config.num_encoder_blocks):
# each block consists of layers
layers = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i]):
layers.append(
PvtLayer(
config=config,
hidden_size=config.hidden_sizes[i],
num_attention_heads=config.num_attention_heads[i],
drop_path=drop_path_decays[cur + j],
sequences_reduction_ratio=config.sequence_reduction_ratios[i],
mlp_ratio=config.mlp_ratios[i],
)
)
blocks.append(nn.ModuleList(layers))
self.block = nn.ModuleList(blocks)
# Layer norms
self.layer_norm = nn.LayerNorm(config.hidden_sizes[-1], eps=config.layer_norm_eps)
def forward(
self,
pixel_values: torch.FloatTensor,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple, BaseModelOutput]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
batch_size = pixel_values.shape[0]
num_blocks = len(self.block)
hidden_states = pixel_values
for idx, (embedding_layer, block_layer) in enumerate(zip(self.patch_embeddings, self.block)):
# first, obtain patch embeddings
hidden_states, height, width = embedding_layer(hidden_states)
# second, send embeddings through blocks
for block in block_layer:
layer_outputs = block(hidden_states, height, width, output_attentions)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if idx != num_blocks - 1:
hidden_states = hidden_states.reshape(batch_size, height, width, -1).permute(0, 3, 1, 2).contiguous()
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
class PvtPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = PvtConfig
base_model_prefix = "pvt"
main_input_name = "pixel_values"
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
# `trunc_normal_cpu` not implemented in `half` issues
module.weight.data = nn.init.trunc_normal_(module.weight.data, mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, PvtPatchEmbeddings):
module.position_embeddings.data = nn.init.trunc_normal_(
module.position_embeddings.data,
mean=0.0,
std=self.config.initializer_range,
)
if module.cls_token is not None:
module.cls_token.data = nn.init.trunc_normal_(
module.cls_token.data,
mean=0.0,
std=self.config.initializer_range,
)
PVT_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`~PvtConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
PVT_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`PvtImageProcessor.__call__`]
for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare Pvt encoder outputting raw hidden-states without any specific head on top.",
PVT_START_DOCSTRING,
)
class PvtModel(PvtPreTrainedModel):
def __init__(self, config: PvtConfig):
super().__init__(config)
self.config = config
# hierarchical Transformer encoder
self.encoder = PvtEncoder(config)
# Initialize weights and apply final processing
self.post_init()
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(PVT_INPUTS_DOCSTRING.format("(batch_size, channels, height, width)"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
pixel_values: torch.FloatTensor,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_outputs = self.encoder(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
if not return_dict:
return (sequence_output,) + encoder_outputs[1:]
return BaseModelOutput(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"""
Pvt Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
the [CLS] token) e.g. for ImageNet.
""",
PVT_START_DOCSTRING,
)
class PvtForImageClassification(PvtPreTrainedModel):
def __init__(self, config: PvtConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.pvt = PvtModel(config)
# Classifier head
self.classifier = (
nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(PVT_INPUTS_DOCSTRING.format("(batch_size, channels, height, width)"))
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
pixel_values: Optional[torch.Tensor],
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, ImageClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.pvt(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output[:, 0, :])
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/pvt/image_processing_pvt.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Image processor class for Pvt."""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
infer_channel_dimension_format,
is_scaled_image,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
logger = logging.get_logger(__name__)
class PvtImageProcessor(BaseImageProcessor):
r"""
Constructs a PVT image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `(size["height"],
size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method.
size (`dict`, *optional*, defaults to `{"height": 224, "width": 224}`):
Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
method.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
`preprocess` method.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
parameter in the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
`preprocess` method.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
size = size if size is not None else {"height": 224, "width": 224}
size = get_size_dict(size)
self.do_resize = do_resize
self.do_rescale = do_rescale
self.do_normalize = do_normalize
self.size = size
self.resample = resample
self.rescale_factor = rescale_factor
self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
# Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BILINEAR,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Resize an image to `(size["height"], size["width"])`.
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
`PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
Returns:
`np.ndarray`: The resized image.
"""
size = get_size_dict(size)
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
output_size = (size["height"], size["width"])
return resize(
image,
size=output_size,
resample=resample,
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
def preprocess(
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
):
"""
Preprocess an image or batch of images.
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Dictionary in the format `{"height": h, "width": w}` specifying the size of the output image after
resizing.
resample (`PILImageResampling` filter, *optional*, defaults to `self.resample`):
`PILImageResampling` filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. Only has
an effect if `do_resize` is set to `True`.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image values between [0 - 1].
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image.
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
Image mean to use if `do_normalize` is set to `True`.
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
Image standard deviation to use if `do_normalize` is set to `True`.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
do_resize = do_resize if do_resize is not None else self.do_resize
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
resample = resample if resample is not None else self.resample
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.image_std
size = size if size is not None else self.size
size_dict = get_size_dict(size)
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True.")
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True.")
# All transformations expect numpy arrays.
images = [to_numpy_array(image) for image in images]
if is_scaled_image(images[0]) and do_rescale:
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
)
if input_data_format is None:
# We assume that all images have the same channel dimension format.
input_data_format = infer_channel_dimension_format(images[0])
if do_resize:
images = [
self.resize(image=image, size=size_dict, resample=resample, input_data_format=input_data_format)
for image in images
]
if do_rescale:
images = [
self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
for image in images
]
if do_normalize:
images = [
self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
for image in images
]
images = [
to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
]
data = {"pixel_values": images}
return BatchFeature(data=data, tensor_type=return_tensors)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/pvt/__init__.py | # coding=utf-8
# Copyright 2023 Authors: Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan,
# Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and The HuggingFace Inc. team.
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
is_vision_available,
)
_import_structure = {
"configuration_pvt": ["PVT_PRETRAINED_CONFIG_ARCHIVE_MAP", "PvtConfig", "PvtOnnxConfig"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["image_processing_pvt"] = ["PvtImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_pvt"] = [
"PVT_PRETRAINED_MODEL_ARCHIVE_LIST",
"PvtForImageClassification",
"PvtModel",
"PvtPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_pvt import PVT_PRETRAINED_CONFIG_ARCHIVE_MAP, PvtConfig, PvtOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pvt import PvtImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pvt import (
PVT_PRETRAINED_MODEL_ARCHIVE_LIST,
PvtForImageClassification,
PvtModel,
PvtPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/mobilenet_v2/convert_original_tf_checkpoint_to_pytorch.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert MobileNetV2 checkpoints from the tensorflow/models library."""
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetV2Config,
MobileNetV2ForImageClassification,
MobileNetV2ForSemanticSegmentation,
MobileNetV2ImageProcessor,
load_tf_weights_in_mobilenet_v2,
)
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def get_mobilenet_v2_config(model_name):
config = MobileNetV2Config(layer_norm_eps=0.001)
if "quant" in model_name:
raise ValueError("Quantized models are not supported.")
matches = re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$", model_name)
if matches:
config.depth_multiplier = float(matches[1])
config.image_size = int(matches[2])
if model_name.startswith("deeplabv3_"):
config.output_stride = 8
config.num_labels = 21
filename = "pascal-voc-id2label.json"
else:
# The TensorFlow version of MobileNetV2 predicts 1001 classes instead
# of the usual 1000. The first class (index 0) is "background".
config.num_labels = 1001
filename = "imagenet-1k-id2label.json"
repo_id = "huggingface/label-files"
id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
if config.num_labels == 1001:
id2label = {int(k) + 1: v for k, v in id2label.items()}
id2label[0] = "background"
else:
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
return config
# We will verify our results on an image of cute cats
def prepare_img():
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
im = Image.open(requests.get(url, stream=True).raw)
return im
@torch.no_grad()
def convert_movilevit_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub=False):
"""
Copy/paste/tweak model's weights to our MobileNetV2 structure.
"""
config = get_mobilenet_v2_config(model_name)
# Load 🤗 model
if model_name.startswith("deeplabv3_"):
model = MobileNetV2ForSemanticSegmentation(config).eval()
else:
model = MobileNetV2ForImageClassification(config).eval()
# Load weights from TensorFlow checkpoint
load_tf_weights_in_mobilenet_v2(model, config, checkpoint_path)
# Check outputs on an image, prepared by MobileNetV2ImageProcessor
image_processor = MobileNetV2ImageProcessor(
crop_size={"width": config.image_size, "height": config.image_size},
size={"shortest_edge": config.image_size + 32},
)
encoding = image_processor(images=prepare_img(), return_tensors="pt")
outputs = model(**encoding)
logits = outputs.logits
if model_name.startswith("deeplabv3_"):
assert logits.shape == (1, 21, 65, 65)
if model_name == "deeplabv3_mobilenet_v2_1.0_513":
expected_logits = torch.tensor(
[
[[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
[[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
[[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
]
)
else:
raise ValueError(f"Unknown model name: {model_name}")
assert torch.allclose(logits[0, :3, :3, :3], expected_logits, atol=1e-4)
else:
assert logits.shape == (1, 1001)
if model_name == "mobilenet_v2_1.4_224":
expected_logits = torch.tensor([0.0181, -1.0015, 0.4688])
elif model_name == "mobilenet_v2_1.0_224":
expected_logits = torch.tensor([0.2445, -1.1993, 0.1905])
elif model_name == "mobilenet_v2_0.75_160":
expected_logits = torch.tensor([0.2482, 0.4136, 0.6669])
elif model_name == "mobilenet_v2_0.35_96":
expected_logits = torch.tensor([0.1451, -0.4624, 0.7192])
else:
expected_logits = None
if expected_logits is not None:
assert torch.allclose(logits[0, :3], expected_logits, atol=1e-4)
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
print(f"Saving image processor to {pytorch_dump_folder_path}")
image_processor.save_pretrained(pytorch_dump_folder_path)
if push_to_hub:
print("Pushing to the hub...")
repo_id = "google/" + model_name
image_processor.push_to_hub(repo_id)
model.push_to_hub(repo_id)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="mobilenet_v2_1.0_224",
type=str,
help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<depth>_<size>'.",
)
parser.add_argument(
"--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
)
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
args = parser.parse_args()
convert_movilevit_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/mobilenet_v2/feature_extraction_mobilenet_v2.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Feature extractor class for MobileNetV2."""
import warnings
from ...utils import logging
from .image_processing_mobilenet_v2 import MobileNetV2ImageProcessor
logger = logging.get_logger(__name__)
class MobileNetV2FeatureExtractor(MobileNetV2ImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use MobileNetV2ImageProcessor instead.",
FutureWarning,
)
super().__init__(*args, **kwargs)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/mobilenet_v2/__init__.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileNetV2Config",
"MobileNetV2OnnxConfig",
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_mobilenet_v2"] = ["MobileNetV2FeatureExtractor"]
_import_structure["image_processing_mobilenet_v2"] = ["MobileNetV2ImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_mobilenet_v2"] = [
"MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
"MobileNetV2ForImageClassification",
"MobileNetV2ForSemanticSegmentation",
"MobileNetV2Model",
"MobileNetV2PreTrainedModel",
"load_tf_weights_in_mobilenet_v2",
]
if TYPE_CHECKING:
from .configuration_mobilenet_v2 import (
MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileNetV2Config,
MobileNetV2OnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilenet_v2 import MobileNetV2FeatureExtractor
from .image_processing_mobilenet_v2 import MobileNetV2ImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilenet_v2 import (
MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileNetV2ForImageClassification,
MobileNetV2ForSemanticSegmentation,
MobileNetV2Model,
MobileNetV2PreTrainedModel,
load_tf_weights_in_mobilenet_v2,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Image processor class for MobileNetV2."""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
get_resize_output_image_size,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
infer_channel_dimension_format,
is_scaled_image,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
class MobileNetV2ImageProcessor(BaseImageProcessor):
r"""
Constructs a MobileNetV2 image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 256}`):
Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with
the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess`
method.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
`preprocess` method.
do_center_crop (`bool`, *optional*, defaults to `True`):
Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, the image
is padded with 0's and then center cropped. Can be overridden by the `do_center_crop` parameter in the
`preprocess` method.
crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
Desired output size when applying center-cropping. Only has an effect if `do_center_crop` is set to `True`.
Can be overridden by the `crop_size` parameter in the `preprocess` method.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
parameter in the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
`preprocess` method.
do_normalize:
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
size = size if size is not None else {"shortest_edge": 256}
size = get_size_dict(size, default_to_square=False)
crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
crop_size = get_size_dict(crop_size, param_name="crop_size")
self.do_resize = do_resize
self.size = size
self.resample = resample
self.do_center_crop = do_center_crop
self.crop_size = crop_size
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
# Copied from transformers.models.mobilenet_v1.image_processing_mobilenet_v1.MobileNetV1ImageProcessor.resize
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BICUBIC,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge
resized to keep the input aspect ratio.
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Size of the output image.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
Resampling filter to use when resiizing the image.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
default_to_square = True
if "shortest_edge" in size:
size = size["shortest_edge"]
default_to_square = False
elif "height" in size and "width" in size:
size = (size["height"], size["width"])
else:
raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.")
output_size = get_resize_output_image_size(
image,
size=size,
default_to_square=default_to_square,
input_data_format=input_data_format,
)
return resize(
image,
size=output_size,
resample=resample,
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
def preprocess(
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_center_crop: bool = None,
crop_size: Dict[str, int] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
):
"""
Preprocess an image or batch of images.
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
the longest edge resized to keep the input aspect ratio.
resample (`PILImageResampling` filter, *optional*, defaults to `self.resample`):
`PILImageResampling` filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. Only has
an effect if `do_resize` is set to `True`.
do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
Whether to center crop the image.
crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image values between [0 - 1].
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image.
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
Image mean to use if `do_normalize` is set to `True`.
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
Image standard deviation to use if `do_normalize` is set to `True`.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
do_resize = do_resize if do_resize is not None else self.do_resize
size = size if size is not None else self.size
size = get_size_dict(size, default_to_square=False)
resample = resample if resample is not None else self.resample
do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
crop_size = crop_size if crop_size is not None else self.crop_size
crop_size = get_size_dict(crop_size, param_name="crop_size")
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.image_std
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True.")
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True.")
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True.")
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True.")
# All transformations expect numpy arrays.
images = [to_numpy_array(image) for image in images]
if is_scaled_image(images[0]) and do_rescale:
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
)
if input_data_format is None:
# We assume that all images have the same channel dimension format.
input_data_format = infer_channel_dimension_format(images[0])
if do_resize:
images = [
self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
for image in images
]
if do_center_crop:
images = [
self.center_crop(image=image, size=crop_size, input_data_format=input_data_format) for image in images
]
if do_rescale:
images = [
self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
for image in images
]
if do_normalize:
images = [
self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
for image in images
]
images = [
to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
]
data = {"pixel_values": images}
return BatchFeature(data=data, tensor_type=return_tensors)
# Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.post_process_semantic_segmentation with Beit->MobileNetV2
def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple] = None):
"""
Converts the output of [`MobileNetV2ForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.
Args:
outputs ([`MobileNetV2ForSemanticSegmentation`]):
Raw outputs of the model.
target_sizes (`List[Tuple]` of length `batch_size`, *optional*):
List of tuples corresponding to the requested final size (height, width) of each prediction. If unset,
predictions will not be resized.
Returns:
semantic_segmentation: `List[torch.Tensor]` of length `batch_size`, where each item is a semantic
segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is
specified). Each entry of each `torch.Tensor` correspond to a semantic class id.
"""
# TODO: add support for other frameworks
logits = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(logits) != len(target_sizes):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits"
)
if is_torch_tensor(target_sizes):
target_sizes = target_sizes.numpy()
semantic_segmentation = []
for idx in range(len(logits)):
resized_logits = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
)
semantic_map = resized_logits[0].argmax(dim=0)
semantic_segmentation.append(semantic_map)
else:
semantic_segmentation = logits.argmax(dim=1)
semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
return semantic_segmentation
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/mobilenet_v2/configuration_mobilenet_v2.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" MobileNetV2 model configuration"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"google/mobilenet_v2_1.4_224": "https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json",
"google/mobilenet_v2_1.0_224": "https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json",
"google/mobilenet_v2_0.75_160": "https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json",
"google/mobilenet_v2_0.35_96": "https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json",
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class MobileNetV2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MobileNetV2Model`]. It is used to instantiate a
MobileNetV2 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the MobileNetV2
[google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
depth_multiplier (`float`, *optional*, defaults to 1.0):
Shrinks or expands the number of channels in each layer. Default is 1.0, which starts the network with 32
channels. This is sometimes also called "alpha" or "width multiplier".
depth_divisible_by (`int`, *optional*, defaults to 8):
The number of channels in each layer will always be a multiple of this number.
min_depth (`int`, *optional*, defaults to 8):
All layers will have at least this many channels.
expand_ratio (`float`, *optional*, defaults to 6.0):
The number of output channels of the first layer in each block is input channels times expansion ratio.
output_stride (`int`, *optional*, defaults to 32):
The ratio between the spatial resolution of the input and output feature maps. By default the model reduces
the input dimensions by a factor of 32. If `output_stride` is 8 or 16, the model uses dilated convolutions
on the depthwise layers instead of regular convolutions, so that the feature maps never become more than 8x
or 16x smaller than the input image.
first_layer_is_expansion (`bool`, *optional*, defaults to `True`):
True if the very first convolution layer is also the expansion layer for the first expansion block.
finegrained_output (`bool`, *optional*, defaults to `True`):
If true, the number of output channels in the final convolution layer will stay large (1280) even if
`depth_multiplier` is less than 1.
hidden_act (`str` or `function`, *optional*, defaults to `"relu6"`):
The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
tf_padding (`bool`, *optional*, defaults to `True`):
Whether to use TensorFlow padding rules on the convolution layers.
classifier_dropout_prob (`float`, *optional*, defaults to 0.8):
The dropout ratio for attached classifiers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 0.001):
The epsilon used by the layer normalization layers.
semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
The index that is ignored by the loss function of the semantic segmentation model.
Example:
```python
>>> from transformers import MobileNetV2Config, MobileNetV2Model
>>> # Initializing a "mobilenet_v2_1.0_224" style configuration
>>> configuration = MobileNetV2Config()
>>> # Initializing a model from the "mobilenet_v2_1.0_224" style configuration
>>> model = MobileNetV2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "mobilenet_v2"
def __init__(
self,
num_channels=3,
image_size=224,
depth_multiplier=1.0,
depth_divisible_by=8,
min_depth=8,
expand_ratio=6.0,
output_stride=32,
first_layer_is_expansion=True,
finegrained_output=True,
hidden_act="relu6",
tf_padding=True,
classifier_dropout_prob=0.8,
initializer_range=0.02,
layer_norm_eps=0.001,
semantic_loss_ignore_index=255,
**kwargs,
):
super().__init__(**kwargs)
if depth_multiplier <= 0:
raise ValueError("depth_multiplier must be greater than zero.")
self.num_channels = num_channels
self.image_size = image_size
self.depth_multiplier = depth_multiplier
self.depth_divisible_by = depth_divisible_by
self.min_depth = min_depth
self.expand_ratio = expand_ratio
self.output_stride = output_stride
self.first_layer_is_expansion = first_layer_is_expansion
self.finegrained_output = finegrained_output
self.hidden_act = hidden_act
self.tf_padding = tf_padding
self.classifier_dropout_prob = classifier_dropout_prob
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.semantic_loss_ignore_index = semantic_loss_ignore_index
class MobileNetV2OnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict([("pixel_values", {0: "batch"})])
@property
def outputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "image-classification":
return OrderedDict([("logits", {0: "batch"})])
else:
return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})])
@property
def atol_for_validation(self) -> float:
return 1e-4
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py | # coding=utf-8
# Copyright 2022 Apple Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch MobileNetV2 model."""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
SemanticSegmenterOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_mobilenet_v2 import MobileNetV2Config
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "MobileNetV2Config"
# Base docstring
_CHECKPOINT_FOR_DOC = "google/mobilenet_v2_1.0_224"
_EXPECTED_OUTPUT_SHAPE = [1, 1280, 7, 7]
# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "google/mobilenet_v2_1.0_224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST = [
"google/mobilenet_v2_1.4_224",
"google/mobilenet_v2_1.0_224",
"google/mobilenet_v2_0.37_160",
"google/mobilenet_v2_0.35_96",
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
]
def _build_tf_to_pytorch_map(model, config, tf_weights=None):
"""
A map of modules from TF to PyTorch.
"""
tf_to_pt_map = {}
if isinstance(model, (MobileNetV2ForImageClassification, MobileNetV2ForSemanticSegmentation)):
backbone = model.mobilenet_v2
else:
backbone = model
# Use the EMA weights if available
def ema(x):
return x + "/ExponentialMovingAverage" if x + "/ExponentialMovingAverage" in tf_weights else x
prefix = "MobilenetV2/Conv/"
tf_to_pt_map[ema(prefix + "weights")] = backbone.conv_stem.first_conv.convolution.weight
tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = backbone.conv_stem.first_conv.normalization.bias
tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = backbone.conv_stem.first_conv.normalization.weight
tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_stem.first_conv.normalization.running_mean
tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_stem.first_conv.normalization.running_var
prefix = "MobilenetV2/expanded_conv/depthwise/"
tf_to_pt_map[ema(prefix + "depthwise_weights")] = backbone.conv_stem.conv_3x3.convolution.weight
tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = backbone.conv_stem.conv_3x3.normalization.bias
tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = backbone.conv_stem.conv_3x3.normalization.weight
tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_stem.conv_3x3.normalization.running_mean
tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_stem.conv_3x3.normalization.running_var
prefix = "MobilenetV2/expanded_conv/project/"
tf_to_pt_map[ema(prefix + "weights")] = backbone.conv_stem.reduce_1x1.convolution.weight
tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = backbone.conv_stem.reduce_1x1.normalization.bias
tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = backbone.conv_stem.reduce_1x1.normalization.weight
tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_stem.reduce_1x1.normalization.running_mean
tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_stem.reduce_1x1.normalization.running_var
for i in range(16):
tf_index = i + 1
pt_index = i
pointer = backbone.layer[pt_index]
prefix = f"MobilenetV2/expanded_conv_{tf_index}/expand/"
tf_to_pt_map[ema(prefix + "weights")] = pointer.expand_1x1.convolution.weight
tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = pointer.expand_1x1.normalization.bias
tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = pointer.expand_1x1.normalization.weight
tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.expand_1x1.normalization.running_mean
tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.expand_1x1.normalization.running_var
prefix = f"MobilenetV2/expanded_conv_{tf_index}/depthwise/"
tf_to_pt_map[ema(prefix + "depthwise_weights")] = pointer.conv_3x3.convolution.weight
tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = pointer.conv_3x3.normalization.bias
tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = pointer.conv_3x3.normalization.weight
tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.conv_3x3.normalization.running_mean
tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.conv_3x3.normalization.running_var
prefix = f"MobilenetV2/expanded_conv_{tf_index}/project/"
tf_to_pt_map[ema(prefix + "weights")] = pointer.reduce_1x1.convolution.weight
tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = pointer.reduce_1x1.normalization.bias
tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = pointer.reduce_1x1.normalization.weight
tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.reduce_1x1.normalization.running_mean
tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.reduce_1x1.normalization.running_var
prefix = "MobilenetV2/Conv_1/"
tf_to_pt_map[ema(prefix + "weights")] = backbone.conv_1x1.convolution.weight
tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = backbone.conv_1x1.normalization.bias
tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = backbone.conv_1x1.normalization.weight
tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_1x1.normalization.running_mean
tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_1x1.normalization.running_var
if isinstance(model, MobileNetV2ForImageClassification):
prefix = "MobilenetV2/Logits/Conv2d_1c_1x1/"
tf_to_pt_map[ema(prefix + "weights")] = model.classifier.weight
tf_to_pt_map[ema(prefix + "biases")] = model.classifier.bias
if isinstance(model, MobileNetV2ForSemanticSegmentation):
prefix = "image_pooling/"
tf_to_pt_map[prefix + "weights"] = model.segmentation_head.conv_pool.convolution.weight
tf_to_pt_map[prefix + "BatchNorm/beta"] = model.segmentation_head.conv_pool.normalization.bias
tf_to_pt_map[prefix + "BatchNorm/gamma"] = model.segmentation_head.conv_pool.normalization.weight
tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = model.segmentation_head.conv_pool.normalization.running_mean
tf_to_pt_map[
prefix + "BatchNorm/moving_variance"
] = model.segmentation_head.conv_pool.normalization.running_var
prefix = "aspp0/"
tf_to_pt_map[prefix + "weights"] = model.segmentation_head.conv_aspp.convolution.weight
tf_to_pt_map[prefix + "BatchNorm/beta"] = model.segmentation_head.conv_aspp.normalization.bias
tf_to_pt_map[prefix + "BatchNorm/gamma"] = model.segmentation_head.conv_aspp.normalization.weight
tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = model.segmentation_head.conv_aspp.normalization.running_mean
tf_to_pt_map[
prefix + "BatchNorm/moving_variance"
] = model.segmentation_head.conv_aspp.normalization.running_var
prefix = "concat_projection/"
tf_to_pt_map[prefix + "weights"] = model.segmentation_head.conv_projection.convolution.weight
tf_to_pt_map[prefix + "BatchNorm/beta"] = model.segmentation_head.conv_projection.normalization.bias
tf_to_pt_map[prefix + "BatchNorm/gamma"] = model.segmentation_head.conv_projection.normalization.weight
tf_to_pt_map[
prefix + "BatchNorm/moving_mean"
] = model.segmentation_head.conv_projection.normalization.running_mean
tf_to_pt_map[
prefix + "BatchNorm/moving_variance"
] = model.segmentation_head.conv_projection.normalization.running_var
prefix = "logits/semantic/"
tf_to_pt_map[ema(prefix + "weights")] = model.segmentation_head.classifier.convolution.weight
tf_to_pt_map[ema(prefix + "biases")] = model.segmentation_head.classifier.convolution.bias
return tf_to_pt_map
def load_tf_weights_in_mobilenet_v2(model, config, tf_checkpoint_path):
"""Load TensorFlow checkpoints in a PyTorch model."""
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
"Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions."
)
raise
# Load weights from TF model
init_vars = tf.train.list_variables(tf_checkpoint_path)
tf_weights = {}
for name, shape in init_vars:
logger.info(f"Loading TF weight {name} with shape {shape}")
array = tf.train.load_variable(tf_checkpoint_path, name)
tf_weights[name] = array
# Build TF to PyTorch weights loading map
tf_to_pt_map = _build_tf_to_pytorch_map(model, config, tf_weights)
for name, pointer in tf_to_pt_map.items():
logger.info(f"Importing {name}")
if name not in tf_weights:
logger.info(f"{name} not in tf pre-trained weights, skipping")
continue
array = tf_weights[name]
if "depthwise_weights" in name:
logger.info("Transposing depthwise")
array = np.transpose(array, (2, 3, 0, 1))
elif "weights" in name:
logger.info("Transposing")
if len(pointer.shape) == 2: # copying into linear layer
array = array.squeeze().transpose()
else:
array = np.transpose(array, (3, 2, 0, 1))
if pointer.shape != array.shape:
raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
logger.info(f"Initialize PyTorch weight {name} {array.shape}")
pointer.data = torch.from_numpy(array)
tf_weights.pop(name, None)
tf_weights.pop(name + "/RMSProp", None)
tf_weights.pop(name + "/RMSProp_1", None)
tf_weights.pop(name + "/ExponentialMovingAverage", None)
tf_weights.pop(name + "/Momentum", None)
logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}")
return model
def make_divisible(value: int, divisor: int = 8, min_value: Optional[int] = None) -> int:
"""
Ensure that all layers have a channel count that is divisible by `divisor`. This function is taken from the
original TensorFlow repo. It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
"""
if min_value is None:
min_value = divisor
new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_value < 0.9 * value:
new_value += divisor
return int(new_value)
def apply_depth_multiplier(config: MobileNetV2Config, channels: int) -> int:
return make_divisible(int(round(channels * config.depth_multiplier)), config.depth_divisible_by, config.min_depth)
def apply_tf_padding(features: torch.Tensor, conv_layer: nn.Conv2d) -> torch.Tensor:
"""
Apply TensorFlow-style "SAME" padding to a convolution layer. See the notes at:
https://www.tensorflow.org/api_docs/python/tf/nn#notes_on_padding_2
"""
in_height = int(features.shape[-2])
in_width = int(features.shape[-1])
stride_height, stride_width = conv_layer.stride
kernel_height, kernel_width = conv_layer.kernel_size
dilation_height, dilation_width = conv_layer.dilation
if in_height % stride_height == 0:
pad_along_height = max(kernel_height - stride_height, 0)
else:
pad_along_height = max(kernel_height - (in_height % stride_height), 0)
if in_width % stride_width == 0:
pad_along_width = max(kernel_width - stride_width, 0)
else:
pad_along_width = max(kernel_width - (in_width % stride_width), 0)
pad_left = pad_along_width // 2
pad_right = pad_along_width - pad_left
pad_top = pad_along_height // 2
pad_bottom = pad_along_height - pad_top
padding = (
pad_left * dilation_width,
pad_right * dilation_width,
pad_top * dilation_height,
pad_bottom * dilation_height,
)
return nn.functional.pad(features, padding, "constant", 0.0)
class MobileNetV2ConvLayer(nn.Module):
def __init__(
self,
config: MobileNetV2Config,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
groups: int = 1,
bias: bool = False,
dilation: int = 1,
use_normalization: bool = True,
use_activation: Union[bool, str] = True,
layer_norm_eps: Optional[float] = None,
) -> None:
super().__init__()
self.config = config
if in_channels % groups != 0:
raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups.")
if out_channels % groups != 0:
raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.")
padding = 0 if config.tf_padding else int((kernel_size - 1) / 2) * dilation
self.convolution = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias,
padding_mode="zeros",
)
if use_normalization:
self.normalization = nn.BatchNorm2d(
num_features=out_channels,
eps=config.layer_norm_eps if layer_norm_eps is None else layer_norm_eps,
momentum=0.997,
affine=True,
track_running_stats=True,
)
else:
self.normalization = None
if use_activation:
if isinstance(use_activation, str):
self.activation = ACT2FN[use_activation]
elif isinstance(config.hidden_act, str):
self.activation = ACT2FN[config.hidden_act]
else:
self.activation = config.hidden_act
else:
self.activation = None
def forward(self, features: torch.Tensor) -> torch.Tensor:
if self.config.tf_padding:
features = apply_tf_padding(features, self.convolution)
features = self.convolution(features)
if self.normalization is not None:
features = self.normalization(features)
if self.activation is not None:
features = self.activation(features)
return features
class MobileNetV2InvertedResidual(nn.Module):
def __init__(
self, config: MobileNetV2Config, in_channels: int, out_channels: int, stride: int, dilation: int = 1
) -> None:
super().__init__()
expanded_channels = make_divisible(
int(round(in_channels * config.expand_ratio)), config.depth_divisible_by, config.min_depth
)
if stride not in [1, 2]:
raise ValueError(f"Invalid stride {stride}.")
self.use_residual = (stride == 1) and (in_channels == out_channels)
self.expand_1x1 = MobileNetV2ConvLayer(
config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=1
)
self.conv_3x3 = MobileNetV2ConvLayer(
config,
in_channels=expanded_channels,
out_channels=expanded_channels,
kernel_size=3,
stride=stride,
groups=expanded_channels,
dilation=dilation,
)
self.reduce_1x1 = MobileNetV2ConvLayer(
config,
in_channels=expanded_channels,
out_channels=out_channels,
kernel_size=1,
use_activation=False,
)
def forward(self, features: torch.Tensor) -> torch.Tensor:
residual = features
features = self.expand_1x1(features)
features = self.conv_3x3(features)
features = self.reduce_1x1(features)
return residual + features if self.use_residual else features
class MobileNetV2Stem(nn.Module):
def __init__(self, config: MobileNetV2Config, in_channels: int, expanded_channels: int, out_channels: int) -> None:
super().__init__()
# The very first layer is a regular 3x3 convolution with stride 2 that expands to 32 channels.
# All other expansion layers use the expansion factor to compute the number of output channels.
self.first_conv = MobileNetV2ConvLayer(
config,
in_channels=in_channels,
out_channels=expanded_channels,
kernel_size=3,
stride=2,
)
if config.first_layer_is_expansion:
self.expand_1x1 = None
else:
self.expand_1x1 = MobileNetV2ConvLayer(
config, in_channels=expanded_channels, out_channels=expanded_channels, kernel_size=1
)
self.conv_3x3 = MobileNetV2ConvLayer(
config,
in_channels=expanded_channels,
out_channels=expanded_channels,
kernel_size=3,
stride=1,
groups=expanded_channels,
)
self.reduce_1x1 = MobileNetV2ConvLayer(
config,
in_channels=expanded_channels,
out_channels=out_channels,
kernel_size=1,
use_activation=False,
)
def forward(self, features: torch.Tensor) -> torch.Tensor:
features = self.first_conv(features)
if self.expand_1x1 is not None:
features = self.expand_1x1(features)
features = self.conv_3x3(features)
features = self.reduce_1x1(features)
return features
class MobileNetV2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MobileNetV2Config
load_tf_weights = load_tf_weights_in_mobilenet_v2
base_model_prefix = "mobilenet_v2"
main_input_name = "pixel_values"
supports_gradient_checkpointing = False
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d]) -> None:
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.BatchNorm2d):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
MOBILENET_V2_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MobileNetV2Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
MOBILENET_V2_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`MobileNetV2ImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare MobileNetV2 model outputting raw hidden-states without any specific head on top.",
MOBILENET_V2_START_DOCSTRING,
)
class MobileNetV2Model(MobileNetV2PreTrainedModel):
def __init__(self, config: MobileNetV2Config, add_pooling_layer: bool = True):
super().__init__(config)
self.config = config
# Output channels for the projection layers
channels = [16, 24, 24, 32, 32, 32, 64, 64, 64, 64, 96, 96, 96, 160, 160, 160, 320]
channels = [apply_depth_multiplier(config, x) for x in channels]
# Strides for the depthwise layers
strides = [2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1]
self.conv_stem = MobileNetV2Stem(
config,
in_channels=config.num_channels,
expanded_channels=apply_depth_multiplier(config, 32),
out_channels=channels[0],
)
current_stride = 2 # first conv layer has stride 2
dilation = 1
self.layer = nn.ModuleList()
for i in range(16):
# Keep making the feature maps smaller or use dilated convolution?
if current_stride == config.output_stride:
layer_stride = 1
layer_dilation = dilation
dilation *= strides[i] # larger dilation starts in next block
else:
layer_stride = strides[i]
layer_dilation = 1
current_stride *= layer_stride
self.layer.append(
MobileNetV2InvertedResidual(
config,
in_channels=channels[i],
out_channels=channels[i + 1],
stride=layer_stride,
dilation=layer_dilation,
)
)
if config.finegrained_output and config.depth_multiplier < 1.0:
output_channels = 1280
else:
output_channels = apply_depth_multiplier(config, 1280)
self.conv_1x1 = MobileNetV2ConvLayer(
config,
in_channels=channels[-1],
out_channels=output_channels,
kernel_size=1,
)
self.pooler = nn.AdaptiveAvgPool2d((1, 1)) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def _prune_heads(self, heads_to_prune):
raise NotImplementedError
@add_start_docstrings_to_model_forward(MOBILENET_V2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndNoAttention,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
hidden_states = self.conv_stem(pixel_values)
all_hidden_states = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer):
hidden_states = layer_module(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
last_hidden_state = self.conv_1x1(hidden_states)
if self.pooler is not None:
pooled_output = torch.flatten(self.pooler(last_hidden_state), start_dim=1)
else:
pooled_output = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None)
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=all_hidden_states,
)
@add_start_docstrings(
"""
MobileNetV2 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""",
MOBILENET_V2_START_DOCSTRING,
)
class MobileNetV2ForImageClassification(MobileNetV2PreTrainedModel):
def __init__(self, config: MobileNetV2Config) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.mobilenet_v2 = MobileNetV2Model(config)
last_hidden_size = self.mobilenet_v2.conv_1x1.convolution.out_channels
# Classifier head
self.dropout = nn.Dropout(config.classifier_dropout_prob, inplace=True)
self.classifier = nn.Linear(last_hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MOBILENET_V2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=ImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss). If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mobilenet_v2(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.classifier(self.dropout(pooled_output))
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
)
class MobileNetV2DeepLabV3Plus(nn.Module):
"""
The neural network from the paper "Encoder-Decoder with Atrous Separable Convolution for Semantic Image
Segmentation" https://arxiv.org/abs/1802.02611
"""
def __init__(self, config: MobileNetV2Config) -> None:
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(output_size=1)
self.conv_pool = MobileNetV2ConvLayer(
config,
in_channels=apply_depth_multiplier(config, 320),
out_channels=256,
kernel_size=1,
stride=1,
use_normalization=True,
use_activation="relu",
layer_norm_eps=1e-5,
)
self.conv_aspp = MobileNetV2ConvLayer(
config,
in_channels=apply_depth_multiplier(config, 320),
out_channels=256,
kernel_size=1,
stride=1,
use_normalization=True,
use_activation="relu",
layer_norm_eps=1e-5,
)
self.conv_projection = MobileNetV2ConvLayer(
config,
in_channels=512,
out_channels=256,
kernel_size=1,
stride=1,
use_normalization=True,
use_activation="relu",
layer_norm_eps=1e-5,
)
self.dropout = nn.Dropout2d(config.classifier_dropout_prob)
self.classifier = MobileNetV2ConvLayer(
config,
in_channels=256,
out_channels=config.num_labels,
kernel_size=1,
use_normalization=False,
use_activation=False,
bias=True,
)
def forward(self, features: torch.Tensor) -> torch.Tensor:
spatial_size = features.shape[-2:]
features_pool = self.avg_pool(features)
features_pool = self.conv_pool(features_pool)
features_pool = nn.functional.interpolate(
features_pool, size=spatial_size, mode="bilinear", align_corners=True
)
features_aspp = self.conv_aspp(features)
features = torch.cat([features_pool, features_aspp], dim=1)
features = self.conv_projection(features)
features = self.dropout(features)
features = self.classifier(features)
return features
@add_start_docstrings(
"""
MobileNetV2 model with a semantic segmentation head on top, e.g. for Pascal VOC.
""",
MOBILENET_V2_START_DOCSTRING,
)
class MobileNetV2ForSemanticSegmentation(MobileNetV2PreTrainedModel):
def __init__(self, config: MobileNetV2Config) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.mobilenet_v2 = MobileNetV2Model(config, add_pooling_layer=False)
self.segmentation_head = MobileNetV2DeepLabV3Plus(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MOBILENET_V2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=SemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, SemanticSegmenterOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, MobileNetV2ForSemanticSegmentation
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")
>>> model = MobileNetV2ForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> # logits are of shape (batch_size, num_labels, height, width)
>>> logits = outputs.logits
```"""
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mobilenet_v2(
pixel_values,
output_hidden_states=True, # we need the intermediate hidden states
return_dict=return_dict,
)
encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1]
logits = self.segmentation_head(encoder_hidden_states[-1])
loss = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError("The number of labels should be greater than one")
else:
# upsample logits to the images' original size
upsampled_logits = nn.functional.interpolate(
logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
)
loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
loss = loss_fct(upsampled_logits, labels)
if not return_dict:
if output_hidden_states:
output = (logits,) + outputs[1:]
else:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states if output_hidden_states else None,
attentions=None,
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/ernie/modeling_ernie.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch ERNIE model."""
import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
MaskedLMOutput,
MultipleChoiceModelOutput,
NextSentencePredictorOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_ernie import ErnieConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "nghuyong/ernie-1.0-base-zh"
_CONFIG_FOR_DOC = "ErnieConfig"
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST = [
"nghuyong/ernie-1.0-base-zh",
"nghuyong/ernie-2.0-base-en",
"nghuyong/ernie-2.0-large-en",
"nghuyong/ernie-3.0-base-zh",
"nghuyong/ernie-3.0-medium-zh",
"nghuyong/ernie-3.0-mini-zh",
"nghuyong/ernie-3.0-micro-zh",
"nghuyong/ernie-3.0-nano-zh",
"nghuyong/ernie-gram-zh",
"nghuyong/ernie-health-zh",
# See all ERNIE models at https://huggingface.co/models?filter=ernie
]
class ErnieEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
self.use_task_id = config.use_task_id
if config.use_task_id:
self.task_type_embeddings = nn.Embedding(config.task_type_vocab_size, config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.register_buffer(
"token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
task_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
past_key_values_length: int = 0,
) -> torch.Tensor:
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
# add `task_type_id` for ERNIE model
if self.use_task_id:
if task_type_ids is None:
task_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
task_type_embeddings = self.task_type_embeddings(task_type_ids)
embeddings += task_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Ernie
class ErnieSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(
config, "position_embedding_type", "absolute"
)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
use_cache = past_key_value is not None
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
query_length, key_length = query_layer.shape[2], key_layer.shape[2]
if use_cache:
position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
-1, 1
)
else:
position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in ErnieModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->Ernie
class ErnieSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Ernie
class ErnieAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = ErnieSelfAttention(config, position_embedding_type=position_embedding_type)
self.output = ErnieSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Ernie
class ErnieIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->Ernie
class ErnieOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Ernie
class ErnieLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = ErnieAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
self.crossattention = ErnieAttention(config, position_embedding_type="absolute")
self.intermediate = ErnieIntermediate(config)
self.output = ErnieOutput(config)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and encoder_hidden_states is not None:
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
" by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
cross_attn_past_key_value,
output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Ernie
class ErnieEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([ErnieLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_values[i] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->Ernie
class ErniePooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->Ernie
class ErniePredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->Ernie
class ErnieLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = ErniePredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->Ernie
class ErnieOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = ErnieLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores
# Copied from transformers.models.bert.modeling_bert.BertOnlyNSPHead with Bert->Ernie
class ErnieOnlyNSPHead(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score
# Copied from transformers.models.bert.modeling_bert.BertPreTrainingHeads with Bert->Ernie
class ErniePreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = ErnieLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predictions(sequence_output)
seq_relationship_score = self.seq_relationship(pooled_output)
return prediction_scores, seq_relationship_score
class ErniePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ErnieConfig
base_model_prefix = "ernie"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
@dataclass
# Copied from transformers.models.bert.modeling_bert.BertForPreTrainingOutput with Bert->Ernie
class ErnieForPreTrainingOutput(ModelOutput):
"""
Output type of [`ErnieForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.
prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
prediction_logits: torch.FloatTensor = None
seq_relationship_logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
ERNIE_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`ErnieConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
ERNIE_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
task_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Task type embedding is a special embedding to represent the characteristic of different tasks, such as
word-aware pre-training task, structure-aware pre-training task and semantic-aware pre-training task. We
assign a `task_type_id` to each task and the `task_type_id` is in the range `[0,
config.task_type_vocab_size-1]
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare Ernie Model transformer outputting raw hidden-states without any specific head on top.",
ERNIE_START_DOCSTRING,
)
class ErnieModel(ErniePreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
"""
# Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Ernie
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = ErnieEmbeddings(config)
self.encoder = ErnieEncoder(config)
self.pooler = ErniePooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
# Copied from transformers.models.bert.modeling_bert.BertModel.get_input_embeddings
def get_input_embeddings(self):
return self.embeddings.word_embeddings
# Copied from transformers.models.bert.modeling_bert.BertModel.set_input_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
# Copied from transformers.models.bert.modeling_bert.BertModel._prune_heads
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
task_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
task_type_ids=task_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
@add_start_docstrings(
"""
Ernie Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next
sentence prediction (classification)` head.
""",
ERNIE_START_DOCSTRING,
)
class ErnieForPreTraining(ErniePreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
# Copied from transformers.models.bert.modeling_bert.BertForPreTraining.__init__ with Bert->Ernie,bert->ernie
def __init__(self, config):
super().__init__(config)
self.ernie = ErnieModel(config)
self.cls = ErniePreTrainingHeads(config)
# Initialize weights and apply final processing
self.post_init()
# Copied from transformers.models.bert.modeling_bert.BertForPreTraining.get_output_embeddings
def get_output_embeddings(self):
return self.cls.predictions.decoder
# Copied from transformers.models.bert.modeling_bert.BertForPreTraining.set_output_embeddings
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=ErnieForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
task_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
next_sentence_label: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], ErnieForPreTrainingOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked),
the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence
pair (see `input_ids` docstring) Indices should be in `[0, 1]`:
- 0 indicates sequence B is a continuation of sequence A,
- 1 indicates sequence B is a random sequence.
kwargs (`Dict[str, any]`, optional, defaults to *{}*):
Used to hide legacy arguments that have been deprecated.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, ErnieForPreTraining
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-1.0-base-zh")
>>> model = ErnieForPreTraining.from_pretrained("nghuyong/ernie-1.0-base-zh")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.prediction_logits
>>> seq_relationship_logits = outputs.seq_relationship_logits
```
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.ernie(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
task_type_ids=task_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output, pooled_output = outputs[:2]
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
total_loss = None
if labels is not None and next_sentence_label is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
total_loss = masked_lm_loss + next_sentence_loss
if not return_dict:
output = (prediction_scores, seq_relationship_score) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return ErnieForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
seq_relationship_logits=seq_relationship_score,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""Ernie Model with a `language modeling` head on top for CLM fine-tuning.""", ERNIE_START_DOCSTRING
)
class ErnieForCausalLM(ErniePreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
# Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.__init__ with BertLMHeadModel->ErnieForCausalLM,Bert->Ernie,bert->ernie
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `ErnieForCausalLM` as a standalone, add `is_decoder=True.`")
self.ernie = ErnieModel(config, add_pooling_layer=False)
self.cls = ErnieOnlyMLMHead(config)
# Initialize weights and apply final processing
self.post_init()
# Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.get_output_embeddings
def get_output_embeddings(self):
return self.cls.predictions.decoder
# Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.set_output_embeddings
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
task_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.Tensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False
outputs = self.ernie(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
task_type_ids=task_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
lm_loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((lm_loss,) + output) if lm_loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=lm_loss,
logits=prediction_scores,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.prepare_inputs_for_generation
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, use_cache=True, **model_kwargs
):
input_shape = input_ids.shape
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_shape)
# cut decoder_input_ids if past_key_values is used
if past_key_values is not None:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_length:]
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"past_key_values": past_key_values,
"use_cache": use_cache,
}
# Copied from transformers.models.bert.modeling_bert.BertLMHeadModel._reorder_cache
def _reorder_cache(self, past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
@add_start_docstrings("""Ernie Model with a `language modeling` head on top.""", ERNIE_START_DOCSTRING)
class ErnieForMaskedLM(ErniePreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
# Copied from transformers.models.bert.modeling_bert.BertForMaskedLM.__init__ with Bert->Ernie,bert->ernie
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `ErnieForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
self.ernie = ErnieModel(config, add_pooling_layer=False)
self.cls = ErnieOnlyMLMHead(config)
# Initialize weights and apply final processing
self.post_init()
# Copied from transformers.models.bert.modeling_bert.BertForMaskedLM.get_output_embeddings
def get_output_embeddings(self):
return self.cls.predictions.decoder
# Copied from transformers.models.bert.modeling_bert.BertForMaskedLM.set_output_embeddings
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="'paris'",
expected_loss=0.88,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
task_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.ernie(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
task_type_ids=task_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss() # -100 index = padding token
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertForMaskedLM.prepare_inputs_for_generation
def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
input_shape = input_ids.shape
effective_batch_size = input_shape[0]
# add a dummy token
if self.config.pad_token_id is None:
raise ValueError("The PAD token should be defined for generation")
attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
dummy_token = torch.full(
(effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
)
input_ids = torch.cat([input_ids, dummy_token], dim=1)
return {"input_ids": input_ids, "attention_mask": attention_mask}
@add_start_docstrings(
"""Ernie Model with a `next sentence prediction (classification)` head on top.""",
ERNIE_START_DOCSTRING,
)
class ErnieForNextSentencePrediction(ErniePreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForNextSentencePrediction.__init__ with Bert->Ernie,bert->ernie
def __init__(self, config):
super().__init__(config)
self.ernie = ErnieModel(config)
self.cls = ErnieOnlyNSPHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
task_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
(see `input_ids` docstring). Indices should be in `[0, 1]`:
- 0 indicates sequence B is a continuation of sequence A,
- 1 indicates sequence B is a random sequence.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, ErnieForNextSentencePrediction
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-1.0-base-zh")
>>> model = ErnieForNextSentencePrediction.from_pretrained("nghuyong/ernie-1.0-base-zh")
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
>>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
>>> outputs = model(**encoding, labels=torch.LongTensor([1]))
>>> logits = outputs.logits
>>> assert logits[0, 0] < logits[0, 1] # next sentence was random
```
"""
if "next_sentence_label" in kwargs:
warnings.warn(
"The `next_sentence_label` argument is deprecated and will be removed in a future version, use"
" `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("next_sentence_label")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.ernie(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
task_type_ids=task_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
seq_relationship_scores = self.cls(pooled_output)
next_sentence_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1))
if not return_dict:
output = (seq_relationship_scores,) + outputs[2:]
return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
return NextSentencePredictorOutput(
loss=next_sentence_loss,
logits=seq_relationship_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
Ernie Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
""",
ERNIE_START_DOCSTRING,
)
class ErnieForSequenceClassification(ErniePreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification.__init__ with Bert->Ernie,bert->ernie
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.ernie = ErnieModel(config)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
task_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.ernie(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
task_type_ids=task_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
Ernie Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
ERNIE_START_DOCSTRING,
)
class ErnieForMultipleChoice(ErniePreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForMultipleChoice.__init__ with Bert->Ernie,bert->ernie
def __init__(self, config):
super().__init__(config)
self.ernie = ErnieModel(config)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
task_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.ernie(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
task_type_ids=task_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
Ernie Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
ERNIE_START_DOCSTRING,
)
class ErnieForTokenClassification(ErniePreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForTokenClassification.__init__ with Bert->Ernie,bert->ernie
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ernie = ErnieModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
task_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.ernie(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
task_type_ids=task_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
Ernie Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
ERNIE_START_DOCSTRING,
)
class ErnieForQuestionAnswering(ErniePreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering.__init__ with Bert->Ernie,bert->ernie
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ernie = ErnieModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(ERNIE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
task_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
start_positions: Optional[torch.Tensor] = None,
end_positions: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.ernie(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
task_type_ids=task_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/ernie/__init__.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
_import_structure = {
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_ernie"] = [
"ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
"ErnieForCausalLM",
"ErnieForMaskedLM",
"ErnieForMultipleChoice",
"ErnieForNextSentencePrediction",
"ErnieForPreTraining",
"ErnieForQuestionAnswering",
"ErnieForSequenceClassification",
"ErnieForTokenClassification",
"ErnieModel",
"ErniePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/ernie/configuration_ernie.py | # coding=utf-8
# Copyright 2022 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" ERNIE model configuration"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"nghuyong/ernie-1.0-base-zh": "https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json",
"nghuyong/ernie-2.0-base-en": "https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json",
"nghuyong/ernie-2.0-large-en": "https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json",
"nghuyong/ernie-3.0-base-zh": "https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json",
"nghuyong/ernie-3.0-medium-zh": "https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json",
"nghuyong/ernie-3.0-mini-zh": "https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json",
"nghuyong/ernie-3.0-micro-zh": "https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json",
"nghuyong/ernie-3.0-nano-zh": "https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json",
"nghuyong/ernie-gram-zh": "https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json",
"nghuyong/ernie-health-zh": "https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json",
}
class ErnieConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ErnieModel`] or a [`TFErnieModel`]. It is used to
instantiate a ERNIE model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the ERNIE
[nghuyong/ernie-3.0-base-zh](https://huggingface.co/nghuyong/ernie-3.0-base-zh) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the ERNIE model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`ErnieModel`] or [`TFErnieModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`ErnieModel`] or [`TFErnieModel`].
task_type_vocab_size (`int`, *optional*, defaults to 3):
The vocabulary size of the `task_type_ids` for ERNIE2.0/ERNIE3.0 model
use_task_id (`bool`, *optional*, defaults to `False`):
Whether or not the model support `task_type_ids`
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 0):
Padding token id.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
classifier_dropout (`float`, *optional*):
The dropout ratio for the classification head.
Examples:
```python
>>> from transformers import ErnieConfig, ErnieModel
>>> # Initializing a ERNIE nghuyong/ernie-3.0-base-zh style configuration
>>> configuration = ErnieConfig()
>>> # Initializing a model (with random weights) from the nghuyong/ernie-3.0-base-zh style configuration
>>> model = ErnieModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "ernie"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
task_type_vocab_size=3,
use_task_id=False,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
position_embedding_type="absolute",
use_cache=True,
classifier_dropout=None,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.task_type_vocab_size = task_type_vocab_size
self.use_task_id = use_task_id
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.use_cache = use_cache
self.classifier_dropout = classifier_dropout
class ErnieOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
("task_type_ids", dynamic_axis),
]
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/vit_msn/__init__.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_vit_msn"] = [
"VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST",
"ViTMSNModel",
"ViTMSNForImageClassification",
"ViTMSNPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/vit_msn/convert_msn_to_pytorch.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert ViT MSN checkpoints from the original repository: https://github.com/facebookresearch/msn"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
# here we list all keys to be renamed (original name on the left, our name on the right)
def create_rename_keys(config, base_model=False):
rename_keys = []
for i in range(config.num_hidden_layers):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"module.blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight"))
rename_keys.append((f"module.blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias"))
rename_keys.append(
(f"module.blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")
)
rename_keys.append((f"module.blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias"))
rename_keys.append((f"module.blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight"))
rename_keys.append((f"module.blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias"))
rename_keys.append((f"module.blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight"))
rename_keys.append((f"module.blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias"))
rename_keys.append((f"module.blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight"))
rename_keys.append((f"module.blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias"))
# projection layer + position embeddings
rename_keys.extend(
[
("module.cls_token", "vit.embeddings.cls_token"),
("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
("module.pos_embed", "vit.embeddings.position_embeddings"),
]
)
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("module.norm.weight", "layernorm.weight"),
("module.norm.bias", "layernorm.bias"),
]
)
# if just the base model, we should remove "vit" from all keys that start with "vit"
rename_keys = [(pair[0], pair[1][4:]) if pair[1].startswith("vit") else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
]
)
return rename_keys
# we split up the matrix of each encoder layer into queries, keys and values
def read_in_q_k_v(state_dict, config, base_model=False):
for i in range(config.num_hidden_layers):
if base_model:
prefix = ""
else:
prefix = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
in_proj_weight = state_dict.pop(f"module.blocks.{i}.attn.qkv.weight")
in_proj_bias = state_dict.pop(f"module.blocks.{i}.attn.qkv.bias")
# next, add query, keys and values (in that order) to the state dict
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[
: config.hidden_size, :
]
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size]
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[
-config.hidden_size :, :
]
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :]
def remove_classification_head_(state_dict):
ignore_keys = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(k, None)
def remove_projection_head(state_dict):
# projection head is used in the self-supervised pre-training in MSN,
# for downstream task it's not needed.
ignore_keys = [
"module.fc.fc1.weight",
"module.fc.fc1.bias",
"module.fc.bn1.weight",
"module.fc.bn1.bias",
"module.fc.bn1.running_mean",
"module.fc.bn1.running_var",
"module.fc.bn1.num_batches_tracked",
"module.fc.fc2.weight",
"module.fc.fc2.bias",
"module.fc.bn2.weight",
"module.fc.bn2.bias",
"module.fc.bn2.running_mean",
"module.fc.bn2.running_var",
"module.fc.bn2.num_batches_tracked",
"module.fc.fc3.weight",
"module.fc.fc3.bias",
]
for k in ignore_keys:
state_dict.pop(k, None)
def rename_key(dct, old, new):
val = dct.pop(old)
dct[new] = val
def convert_vit_msn_checkpoint(checkpoint_url, pytorch_dump_folder_path):
config = ViTMSNConfig()
config.num_labels = 1000
repo_id = "datasets/huggingface/label-files"
filename = "imagenet-1k-id2label.json"
id2label = json.load(open(hf_hub_download(repo_id, filename), "r"))
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
if "s16" in checkpoint_url:
config.hidden_size = 384
config.intermediate_size = 1536
config.num_attention_heads = 6
elif "l16" in checkpoint_url:
config.hidden_size = 1024
config.intermediate_size = 4096
config.num_hidden_layers = 24
config.num_attention_heads = 16
config.hidden_dropout_prob = 0.1
elif "b4" in checkpoint_url:
config.patch_size = 4
elif "l7" in checkpoint_url:
config.patch_size = 7
config.hidden_size = 1024
config.intermediate_size = 4096
config.num_hidden_layers = 24
config.num_attention_heads = 16
config.hidden_dropout_prob = 0.1
model = ViTMSNModel(config)
state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["target_encoder"]
image_processor = ViTImageProcessor(size=config.image_size)
remove_projection_head(state_dict)
rename_keys = create_rename_keys(config, base_model=True)
for src, dest in rename_keys:
rename_key(state_dict, src, dest)
read_in_q_k_v(state_dict, config, base_model=True)
model.load_state_dict(state_dict)
model.eval()
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
image_processor = ViTImageProcessor(
size=config.image_size, image_mean=IMAGENET_DEFAULT_MEAN, image_std=IMAGENET_DEFAULT_STD
)
inputs = image_processor(images=image, return_tensors="pt")
# forward pass
torch.manual_seed(2)
outputs = model(**inputs)
last_hidden_state = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
expected_slice = torch.tensor([[-1.0915, -1.4876, -1.1809]])
elif "b16" in checkpoint_url:
expected_slice = torch.tensor([[14.2889, -18.9045, 11.7281]])
elif "l16" in checkpoint_url:
expected_slice = torch.tensor([[41.5028, -22.8681, 45.6475]])
elif "b4" in checkpoint_url:
expected_slice = torch.tensor([[-4.3868, 5.2932, -0.4137]])
else:
expected_slice = torch.tensor([[-0.1792, -0.6465, 2.4263]])
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3], expected_slice, atol=1e-4)
print(f"Saving model to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
print(f"Saving image processor to {pytorch_dump_folder_path}")
image_processor.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
args = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/vit_msn/modeling_vit_msn.py | # coding=utf-8
# Copyright 2022 Facebook AI and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch ViT MSN (masked siamese network) model."""
import collections.abc
import math
from typing import Dict, List, Optional, Set, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_vit_msn import ViTMSNConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "ViTMSNConfig"
_CHECKPOINT_FOR_DOC = "facebook/vit-msn-small"
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/vit-msn-small",
# See all ViTMSN models at https://huggingface.co/models?filter=vit_msn
]
class ViTMSNEmbeddings(nn.Module):
"""
Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
"""
def __init__(self, config: ViTMSNConfig, use_mask_token: bool = False) -> None:
super().__init__()
self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None
self.patch_embeddings = ViTMSNPatchEmbeddings(config)
num_patches = self.patch_embeddings.num_patches
self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size))
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.config = config
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
resolution images.
Source:
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
"""
num_patches = embeddings.shape[1] - 1
num_positions = self.position_embeddings.shape[1] - 1
if num_patches == num_positions and height == width:
return self.position_embeddings
class_pos_embed = self.position_embeddings[:, 0]
patch_pos_embed = self.position_embeddings[:, 1:]
dim = embeddings.shape[-1]
patch_window_height = height // self.config.patch_size
patch_window_width = width // self.config.patch_size
# we add a small number to avoid floating point error in the interpolation
# see discussion at https://github.com/facebookresearch/dino/issues/8
patch_window_height, patch_window_width = patch_window_height + 0.1, patch_window_width + 0.1
patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
scale_factor=(
patch_window_height / math.sqrt(num_positions),
patch_window_width / math.sqrt(num_positions),
),
mode="bicubic",
align_corners=False,
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
def forward(
self,
pixel_values: torch.Tensor,
bool_masked_pos: Optional[torch.BoolTensor] = None,
interpolate_pos_encoding: bool = False,
) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
if bool_masked_pos is not None:
seq_length = embeddings.shape[1]
mask_tokens = self.mask_token.expand(batch_size, seq_length, -1)
# replace the masked visual tokens by mask_tokens
mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
# add the [CLS] token to the embedded patch tokens
cls_tokens = self.cls_token.expand(batch_size, -1, -1)
embeddings = torch.cat((cls_tokens, embeddings), dim=1)
# add positional encoding to each token
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
return embeddings
# Copied from transformers.models.vit.modeling_vit.ViTPatchEmbeddings with ViT->ViTMSN
class ViTMSNPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config):
super().__init__()
image_size, patch_size = config.image_size, config.patch_size
num_channels, hidden_size = config.num_channels, config.hidden_size
image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.num_patches = num_patches
self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
f" Expected {self.num_channels} but got {num_channels}."
)
if not interpolate_pos_encoding:
if height != self.image_size[0] or width != self.image_size[1]:
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model"
f" ({self.image_size[0]}*{self.image_size[1]})."
)
embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
return embeddings
# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->ViTMSN
class ViTMSNSelfAttention(nn.Module):
def __init__(self, config: ViTMSNConfig) -> None:
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
f"heads {config.num_attention_heads}."
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
mixed_query_layer = self.query(hidden_states)
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->ViTMSN
class ViTMSNSelfOutput(nn.Module):
"""
The residual connection is defined in ViTMSNLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: ViTMSNConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->ViTMSN
class ViTMSNAttention(nn.Module):
def __init__(self, config: ViTMSNConfig) -> None:
super().__init__()
self.attention = ViTMSNSelfAttention(config)
self.output = ViTMSNSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads: Set[int]) -> None:
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.attention.query = prune_linear_layer(self.attention.query, index)
self.attention.key = prune_linear_layer(self.attention.key, index)
self.attention.value = prune_linear_layer(self.attention.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
self_outputs = self.attention(hidden_states, head_mask, output_attentions)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->ViTMSN
class ViTMSNIntermediate(nn.Module):
def __init__(self, config: ViTMSNConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->ViTMSN
class ViTMSNOutput(nn.Module):
def __init__(self, config: ViTMSNConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states + input_tensor
return hidden_states
# Copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->ViTMSN
class ViTMSNLayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: ViTMSNConfig) -> None:
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = ViTMSNAttention(config)
self.intermediate = ViTMSNIntermediate(config)
self.output = ViTMSNOutput(config)
self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
self_attention_outputs = self.attention(
self.layernorm_before(hidden_states), # in ViTMSN, layernorm is applied before self-attention
head_mask,
output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
# first residual connection
hidden_states = attention_output + hidden_states
# in ViTMSN, layernorm is also applied after self-attention
layer_output = self.layernorm_after(hidden_states)
layer_output = self.intermediate(layer_output)
# second residual connection is done here
layer_output = self.output(layer_output, hidden_states)
outputs = (layer_output,) + outputs
return outputs
# Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->ViTMSN
class ViTMSNEncoder(nn.Module):
def __init__(self, config: ViTMSNConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([ViTMSNLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
) -> Union[tuple, BaseModelOutput]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
layer_head_mask,
output_attentions,
)
else:
layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
class ViTMSNPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ViTMSNConfig
base_model_prefix = "vit"
main_input_name = "pixel_values"
supports_gradient_checkpointing = True
# todo: Resort to https://github.com/facebookresearch/msn/blob/main/src/deit.py#L200-#L211
# when creating pre-training scripts.
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
VIT_MSN_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`ViTMSNConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
VIT_MSN_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`]
for details.
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
interpolate_pos_encoding (`bool`, *optional*):
Whether to interpolate the pre-trained position encodings.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare ViTMSN Model outputting raw hidden-states without any specific head on top.",
VIT_MSN_START_DOCSTRING,
)
class ViTMSNModel(ViTMSNPreTrainedModel):
def __init__(self, config: ViTMSNConfig, use_mask_token: bool = False):
super().__init__(config)
self.config = config
self.embeddings = ViTMSNEmbeddings(config, use_mask_token=use_mask_token)
self.encoder = ViTMSNEncoder(config)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> ViTMSNPatchEmbeddings:
return self.embeddings.patch_embeddings
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(VIT_MSN_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
bool_masked_pos: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, BaseModelOutput]:
r"""
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, ViTMSNModel
>>> import torch
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-msn-small")
>>> model = ViTMSNModel.from_pretrained("facebook/vit-msn-small")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding
)
encoder_outputs = self.encoder(
embedding_output,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(sequence_output)
if not return_dict:
head_outputs = (sequence_output,)
return head_outputs + encoder_outputs[1:]
return BaseModelOutput(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
# Caution: We don't have the weights for the classification head yet. This class
# is here for the users that are interested to fine-tune the base model (ViTMSNModel).
@add_start_docstrings(
"""
ViTMSN Model with an image classification head on top e.g. for ImageNet.
""",
VIT_MSN_START_DOCSTRING,
)
class ViTMSNForImageClassification(ViTMSNPreTrainedModel):
def __init__(self, config: ViTMSNConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.vit = ViTMSNModel(config)
# Classifier head
self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(VIT_MSN_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, ImageClassifierOutput]:
r"""
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, ViTMSNForImageClassification
>>> import torch
>>> from PIL import Image
>>> import requests
>>> torch.manual_seed(2) # doctest: +IGNORE_RESULT
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-msn-small")
>>> model = ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
Kerry blue terrier
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.vit(
pixel_values,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output[:, 0, :])
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/vit_msn/configuration_vit_msn.py | # coding=utf-8
# Copyright 2022 Facebook AI and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" ViT MSN model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class ViTMSNConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ViTMSNModel`]. It is used to instantiate an ViT
MSN model according to the specified arguments, defining the model architecture. Instantiating a configuration with
the defaults will yield a similar configuration to that of the ViT
[facebook/vit_msn_base](https://huggingface.co/facebook/vit_msn_base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 16):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries, keys and values.
Example:
```python
>>> from transformers import ViTMSNModel, ViTMSNConfig
>>> # Initializing a ViT MSN vit-msn-base style configuration
>>> configuration = ViTConfig()
>>> # Initializing a model from the vit-msn-base style configuration
>>> model = ViTMSNModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "vit_msn"
def __init__(
self,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
initializer_range=0.02,
layer_norm_eps=1e-06,
image_size=224,
patch_size=16,
num_channels=3,
qkv_bias=True,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.qkv_bias = qkv_bias
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/yoso/modeling_yoso.py | # coding=utf-8
# Copyright 2022 University of Wisconsin-Madison and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch YOSO model."""
import math
from pathlib import Path
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithCrossAttentions,
MaskedLMOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_yoso import YosoConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "uw-madison/yoso-4096"
_CONFIG_FOR_DOC = "YosoConfig"
YOSO_PRETRAINED_MODEL_ARCHIVE_LIST = [
"uw-madison/yoso-4096",
# See all YOSO models at https://huggingface.co/models?filter=yoso
]
def load_cuda_kernels():
global lsh_cumulation
try:
from torch.utils.cpp_extension import load
def append_root(files):
src_folder = Path(__file__).resolve().parent.parent.parent / "kernels" / "yoso"
return [src_folder / file for file in files]
src_files = append_root(
["fast_lsh_cumulation_torch.cpp", "fast_lsh_cumulation.cu", "fast_lsh_cumulation_cuda.cu"]
)
load("fast_lsh_cumulation", src_files, verbose=True)
import fast_lsh_cumulation as lsh_cumulation
return True
except Exception:
lsh_cumulation = None
return False
def to_contiguous(input_tensors):
if isinstance(input_tensors, list):
out = []
for tensor in input_tensors:
if not tensor.is_contiguous():
tensor = tensor.contiguous()
out.append(tensor)
return out
else:
if not input_tensors.is_contiguous():
input_tensors = input_tensors.contiguous()
return input_tensors
def normalize(input_tensors):
if isinstance(input_tensors, list):
out = []
for tensor in input_tensors:
out.append(nn.functional.normalize(tensor, p=2, dim=-1))
return out
else:
return nn.functional.normalize(input_tensors, p=2, dim=-1)
def hashing(query, key, num_hash, hash_len):
if len(query.size()) != 3:
raise ValueError("Query has incorrect size.")
if len(key.size()) != 3:
raise ValueError("Key has incorrect size.")
rmat = torch.randn(query.size(0), query.size(2), num_hash * hash_len, device=query.device)
raise_pow = 2 ** torch.arange(hash_len, device=query.device)
query_projection = torch.matmul(query, rmat).reshape(query.size(0), query.size(1), num_hash, hash_len)
key_projection = torch.matmul(key, rmat).reshape(key.size(0), key.size(1), num_hash, hash_len)
query_binary = (query_projection > 0).int()
key_binary = (key_projection > 0).int()
query_hash = torch.sum(query_binary * raise_pow, dim=-1)
query_hash = torch.sum(key_binary * raise_pow, dim=-1)
return query_hash.int(), query_hash.int()
class YosoCumulation(torch.autograd.Function):
@staticmethod
def forward(ctx, query_mask, key_mask, query, key, value, config):
hash_code_len = config["hash_code_len"]
expectation = (1 - torch.acos(torch.matmul(query, key.transpose(-1, -2))) / math.pi) ** hash_code_len
expectation = expectation * query_mask[:, :, None] * key_mask[:, None, :]
cumulation_value = torch.matmul(expectation, value)
ctx.save_for_backward(query_mask, key_mask, expectation, query, key, value)
ctx.config = config
return cumulation_value
@staticmethod
def backward(ctx, grad):
grad = to_contiguous(grad)
query_mask, key_mask, expectation, query, key, value = ctx.saved_tensors
config = ctx.config
hash_code_len = config["hash_code_len"]
weighted_exp = torch.matmul(grad, value.transpose(-1, -2)) * expectation
grad_query = torch.matmul(weighted_exp, (hash_code_len / 2) * key)
grad_key = torch.matmul(weighted_exp.transpose(-1, -2), (hash_code_len / 2) * query)
grad_value = torch.matmul(expectation.transpose(-1, -2), grad)
return None, None, grad_query, grad_key, grad_value, None
class YosoLSHCumulation(torch.autograd.Function):
@staticmethod
def forward(ctx, query_mask, key_mask, query, key, value, config):
if query_mask.size(0) != key_mask.size(0):
raise ValueError("Query mask and Key mask differ in sizes in dimension 0")
if query_mask.size(0) != query.size(0):
raise ValueError("Query mask and Query differ in sizes in dimension 0")
if query_mask.size(0) != key.size(0):
raise ValueError("Query mask and Key differ in sizes in dimension 0")
if query_mask.size(0) != value.size(0):
raise ValueError("Query mask and Value mask differ in sizes in dimension 0")
if key.size(1) != value.size(1):
raise ValueError("Key and Value differ in sizes in dimension 1")
if query.size(2) != key.size(2):
raise ValueError("Query and Key differ in sizes in dimension 2")
query_mask, key_mask, query, key, value = to_contiguous([query_mask, key_mask, query, key, value])
use_cuda = query_mask.is_cuda
num_hash = config["num_hash"]
hash_code_len = config["hash_code_len"]
hashtable_capacity = int(2**hash_code_len)
if config["use_fast_hash"]:
query_hash_code, key_hash_code = lsh_cumulation.fast_hash(
query_mask, query, key_mask, key, num_hash, hash_code_len, use_cuda, 1
)
else:
query_hash_code, key_hash_code = hashing(query, key, num_hash, hash_code_len)
cumulation_value = lsh_cumulation.lsh_cumulation(
query_mask, query_hash_code, key_mask, key_hash_code, value, hashtable_capacity, use_cuda, 1
)
ctx.save_for_backward(query_mask, key_mask, query_hash_code, key_hash_code, query, key, value)
ctx.config = config
return cumulation_value
@staticmethod
def backward(ctx, grad):
grad = to_contiguous(grad)
query_mask, key_mask, query_hash_code, key_hash_code, query, key, value = ctx.saved_tensors
config = ctx.config
use_cuda = grad.is_cuda
hash_code_len = config["hash_code_len"]
hashtable_capacity = int(2**hash_code_len)
if config["lsh_backward"]:
grad_value = lsh_cumulation.lsh_cumulation(
key_mask, key_hash_code, query_mask, query_hash_code, grad, hashtable_capacity, use_cuda, 1
)
grad_query = lsh_cumulation.lsh_weighted_cumulation(
query_mask,
query_hash_code,
grad,
key_mask,
key_hash_code,
value,
(hash_code_len / 2) * key,
hashtable_capacity,
use_cuda,
4,
)
grad_key = lsh_cumulation.lsh_weighted_cumulation(
key_mask,
key_hash_code,
value,
query_mask,
query_hash_code,
grad,
(hash_code_len / 2) * query,
hashtable_capacity,
use_cuda,
4,
)
else:
expectation = (1 - torch.acos(torch.matmul(query, key.transpose(-1, -2))) / math.pi) ** hash_code_len
expectation = expectation * query_mask[:, :, None] * key_mask[:, None, :]
weighted_exp = torch.matmul(grad, value.transpose(-1, -2)) * expectation
grad_query = torch.matmul(weighted_exp, (hash_code_len / 2) * key)
grad_key = torch.matmul(weighted_exp.transpose(-1, -2), (hash_code_len / 2) * query)
grad_value = torch.matmul(expectation.transpose(-1, -2), grad)
return None, None, grad_query, grad_key, grad_value, None
# Copied from transformers.models.nystromformer.modeling_nystromformer.NystromformerEmbeddings
class YosoEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings + 2, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + 2, persistent=False
)
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
self.register_buffer(
"token_type_ids",
torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device),
persistent=False,
)
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class YosoSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = (
position_embedding_type if position_embedding_type is not None else config.position_embedding_type
)
self.use_expectation = config.use_expectation
self.hash_code_len = config.hash_code_len
self.use_conv = config.conv_window is not None
self.use_fast_hash = config.use_fast_hash
self.num_hash = config.num_hash
self.lsh_backward = config.lsh_backward
self.lsh_config = {
"hash_code_len": self.hash_code_len,
"use_fast_hash": self.use_fast_hash,
"num_hash": self.num_hash,
"lsh_backward": self.lsh_backward,
}
if config.conv_window is not None:
self.conv = nn.Conv2d(
in_channels=config.num_attention_heads,
out_channels=config.num_attention_heads,
kernel_size=(config.conv_window, 1),
padding=(config.conv_window // 2, 0),
bias=False,
groups=config.num_attention_heads,
)
def transpose_for_scores(self, layer):
new_layer_shape = layer.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
layer = layer.view(*new_layer_shape)
return layer.permute(0, 2, 1, 3)
def forward(self, hidden_states, attention_mask=None, output_attentions=False):
mixed_query_layer = self.query(hidden_states)
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
if self.use_conv:
conv_value_layer = self.conv(value_layer * attention_mask[:, None, :, None])
batch_size, num_heads, seq_len, head_dim = query_layer.size()
query_layer = query_layer.reshape(batch_size * num_heads, seq_len, head_dim)
key_layer = key_layer.reshape(batch_size * num_heads, seq_len, head_dim)
value_layer = value_layer.reshape(batch_size * num_heads, seq_len, head_dim)
# revert changes made by get_extended_attention_mask
attention_mask = 1.0 + attention_mask / 10000.0
attention_mask = (
attention_mask.squeeze().repeat(1, num_heads, 1).reshape(batch_size * num_heads, seq_len).int()
)
# The CUDA kernels are most efficient with inputs whose size is a multiple of a GPU's warp size (32). Inputs
# smaller than this are padded with zeros.
gpu_warp_size = 32
if (not self.use_expectation) and head_dim < gpu_warp_size:
pad_size = batch_size * num_heads, seq_len, gpu_warp_size - head_dim
query_layer = torch.cat(
[
query_layer,
torch.zeros(pad_size, device=query_layer.device),
],
dim=-1,
)
key_layer = torch.cat(
[
key_layer,
torch.zeros(pad_size, device=key_layer.device),
],
dim=-1,
)
value_layer = torch.cat(
[
value_layer,
torch.zeros(pad_size, device=value_layer.device),
],
dim=-1,
)
if self.use_expectation or self.training:
query_layer, key_layer = normalize([query_layer, key_layer])
if self.use_expectation:
context_layer = YosoCumulation.apply(
attention_mask, attention_mask, query_layer, key_layer, value_layer, self.lsh_config
)
else:
context_layer = YosoLSHCumulation.apply(
attention_mask, attention_mask, query_layer, key_layer, value_layer, self.lsh_config
)
if (not self.use_expectation) and head_dim < gpu_warp_size:
context_layer = context_layer[:, :, :head_dim]
context_layer = normalize(context_layer)
context_layer = context_layer.reshape(batch_size, num_heads, seq_len, head_dim)
if self.use_conv:
context_layer += conv_value_layer
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, context_layer) if output_attentions else (context_layer,)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
class YosoSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class YosoAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = YosoSelfAttention(config, position_embedding_type=position_embedding_type)
self.output = YosoSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(self, hidden_states, attention_mask=None, output_attentions=False):
self_outputs = self.self(hidden_states, attention_mask, output_attentions)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
class YosoIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOutput
class YosoOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class YosoLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = YosoAttention(config)
self.add_cross_attention = config.add_cross_attention
self.intermediate = YosoIntermediate(config)
self.output = YosoOutput(config)
def forward(self, hidden_states, attention_mask=None, output_attentions=False):
self_attention_outputs = self.attention(hidden_states, attention_mask, output_attentions=output_attentions)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class YosoEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([YosoLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
output_attentions,
)
else:
layer_outputs = layer_module(hidden_states, attention_mask, output_attentions)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutputWithCrossAttentions(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform
class YosoPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->Yoso
class YosoLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = YosoPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->Yoso
class YosoOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = YosoLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores
class YosoPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = YosoConfig
base_model_prefix = "yoso"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
YOSO_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`YosoConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
YOSO_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare YOSO Model transformer outputting raw hidden-states without any specific head on top.",
YOSO_START_DOCSTRING,
)
class YosoModel(YosoPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = YosoEmbeddings(config)
self.encoder = YosoEncoder(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(YOSO_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithCrossAttentions]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length)), device=device)
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
if not return_dict:
return (sequence_output,) + encoder_outputs[1:]
return BaseModelOutputWithCrossAttentions(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
@add_start_docstrings("""YOSO Model with a `language modeling` head on top.""", YOSO_START_DOCSTRING)
class YosoForMaskedLM(YosoPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.yoso = YosoModel(config)
self.cls = YosoOnlyMLMHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward(YOSO_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, MaskedLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.yoso(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss() # -100 index = padding token
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class YosoClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
self.config = config
def forward(self, features, **kwargs):
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x)
x = self.dense(x)
x = ACT2FN[self.config.hidden_act](x)
x = self.dropout(x)
x = self.out_proj(x)
return x
@add_start_docstrings(
"""YOSO Model transformer with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks.""",
YOSO_START_DOCSTRING,
)
class YosoForSequenceClassification(YosoPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.yoso = YosoModel(config)
self.classifier = YosoClassificationHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(YOSO_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.yoso(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""YOSO Model with a multiple choice classification head on top (a linear layer on top of
the pooled output and a softmax) e.g. for RocStories/SWAG tasks.""",
YOSO_START_DOCSTRING,
)
class YosoForMultipleChoice(YosoPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.yoso = YosoModel(config)
self.pre_classifier = nn.Linear(config.hidden_size, config.hidden_size)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(YOSO_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, MultipleChoiceModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.yoso(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_state = outputs[0] # (bs * num_choices, seq_len, dim)
pooled_output = hidden_state[:, 0] # (bs * num_choices, dim)
pooled_output = self.pre_classifier(pooled_output) # (bs * num_choices, dim)
pooled_output = nn.ReLU()(pooled_output) # (bs * num_choices, dim)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""YOSO Model with a token classification head on top (a linear layer on top of
the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.""",
YOSO_START_DOCSTRING,
)
class YosoForTokenClassification(YosoPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.yoso = YosoModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(YOSO_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.yoso(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
# Only keep active parts of the loss
if attention_mask is not None:
active_loss = attention_mask.view(-1) == 1
active_logits = logits.view(-1, self.num_labels)
active_labels = torch.where(
active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
)
loss = loss_fct(active_logits, active_labels)
else:
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""YOSO Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).""",
YOSO_START_DOCSTRING,
)
class YosoForQuestionAnswering(YosoPreTrainedModel):
def __init__(self, config):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.yoso = YosoModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(YOSO_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
start_positions: Optional[torch.Tensor] = None,
end_positions: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.yoso(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[1:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/yoso/configuration_yoso.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" YOSO model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
# See all YOSO models at https://huggingface.co/models?filter=yoso
}
class YosoConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`YosoModel`]. It is used to instantiate an YOSO
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the YOSO
[uw-madison/yoso-4096](https://huggingface.co/uw-madison/yoso-4096) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50265):
Vocabulary size of the YOSO model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`YosoModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimension of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`YosoModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`.
use_expectation (`bool`, *optional*, defaults to `True`):
Whether or not to use YOSO Expectation. Overrides any effect of num_hash.
hash_code_len (`int`, *optional*, defaults to 9):
The length of hashes generated by the hash functions.
num_hash (`int`, *optional*, defaults to 64):
Number of hash functions used in [`YosoSelfAttention`].
conv_window (`int`, *optional*):
Kernel size of depth-wise convolution.
use_fast_hash (`bool`, *optional*, defaults to `False`):
Whether or not to use custom cuda kernels which perform fast random projection via hadamard transform.
lsh_backward (`bool`, *optional*, defaults to `True`):
Whether or not to perform backpropagation using Locality Sensitive Hashing.
Example:
```python
>>> from transformers import YosoConfig, YosoModel
>>> # Initializing a YOSO uw-madison/yoso-4096 style configuration
>>> configuration = YosoConfig()
>>> # Initializing a model (with random weights) from the uw-madison/yoso-4096 style configuration
>>> model = YosoModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "yoso"
def __init__(
self,
vocab_size=50265,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=4096,
type_vocab_size=1,
initializer_range=0.02,
layer_norm_eps=1e-12,
position_embedding_type="absolute",
use_expectation=True,
hash_code_len=9,
num_hash=64,
conv_window=None,
use_fast_hash=True,
lsh_backward=True,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.type_vocab_size = type_vocab_size
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.use_expectation = use_expectation
self.hash_code_len = hash_code_len
self.num_hash = num_hash
self.conv_window = conv_window
self.use_fast_hash = use_fast_hash
self.lsh_backward = lsh_backward
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/yoso/__init__.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_import_structure = {"configuration_yoso": ["YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP", "YosoConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_yoso"] = [
"YOSO_PRETRAINED_MODEL_ARCHIVE_LIST",
"YosoForMaskedLM",
"YosoForMultipleChoice",
"YosoForQuestionAnswering",
"YosoForSequenceClassification",
"YosoForTokenClassification",
"YosoLayer",
"YosoModel",
"YosoPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yoso import (
YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
YosoForMaskedLM,
YosoForMultipleChoice,
YosoForQuestionAnswering,
YosoForSequenceClassification,
YosoForTokenClassification,
YosoLayer,
YosoModel,
YosoPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/yoso/convert_yoso_pytorch_to_pytorch.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert YOSO checkpoints from the original repository. URL: https://github.com/mlpen/YOSO"""
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def rename_key(orig_key):
if "model" in orig_key:
orig_key = orig_key.replace("model.", "")
if "norm1" in orig_key:
orig_key = orig_key.replace("norm1", "attention.output.LayerNorm")
if "norm2" in orig_key:
orig_key = orig_key.replace("norm2", "output.LayerNorm")
if "norm" in orig_key:
orig_key = orig_key.replace("norm", "LayerNorm")
if "transformer" in orig_key:
layer_num = orig_key.split(".")[0].split("_")[-1]
orig_key = orig_key.replace(f"transformer_{layer_num}", f"encoder.layer.{layer_num}")
if "mha.attn" in orig_key:
orig_key = orig_key.replace("mha.attn", "attention.self")
if "mha" in orig_key:
orig_key = orig_key.replace("mha", "attention")
if "W_q" in orig_key:
orig_key = orig_key.replace("W_q", "self.query")
if "W_k" in orig_key:
orig_key = orig_key.replace("W_k", "self.key")
if "W_v" in orig_key:
orig_key = orig_key.replace("W_v", "self.value")
if "ff1" in orig_key:
orig_key = orig_key.replace("ff1", "intermediate.dense")
if "ff2" in orig_key:
orig_key = orig_key.replace("ff2", "output.dense")
if "ff" in orig_key:
orig_key = orig_key.replace("ff", "output.dense")
if "mlm_class" in orig_key:
orig_key = orig_key.replace("mlm.mlm_class", "cls.predictions.decoder")
if "mlm" in orig_key:
orig_key = orig_key.replace("mlm", "cls.predictions.transform")
if "cls" not in orig_key:
orig_key = "yoso." + orig_key
return orig_key
def convert_checkpoint_helper(max_position_embeddings, orig_state_dict):
for key in orig_state_dict.copy().keys():
val = orig_state_dict.pop(key)
if ("pooler" in key) or ("sen_class" in key):
continue
else:
orig_state_dict[rename_key(key)] = val
orig_state_dict["cls.predictions.bias"] = orig_state_dict["cls.predictions.decoder.bias"]
orig_state_dict["yoso.embeddings.position_ids"] = torch.arange(max_position_embeddings).expand((1, -1)) + 2
return orig_state_dict
def convert_yoso_checkpoint(checkpoint_path, yoso_config_file, pytorch_dump_path):
orig_state_dict = torch.load(checkpoint_path, map_location="cpu")["model_state_dict"]
config = YosoConfig.from_json_file(yoso_config_file)
model = YosoForMaskedLM(config)
new_state_dict = convert_checkpoint_helper(config.max_position_embeddings, orig_state_dict)
print(model.load_state_dict(new_state_dict))
model.eval()
model.save_pretrained(pytorch_dump_path)
print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The json file for YOSO model config.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
args = parser.parse_args()
convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/resnet/convert_resnet_to_pytorch.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert ResNet checkpoints from timm."""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger()
@dataclass
class Tracker:
module: nn.Module
traced: List[nn.Module] = field(default_factory=list)
handles: list = field(default_factory=list)
def _forward_hook(self, m, inputs: Tensor, outputs: Tensor):
has_not_submodules = len(list(m.modules())) == 1 or isinstance(m, nn.Conv2d) or isinstance(m, nn.BatchNorm2d)
if has_not_submodules:
self.traced.append(m)
def __call__(self, x: Tensor):
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook))
self.module(x)
[x.remove() for x in self.handles]
return self
@property
def parametrized(self):
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda x: len(list(x.state_dict().keys())) > 0, self.traced))
@dataclass
class ModuleTransfer:
src: nn.Module
dest: nn.Module
verbose: int = 0
src_skip: List = field(default_factory=list)
dest_skip: List = field(default_factory=list)
def __call__(self, x: Tensor):
"""
Transfer the weights of `self.src` to `self.dest` by performing a forward pass using `x` as input. Under the
hood we tracked all the operations in both modules.
"""
dest_traced = Tracker(self.dest)(x).parametrized
src_traced = Tracker(self.src)(x).parametrized
src_traced = list(filter(lambda x: type(x) not in self.src_skip, src_traced))
dest_traced = list(filter(lambda x: type(x) not in self.dest_skip, dest_traced))
if len(dest_traced) != len(src_traced):
raise Exception(
f"Numbers of operations are different. Source module has {len(src_traced)} operations while"
f" destination module has {len(dest_traced)}."
)
for dest_m, src_m in zip(dest_traced, src_traced):
dest_m.load_state_dict(src_m.state_dict())
if self.verbose == 1:
print(f"Transfered from={src_m} to={dest_m}")
def convert_weight_and_push(name: str, config: ResNetConfig, save_directory: Path, push_to_hub: bool = True):
print(f"Converting {name}...")
with torch.no_grad():
from_model = timm.create_model(name, pretrained=True).eval()
our_model = ResNetForImageClassification(config).eval()
module_transfer = ModuleTransfer(src=from_model, dest=our_model)
x = torch.randn((1, 3, 224, 224))
module_transfer(x)
assert torch.allclose(from_model(x), our_model(x).logits), "The model logits don't match the original one."
checkpoint_name = f"resnet{'-'.join(name.split('resnet'))}"
print(checkpoint_name)
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name,
commit_message="Add model",
use_temp_dir=True,
)
# we can use the convnext one
image_processor = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k")
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name,
commit_message="Add image processor",
use_temp_dir=True,
)
print(f"Pushed {checkpoint_name}")
def convert_weights_and_push(save_directory: Path, model_name: str = None, push_to_hub: bool = True):
filename = "imagenet-1k-id2label.json"
num_labels = 1000
expected_shape = (1, num_labels)
repo_id = "huggingface/label-files"
num_labels = num_labels
id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
id2label = {int(k): v for k, v in id2label.items()}
id2label = id2label
label2id = {v: k for k, v in id2label.items()}
ImageNetPreTrainedConfig = partial(ResNetConfig, num_labels=num_labels, id2label=id2label, label2id=label2id)
names_to_config = {
"resnet18": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2], hidden_sizes=[64, 128, 256, 512], layer_type="basic"
),
"resnet26": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2], hidden_sizes=[256, 512, 1024, 2048], layer_type="bottleneck"
),
"resnet34": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3], hidden_sizes=[64, 128, 256, 512], layer_type="basic"
),
"resnet50": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type="bottleneck"
),
"resnet101": ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type="bottleneck"
),
"resnet152": ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type="bottleneck"
),
}
if model_name:
convert_weight_and_push(model_name, names_to_config[model_name], save_directory, push_to_hub)
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(model_name, config, save_directory, push_to_hub)
return config, expected_shape
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
args = parser.parse_args()
pytorch_dump_folder_path: Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/resnet/modeling_tf_resnet.py | # coding=utf-8
# Copyright 2022 Microsoft Research, Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TensorFlow ResNet model."""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACT2FN
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "ResNetConfig"
# Base docstring
_CHECKPOINT_FOR_DOC = "microsoft/resnet-50"
_EXPECTED_OUTPUT_SHAPE = [1, 2048, 7, 7]
# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "microsoft/resnet-50"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tiger cat"
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST = [
"microsoft/resnet-50",
# See all resnet models at https://huggingface.co/models?filter=resnet
]
class TFResNetConvLayer(tf.keras.layers.Layer):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int = 3,
stride: int = 1,
activation: str = "relu",
**kwargs,
) -> None:
super().__init__(**kwargs)
self.pad_value = kernel_size // 2
self.conv = tf.keras.layers.Conv2D(
out_channels, kernel_size=kernel_size, strides=stride, padding="valid", use_bias=False, name="convolution"
)
# Use same default momentum and epsilon as PyTorch equivalent
self.normalization = tf.keras.layers.BatchNormalization(epsilon=1e-5, momentum=0.9, name="normalization")
self.activation = ACT2FN[activation] if activation is not None else tf.keras.layers.Activation("linear")
self.in_channels = in_channels
self.out_channels = out_channels
def convolution(self, hidden_state: tf.Tensor) -> tf.Tensor:
# Pad to match that done in the PyTorch Conv2D model
height_pad = width_pad = (self.pad_value, self.pad_value)
hidden_state = tf.pad(hidden_state, [(0, 0), height_pad, width_pad, (0, 0)])
hidden_state = self.conv(hidden_state)
return hidden_state
def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_state = self.convolution(hidden_state)
hidden_state = self.normalization(hidden_state, training=training)
hidden_state = self.activation(hidden_state)
return hidden_state
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "conv", None) is not None:
with tf.name_scope(self.conv.name):
self.conv.build([None, None, None, self.in_channels])
if getattr(self, "normalization", None) is not None:
with tf.name_scope(self.normalization.name):
self.normalization.build([None, None, None, self.out_channels])
class TFResNetEmbeddings(tf.keras.layers.Layer):
"""
ResNet Embeddings (stem) composed of a single aggressive convolution.
"""
def __init__(self, config: ResNetConfig, **kwargs) -> None:
super().__init__(**kwargs)
self.embedder = TFResNetConvLayer(
config.num_channels,
config.embedding_size,
kernel_size=7,
stride=2,
activation=config.hidden_act,
name="embedder",
)
self.pooler = tf.keras.layers.MaxPool2D(pool_size=3, strides=2, padding="valid", name="pooler")
self.num_channels = config.num_channels
def call(self, pixel_values: tf.Tensor, training: bool = False) -> tf.Tensor:
_, _, _, num_channels = shape_list(pixel_values)
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
hidden_state = pixel_values
hidden_state = self.embedder(hidden_state)
hidden_state = tf.pad(hidden_state, [[0, 0], [1, 1], [1, 1], [0, 0]])
hidden_state = self.pooler(hidden_state)
return hidden_state
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embedder", None) is not None:
with tf.name_scope(self.embedder.name):
self.embedder.build(None)
if getattr(self, "pooler", None) is not None:
with tf.name_scope(self.pooler.name):
self.pooler.build(None)
class TFResNetShortCut(tf.keras.layers.Layer):
"""
ResNet shortcut, used to project the residual features to the correct size. If needed, it is also used to
downsample the input using `stride=2`.
"""
def __init__(self, in_channels: int, out_channels: int, stride: int = 2, **kwargs) -> None:
super().__init__(**kwargs)
self.convolution = tf.keras.layers.Conv2D(
out_channels, kernel_size=1, strides=stride, use_bias=False, name="convolution"
)
# Use same default momentum and epsilon as PyTorch equivalent
self.normalization = tf.keras.layers.BatchNormalization(epsilon=1e-5, momentum=0.9, name="normalization")
self.in_channels = in_channels
self.out_channels = out_channels
def call(self, x: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_state = x
hidden_state = self.convolution(hidden_state)
hidden_state = self.normalization(hidden_state, training=training)
return hidden_state
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "convolution", None) is not None:
with tf.name_scope(self.convolution.name):
self.convolution.build([None, None, None, self.in_channels])
if getattr(self, "normalization", None) is not None:
with tf.name_scope(self.normalization.name):
self.normalization.build([None, None, None, self.out_channels])
class TFResNetBasicLayer(tf.keras.layers.Layer):
"""
A classic ResNet's residual layer composed by two `3x3` convolutions.
"""
def __init__(
self, in_channels: int, out_channels: int, stride: int = 1, activation: str = "relu", **kwargs
) -> None:
super().__init__(**kwargs)
should_apply_shortcut = in_channels != out_channels or stride != 1
self.conv1 = TFResNetConvLayer(in_channels, out_channels, stride=stride, name="layer.0")
self.conv2 = TFResNetConvLayer(out_channels, out_channels, activation=None, name="layer.1")
self.shortcut = (
TFResNetShortCut(in_channels, out_channels, stride=stride, name="shortcut")
if should_apply_shortcut
else tf.keras.layers.Activation("linear", name="shortcut")
)
self.activation = ACT2FN[activation]
def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor:
residual = hidden_state
hidden_state = self.conv1(hidden_state, training=training)
hidden_state = self.conv2(hidden_state, training=training)
residual = self.shortcut(residual, training=training)
hidden_state += residual
hidden_state = self.activation(hidden_state)
return hidden_state
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "conv1", None) is not None:
with tf.name_scope(self.conv1.name):
self.conv1.build(None)
if getattr(self, "conv2", None) is not None:
with tf.name_scope(self.conv2.name):
self.conv2.build(None)
if getattr(self, "shortcut", None) is not None:
with tf.name_scope(self.shortcut.name):
self.shortcut.build(None)
class TFResNetBottleNeckLayer(tf.keras.layers.Layer):
"""
A classic ResNet's bottleneck layer composed by three `3x3` convolutions.
The first `1x1` convolution reduces the input by a factor of `reduction` in order to make the second `3x3`
convolution faster. The last `1x1` convolution remaps the reduced features to `out_channels`.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
stride: int = 1,
activation: str = "relu",
reduction: int = 4,
**kwargs,
) -> None:
super().__init__(**kwargs)
should_apply_shortcut = in_channels != out_channels or stride != 1
reduces_channels = out_channels // reduction
self.conv0 = TFResNetConvLayer(in_channels, reduces_channels, kernel_size=1, name="layer.0")
self.conv1 = TFResNetConvLayer(reduces_channels, reduces_channels, stride=stride, name="layer.1")
self.conv2 = TFResNetConvLayer(reduces_channels, out_channels, kernel_size=1, activation=None, name="layer.2")
self.shortcut = (
TFResNetShortCut(in_channels, out_channels, stride=stride, name="shortcut")
if should_apply_shortcut
else tf.keras.layers.Activation("linear", name="shortcut")
)
self.activation = ACT2FN[activation]
def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor:
residual = hidden_state
hidden_state = self.conv0(hidden_state, training=training)
hidden_state = self.conv1(hidden_state, training=training)
hidden_state = self.conv2(hidden_state, training=training)
residual = self.shortcut(residual, training=training)
hidden_state += residual
hidden_state = self.activation(hidden_state)
return hidden_state
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "conv0", None) is not None:
with tf.name_scope(self.conv0.name):
self.conv0.build(None)
if getattr(self, "conv1", None) is not None:
with tf.name_scope(self.conv1.name):
self.conv1.build(None)
if getattr(self, "conv2", None) is not None:
with tf.name_scope(self.conv2.name):
self.conv2.build(None)
if getattr(self, "shortcut", None) is not None:
with tf.name_scope(self.shortcut.name):
self.shortcut.build(None)
class TFResNetStage(tf.keras.layers.Layer):
"""
A ResNet stage composed of stacked layers.
"""
def __init__(
self, config: ResNetConfig, in_channels: int, out_channels: int, stride: int = 2, depth: int = 2, **kwargs
) -> None:
super().__init__(**kwargs)
layer = TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer
layers = [layer(in_channels, out_channels, stride=stride, activation=config.hidden_act, name="layers.0")]
layers += [
layer(out_channels, out_channels, activation=config.hidden_act, name=f"layers.{i + 1}")
for i in range(depth - 1)
]
self.stage_layers = layers
def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor:
for layer in self.stage_layers:
hidden_state = layer(hidden_state, training=training)
return hidden_state
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "stage_layers", None) is not None:
for layer in self.stage_layers:
with tf.name_scope(layer.name):
layer.build(None)
class TFResNetEncoder(tf.keras.layers.Layer):
def __init__(self, config: ResNetConfig, **kwargs) -> None:
super().__init__(**kwargs)
# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
self.stages = [
TFResNetStage(
config,
config.embedding_size,
config.hidden_sizes[0],
stride=2 if config.downsample_in_first_stage else 1,
depth=config.depths[0],
name="stages.0",
)
]
for i, (in_channels, out_channels, depth) in enumerate(
zip(config.hidden_sizes, config.hidden_sizes[1:], config.depths[1:])
):
self.stages.append(TFResNetStage(config, in_channels, out_channels, depth=depth, name=f"stages.{i + 1}"))
def call(
self,
hidden_state: tf.Tensor,
output_hidden_states: bool = False,
return_dict: bool = True,
training: bool = False,
) -> TFBaseModelOutputWithNoAttention:
hidden_states = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
hidden_states = hidden_states + (hidden_state,)
hidden_state = stage_module(hidden_state, training=training)
if output_hidden_states:
hidden_states = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None)
return TFBaseModelOutputWithNoAttention(last_hidden_state=hidden_state, hidden_states=hidden_states)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "stages", None) is not None:
for layer in self.stages:
with tf.name_scope(layer.name):
layer.build(None)
class TFResNetPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ResNetConfig
base_model_prefix = "resnet"
main_input_name = "pixel_values"
@property
def input_signature(self):
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224), dtype=tf.float32)}
RESNET_START_DOCSTRING = r"""
This model is a TensorFlow
[tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and
behavior.
Parameters:
config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""
RESNET_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@keras_serializable
class TFResNetMainLayer(tf.keras.layers.Layer):
config_class = ResNetConfig
def __init__(self, config: ResNetConfig, **kwargs) -> None:
super().__init__(**kwargs)
self.config = config
self.embedder = TFResNetEmbeddings(config, name="embedder")
self.encoder = TFResNetEncoder(config, name="encoder")
self.pooler = tf.keras.layers.GlobalAveragePooling2D(keepdims=True)
@unpack_inputs
def call(
self,
pixel_values: tf.Tensor,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# TF 2.0 image layers can't use NCHW format when running on CPU.
# We transpose to NHWC format and then transpose back after the full forward pass.
# (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
pixel_values = tf.transpose(pixel_values, perm=[0, 2, 3, 1])
embedding_output = self.embedder(pixel_values, training=training)
encoder_outputs = self.encoder(
embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training
)
last_hidden_state = encoder_outputs[0]
pooled_output = self.pooler(last_hidden_state)
# Transpose all the outputs to the NCHW format
# (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
last_hidden_state = tf.transpose(last_hidden_state, (0, 3, 1, 2))
pooled_output = tf.transpose(pooled_output, (0, 3, 1, 2))
hidden_states = ()
for hidden_state in encoder_outputs[1:]:
hidden_states = hidden_states + tuple(tf.transpose(h, (0, 3, 1, 2)) for h in hidden_state)
if not return_dict:
return (last_hidden_state, pooled_output) + hidden_states
hidden_states = hidden_states if output_hidden_states else None
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=hidden_states,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embedder", None) is not None:
with tf.name_scope(self.embedder.name):
self.embedder.build(None)
if getattr(self, "encoder", None) is not None:
with tf.name_scope(self.encoder.name):
self.encoder.build(None)
@add_start_docstrings(
"The bare ResNet model outputting raw features without any specific head on top.",
RESNET_START_DOCSTRING,
)
class TFResNetModel(TFResNetPreTrainedModel):
def __init__(self, config: ResNetConfig, **kwargs) -> None:
super().__init__(config, **kwargs)
self.resnet = TFResNetMainLayer(config=config, name="resnet")
@add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPoolingAndNoAttention,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
@unpack_inputs
def call(
self,
pixel_values: tf.Tensor,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
resnet_outputs = self.resnet(
pixel_values=pixel_values,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return resnet_outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "resnet", None) is not None:
with tf.name_scope(self.resnet.name):
self.resnet.build(None)
@add_start_docstrings(
"""
ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""",
RESNET_START_DOCSTRING,
)
class TFResNetForImageClassification(TFResNetPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: ResNetConfig, **kwargs) -> None:
super().__init__(config, **kwargs)
self.num_labels = config.num_labels
self.resnet = TFResNetMainLayer(config, name="resnet")
# classification head
self.classifier_layer = (
tf.keras.layers.Dense(config.num_labels, name="classifier.1")
if config.num_labels > 0
else tf.keras.layers.Activation("linear", name="classifier.1")
)
self.config = config
def classifier(self, x: tf.Tensor) -> tf.Tensor:
x = tf.keras.layers.Flatten()(x)
logits = self.classifier_layer(x)
return logits
@add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=TFImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
@unpack_inputs
def call(
self,
pixel_values: tf.Tensor = None,
labels: tf.Tensor = None,
output_hidden_states: bool = None,
return_dict: bool = None,
training: bool = False,
) -> Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:
r"""
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.resnet(
pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training
)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.classifier(pooled_output)
loss = None if labels is None else self.hf_compute_loss(labels, logits)
if not return_dict:
output = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return TFImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "resnet", None) is not None:
with tf.name_scope(self.resnet.name):
self.resnet.build(None)
if getattr(self, "classifier_layer", None) is not None:
with tf.name_scope(self.classifier_layer.name):
self.classifier_layer.build([None, None, self.config.hidden_sizes[-1]])
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/resnet/modeling_flax_resnet.py | # coding=utf-8
# Copyright 2023 HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
from typing import Optional, Tuple
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.traverse_util import flatten_dict, unflatten_dict
from ...modeling_flax_outputs import (
FlaxBaseModelOutputWithNoAttention,
FlaxBaseModelOutputWithPoolingAndNoAttention,
FlaxImageClassifierOutputWithNoAttention,
)
from ...modeling_flax_utils import (
ACT2FN,
FlaxPreTrainedModel,
append_replace_return_docstrings,
overwrite_call_docstring,
)
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward
from .configuration_resnet import ResNetConfig
RESNET_START_DOCSTRING = r"""
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a
[flax.linen.Module](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html) subclass. Use it as
a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and
behavior.
Finally, this model supports inherent JAX features such as:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
Parameters:
config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
`jax.numpy.bfloat16` (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.**
If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
[`~FlaxPreTrainedModel.to_bf16`].
"""
RESNET_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`jax.numpy.float32` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`AutoImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class Identity(nn.Module):
"""Identity function."""
@nn.compact
def __call__(self, x, **kwargs):
return x
class FlaxResNetConvLayer(nn.Module):
out_channels: int
kernel_size: int = 3
stride: int = 1
activation: Optional[str] = "relu"
dtype: jnp.dtype = jnp.float32
def setup(self):
self.convolution = nn.Conv(
self.out_channels,
kernel_size=(self.kernel_size, self.kernel_size),
strides=self.stride,
padding=self.kernel_size // 2,
dtype=self.dtype,
use_bias=False,
kernel_init=nn.initializers.variance_scaling(2.0, mode="fan_out", distribution="normal", dtype=self.dtype),
)
self.normalization = nn.BatchNorm(momentum=0.9, epsilon=1e-05, dtype=self.dtype)
self.activation_func = ACT2FN[self.activation] if self.activation is not None else Identity()
def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
hidden_state = self.convolution(x)
hidden_state = self.normalization(hidden_state, use_running_average=deterministic)
hidden_state = self.activation_func(hidden_state)
return hidden_state
class FlaxResNetEmbeddings(nn.Module):
"""
ResNet Embeddings (stem) composed of a single aggressive convolution.
"""
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.embedder = FlaxResNetConvLayer(
self.config.embedding_size,
kernel_size=7,
stride=2,
activation=self.config.hidden_act,
dtype=self.dtype,
)
self.max_pool = partial(nn.max_pool, window_shape=(3, 3), strides=(2, 2), padding=((1, 1), (1, 1)))
def __call__(self, pixel_values: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
num_channels = pixel_values.shape[-1]
if num_channels != self.config.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
embedding = self.embedder(pixel_values, deterministic=deterministic)
embedding = self.max_pool(embedding)
return embedding
class FlaxResNetShortCut(nn.Module):
"""
ResNet shortcut, used to project the residual features to the correct size. If needed, it is also used to
downsample the input using `stride=2`.
"""
out_channels: int
stride: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
self.convolution = nn.Conv(
self.out_channels,
kernel_size=(1, 1),
strides=self.stride,
use_bias=False,
kernel_init=nn.initializers.variance_scaling(2.0, mode="fan_out", distribution="truncated_normal"),
dtype=self.dtype,
)
self.normalization = nn.BatchNorm(momentum=0.9, epsilon=1e-05, dtype=self.dtype)
def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
hidden_state = self.convolution(x)
hidden_state = self.normalization(hidden_state, use_running_average=deterministic)
return hidden_state
class FlaxResNetBasicLayerCollection(nn.Module):
out_channels: int
stride: int = 1
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layer = [
FlaxResNetConvLayer(self.out_channels, stride=self.stride, dtype=self.dtype),
FlaxResNetConvLayer(self.out_channels, activation=None, dtype=self.dtype),
]
def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
for layer in self.layer:
hidden_state = layer(hidden_state, deterministic=deterministic)
return hidden_state
class FlaxResNetBasicLayer(nn.Module):
"""
A classic ResNet's residual layer composed by two `3x3` convolutions.
"""
in_channels: int
out_channels: int
stride: int = 1
activation: Optional[str] = "relu"
dtype: jnp.dtype = jnp.float32
def setup(self):
should_apply_shortcut = self.in_channels != self.out_channels or self.stride != 1
self.shortcut = (
FlaxResNetShortCut(self.out_channels, stride=self.stride, dtype=self.dtype)
if should_apply_shortcut
else None
)
self.layer = FlaxResNetBasicLayerCollection(
out_channels=self.out_channels,
stride=self.stride,
dtype=self.dtype,
)
self.activation_func = ACT2FN[self.activation]
def __call__(self, hidden_state, deterministic: bool = True):
residual = hidden_state
hidden_state = self.layer(hidden_state, deterministic=deterministic)
if self.shortcut is not None:
residual = self.shortcut(residual, deterministic=deterministic)
hidden_state += residual
hidden_state = self.activation_func(hidden_state)
return hidden_state
class FlaxResNetBottleNeckLayerCollection(nn.Module):
out_channels: int
stride: int = 1
activation: Optional[str] = "relu"
reduction: int = 4
dtype: jnp.dtype = jnp.float32
def setup(self):
reduces_channels = self.out_channels // self.reduction
self.layer = [
FlaxResNetConvLayer(reduces_channels, kernel_size=1, dtype=self.dtype, name="0"),
FlaxResNetConvLayer(reduces_channels, stride=self.stride, dtype=self.dtype, name="1"),
FlaxResNetConvLayer(self.out_channels, kernel_size=1, activation=None, dtype=self.dtype, name="2"),
]
def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
for layer in self.layer:
hidden_state = layer(hidden_state, deterministic=deterministic)
return hidden_state
class FlaxResNetBottleNeckLayer(nn.Module):
"""
A classic ResNet's bottleneck layer composed by three `3x3` convolutions. The first `1x1` convolution reduces the
input by a factor of `reduction` in order to make the second `3x3` convolution faster. The last `1x1` convolution
remaps the reduced features to `out_channels`.
"""
in_channels: int
out_channels: int
stride: int = 1
activation: Optional[str] = "relu"
reduction: int = 4
dtype: jnp.dtype = jnp.float32
def setup(self):
should_apply_shortcut = self.in_channels != self.out_channels or self.stride != 1
self.shortcut = (
FlaxResNetShortCut(self.out_channels, stride=self.stride, dtype=self.dtype)
if should_apply_shortcut
else None
)
self.layer = FlaxResNetBottleNeckLayerCollection(
self.out_channels,
stride=self.stride,
activation=self.activation,
reduction=self.reduction,
dtype=self.dtype,
)
self.activation_func = ACT2FN[self.activation]
def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
residual = hidden_state
if self.shortcut is not None:
residual = self.shortcut(residual, deterministic=deterministic)
hidden_state = self.layer(hidden_state, deterministic)
hidden_state += residual
hidden_state = self.activation_func(hidden_state)
return hidden_state
class FlaxResNetStageLayersCollection(nn.Module):
"""
A ResNet stage composed by stacked layers.
"""
config: ResNetConfig
in_channels: int
out_channels: int
stride: int = 2
depth: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
layer = FlaxResNetBottleNeckLayer if self.config.layer_type == "bottleneck" else FlaxResNetBasicLayer
layers = [
# downsampling is done in the first layer with stride of 2
layer(
self.in_channels,
self.out_channels,
stride=self.stride,
activation=self.config.hidden_act,
dtype=self.dtype,
name="0",
),
]
for i in range(self.depth - 1):
layers.append(
layer(
self.out_channels,
self.out_channels,
activation=self.config.hidden_act,
dtype=self.dtype,
name=str(i + 1),
)
)
self.layers = layers
def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
hidden_state = x
for layer in self.layers:
hidden_state = layer(hidden_state, deterministic=deterministic)
return hidden_state
class FlaxResNetStage(nn.Module):
"""
A ResNet stage composed by stacked layers.
"""
config: ResNetConfig
in_channels: int
out_channels: int
stride: int = 2
depth: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layers = FlaxResNetStageLayersCollection(
self.config,
in_channels=self.in_channels,
out_channels=self.out_channels,
stride=self.stride,
depth=self.depth,
dtype=self.dtype,
)
def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
return self.layers(x, deterministic=deterministic)
class FlaxResNetStageCollection(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
in_out_channels = zip(self.config.hidden_sizes, self.config.hidden_sizes[1:])
stages = [
FlaxResNetStage(
self.config,
self.config.embedding_size,
self.config.hidden_sizes[0],
stride=2 if self.config.downsample_in_first_stage else 1,
depth=self.config.depths[0],
dtype=self.dtype,
name="0",
)
]
for i, ((in_channels, out_channels), depth) in enumerate(zip(in_out_channels, self.config.depths[1:])):
stages.append(
FlaxResNetStage(self.config, in_channels, out_channels, depth=depth, dtype=self.dtype, name=str(i + 1))
)
self.stages = stages
def __call__(
self,
hidden_state: jnp.ndarray,
output_hidden_states: bool = False,
deterministic: bool = True,
) -> FlaxBaseModelOutputWithNoAttention:
hidden_states = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
hidden_states = hidden_states + (hidden_state.transpose(0, 3, 1, 2),)
hidden_state = stage_module(hidden_state, deterministic=deterministic)
return hidden_state, hidden_states
class FlaxResNetEncoder(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.stages = FlaxResNetStageCollection(self.config, dtype=self.dtype)
def __call__(
self,
hidden_state: jnp.ndarray,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
) -> FlaxBaseModelOutputWithNoAttention:
hidden_state, hidden_states = self.stages(
hidden_state, output_hidden_states=output_hidden_states, deterministic=deterministic
)
if output_hidden_states:
hidden_states = hidden_states + (hidden_state.transpose(0, 3, 1, 2),)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None)
return FlaxBaseModelOutputWithNoAttention(
last_hidden_state=hidden_state,
hidden_states=hidden_states,
)
class FlaxResNetPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ResNetConfig
base_model_prefix = "resnet"
main_input_name = "pixel_values"
module_class: nn.Module = None
def __init__(
self,
config: ResNetConfig,
input_shape=(1, 224, 224, 3),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs,
):
module = self.module_class(config=config, dtype=dtype, **kwargs)
if input_shape is None:
input_shape = (1, config.image_size, config.image_size, config.num_channels)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
pixel_values = jnp.zeros(input_shape, dtype=self.dtype)
rngs = {"params": rng}
random_params = self.module.init(rngs, pixel_values, return_dict=False)
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freeze(unflatten_dict(params))
else:
return random_params
@add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING)
def __call__(
self,
pixel_values,
params: dict = None,
train: bool = False,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
# Handle any PRNG if needed
rngs = {}
return self.module.apply(
{
"params": params["params"] if params is not None else self.params["params"],
"batch_stats": params["batch_stats"] if params is not None else self.params["batch_stats"],
},
jnp.array(pixel_values, dtype=jnp.float32),
not train,
output_hidden_states,
return_dict,
rngs=rngs,
mutable=["batch_stats"] if train else False, # Returing tuple with batch_stats only when train is True
)
class FlaxResNetModule(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.embedder = FlaxResNetEmbeddings(self.config, dtype=self.dtype)
self.encoder = FlaxResNetEncoder(self.config, dtype=self.dtype)
# Adaptive average pooling used in resnet
self.pooler = partial(
nn.avg_pool,
padding=((0, 0), (0, 0)),
)
def __call__(
self,
pixel_values,
deterministic: bool = True,
output_hidden_states: bool = False,
return_dict: bool = True,
) -> FlaxBaseModelOutputWithPoolingAndNoAttention:
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
embedding_output = self.embedder(pixel_values, deterministic=deterministic)
encoder_outputs = self.encoder(
embedding_output,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
last_hidden_state = encoder_outputs[0]
pooled_output = self.pooler(
last_hidden_state,
window_shape=(last_hidden_state.shape[1], last_hidden_state.shape[2]),
strides=(last_hidden_state.shape[1], last_hidden_state.shape[2]),
).transpose(0, 3, 1, 2)
last_hidden_state = last_hidden_state.transpose(0, 3, 1, 2)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return FlaxBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
)
@add_start_docstrings(
"The bare ResNet model outputting raw features without any specific head on top.",
RESNET_START_DOCSTRING,
)
class FlaxResNetModel(FlaxResNetPreTrainedModel):
module_class = FlaxResNetModule
FLAX_VISION_MODEL_DOCSTRING = """
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, FlaxResNetModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
>>> model = FlaxResNetModel.from_pretrained("microsoft/resnet-50")
>>> inputs = image_processor(images=image, return_tensors="np")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
```
"""
overwrite_call_docstring(FlaxResNetModel, FLAX_VISION_MODEL_DOCSTRING)
append_replace_return_docstrings(
FlaxResNetModel, output_type=FlaxBaseModelOutputWithPoolingAndNoAttention, config_class=ResNetConfig
)
class FlaxResNetClassifierCollection(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype, name="1")
def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
return self.classifier(x)
class FlaxResNetForImageClassificationModule(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.resnet = FlaxResNetModule(config=self.config, dtype=self.dtype)
if self.config.num_labels > 0:
self.classifier = FlaxResNetClassifierCollection(self.config, dtype=self.dtype)
else:
self.classifier = Identity()
def __call__(
self,
pixel_values=None,
deterministic: bool = True,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.resnet(
pixel_values,
deterministic=deterministic,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.classifier(pooled_output[:, :, 0, 0])
if not return_dict:
output = (logits,) + outputs[2:]
return output
return FlaxImageClassifierOutputWithNoAttention(logits=logits, hidden_states=outputs.hidden_states)
@add_start_docstrings(
"""
ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""",
RESNET_START_DOCSTRING,
)
class FlaxResNetForImageClassification(FlaxResNetPreTrainedModel):
module_class = FlaxResNetForImageClassificationModule
FLAX_VISION_CLASSIF_DOCSTRING = """
Returns:
Example:
```python
>>> from transformers import AutoImageProcessor, FlaxResNetForImageClassification
>>> from PIL import Image
>>> import jax
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
>>> model = FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50")
>>> inputs = image_processor(images=image, return_tensors="np")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_class_idx = jax.numpy.argmax(logits, axis=-1)
>>> print("Predicted class:", model.config.id2label[predicted_class_idx.item()])
```
"""
overwrite_call_docstring(FlaxResNetForImageClassification, FLAX_VISION_CLASSIF_DOCSTRING)
append_replace_return_docstrings(
FlaxResNetForImageClassification, output_type=FlaxImageClassifierOutputWithNoAttention, config_class=ResNetConfig
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/resnet/modeling_resnet.py | # coding=utf-8
# Copyright 2022 Microsoft Research, Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch ResNet model."""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "ResNetConfig"
# Base docstring
_CHECKPOINT_FOR_DOC = "microsoft/resnet-50"
_EXPECTED_OUTPUT_SHAPE = [1, 2048, 7, 7]
# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "microsoft/resnet-50"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tiger cat"
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST = [
"microsoft/resnet-50",
# See all resnet models at https://huggingface.co/models?filter=resnet
]
class ResNetConvLayer(nn.Module):
def __init__(
self, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1, activation: str = "relu"
):
super().__init__()
self.convolution = nn.Conv2d(
in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=kernel_size // 2, bias=False
)
self.normalization = nn.BatchNorm2d(out_channels)
self.activation = ACT2FN[activation] if activation is not None else nn.Identity()
def forward(self, input: Tensor) -> Tensor:
hidden_state = self.convolution(input)
hidden_state = self.normalization(hidden_state)
hidden_state = self.activation(hidden_state)
return hidden_state
class ResNetEmbeddings(nn.Module):
"""
ResNet Embeddings (stem) composed of a single aggressive convolution.
"""
def __init__(self, config: ResNetConfig):
super().__init__()
self.embedder = ResNetConvLayer(
config.num_channels, config.embedding_size, kernel_size=7, stride=2, activation=config.hidden_act
)
self.pooler = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.num_channels = config.num_channels
def forward(self, pixel_values: Tensor) -> Tensor:
num_channels = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
embedding = self.embedder(pixel_values)
embedding = self.pooler(embedding)
return embedding
class ResNetShortCut(nn.Module):
"""
ResNet shortcut, used to project the residual features to the correct size. If needed, it is also used to
downsample the input using `stride=2`.
"""
def __init__(self, in_channels: int, out_channels: int, stride: int = 2):
super().__init__()
self.convolution = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False)
self.normalization = nn.BatchNorm2d(out_channels)
def forward(self, input: Tensor) -> Tensor:
hidden_state = self.convolution(input)
hidden_state = self.normalization(hidden_state)
return hidden_state
class ResNetBasicLayer(nn.Module):
"""
A classic ResNet's residual layer composed by two `3x3` convolutions.
"""
def __init__(self, in_channels: int, out_channels: int, stride: int = 1, activation: str = "relu"):
super().__init__()
should_apply_shortcut = in_channels != out_channels or stride != 1
self.shortcut = (
ResNetShortCut(in_channels, out_channels, stride=stride) if should_apply_shortcut else nn.Identity()
)
self.layer = nn.Sequential(
ResNetConvLayer(in_channels, out_channels, stride=stride),
ResNetConvLayer(out_channels, out_channels, activation=None),
)
self.activation = ACT2FN[activation]
def forward(self, hidden_state):
residual = hidden_state
hidden_state = self.layer(hidden_state)
residual = self.shortcut(residual)
hidden_state += residual
hidden_state = self.activation(hidden_state)
return hidden_state
class ResNetBottleNeckLayer(nn.Module):
"""
A classic ResNet's bottleneck layer composed by three `3x3` convolutions.
The first `1x1` convolution reduces the input by a factor of `reduction` in order to make the second `3x3`
convolution faster. The last `1x1` convolution remaps the reduced features to `out_channels`. If
`downsample_in_bottleneck` is true, downsample will be in the first layer instead of the second layer.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
stride: int = 1,
activation: str = "relu",
reduction: int = 4,
downsample_in_bottleneck: bool = False,
):
super().__init__()
should_apply_shortcut = in_channels != out_channels or stride != 1
reduces_channels = out_channels // reduction
self.shortcut = (
ResNetShortCut(in_channels, out_channels, stride=stride) if should_apply_shortcut else nn.Identity()
)
self.layer = nn.Sequential(
ResNetConvLayer(
in_channels, reduces_channels, kernel_size=1, stride=stride if downsample_in_bottleneck else 1
),
ResNetConvLayer(reduces_channels, reduces_channels, stride=stride if not downsample_in_bottleneck else 1),
ResNetConvLayer(reduces_channels, out_channels, kernel_size=1, activation=None),
)
self.activation = ACT2FN[activation]
def forward(self, hidden_state):
residual = hidden_state
hidden_state = self.layer(hidden_state)
residual = self.shortcut(residual)
hidden_state += residual
hidden_state = self.activation(hidden_state)
return hidden_state
class ResNetStage(nn.Module):
"""
A ResNet stage composed by stacked layers.
"""
def __init__(
self,
config: ResNetConfig,
in_channels: int,
out_channels: int,
stride: int = 2,
depth: int = 2,
):
super().__init__()
layer = ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer
if config.layer_type == "bottleneck":
first_layer = layer(
in_channels,
out_channels,
stride=stride,
activation=config.hidden_act,
downsample_in_bottleneck=config.downsample_in_bottleneck,
)
else:
first_layer = layer(in_channels, out_channels, stride=stride, activation=config.hidden_act)
self.layers = nn.Sequential(
first_layer, *[layer(out_channels, out_channels, activation=config.hidden_act) for _ in range(depth - 1)]
)
def forward(self, input: Tensor) -> Tensor:
hidden_state = input
for layer in self.layers:
hidden_state = layer(hidden_state)
return hidden_state
class ResNetEncoder(nn.Module):
def __init__(self, config: ResNetConfig):
super().__init__()
self.stages = nn.ModuleList([])
# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
self.stages.append(
ResNetStage(
config,
config.embedding_size,
config.hidden_sizes[0],
stride=2 if config.downsample_in_first_stage else 1,
depth=config.depths[0],
)
)
in_out_channels = zip(config.hidden_sizes, config.hidden_sizes[1:])
for (in_channels, out_channels), depth in zip(in_out_channels, config.depths[1:]):
self.stages.append(ResNetStage(config, in_channels, out_channels, depth=depth))
def forward(
self, hidden_state: Tensor, output_hidden_states: bool = False, return_dict: bool = True
) -> BaseModelOutputWithNoAttention:
hidden_states = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
hidden_states = hidden_states + (hidden_state,)
hidden_state = stage_module(hidden_state)
if output_hidden_states:
hidden_states = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None)
return BaseModelOutputWithNoAttention(
last_hidden_state=hidden_state,
hidden_states=hidden_states,
)
class ResNetPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ResNetConfig
base_model_prefix = "resnet"
main_input_name = "pixel_values"
def _init_weights(self, module):
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(module.weight, 1)
nn.init.constant_(module.bias, 0)
RESNET_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
RESNET_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare ResNet model outputting raw features without any specific head on top.",
RESNET_START_DOCSTRING,
)
class ResNetModel(ResNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embedder = ResNetEmbeddings(config)
self.encoder = ResNetEncoder(config)
self.pooler = nn.AdaptiveAvgPool2d((1, 1))
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndNoAttention,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self, pixel_values: Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None
) -> BaseModelOutputWithPoolingAndNoAttention:
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
embedding_output = self.embedder(pixel_values)
encoder_outputs = self.encoder(
embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict
)
last_hidden_state = encoder_outputs[0]
pooled_output = self.pooler(last_hidden_state)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
)
@add_start_docstrings(
"""
ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""",
RESNET_START_DOCSTRING,
)
class ResNetForImageClassification(ResNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.resnet = ResNetModel(config)
# classification head
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity(),
)
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> ImageClassifierOutputWithNoAttention:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.resnet(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
@add_start_docstrings(
"""
ResNet backbone, to be used with frameworks like DETR and MaskFormer.
""",
RESNET_START_DOCSTRING,
)
class ResNetBackbone(ResNetPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.num_features = [config.embedding_size] + config.hidden_sizes
self.embedder = ResNetEmbeddings(config)
self.encoder = ResNetEncoder(config)
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self, pixel_values: Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None
) -> BackboneOutput:
"""
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, AutoBackbone
>>> import torch
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
>>> model = AutoBackbone.from_pretrained(
... "microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]
... )
>>> inputs = processor(image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> feature_maps = outputs.feature_maps
>>> list(feature_maps[-1].shape)
[1, 2048, 7, 7]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
embedding_output = self.embedder(pixel_values)
outputs = self.encoder(embedding_output, output_hidden_states=True, return_dict=True)
hidden_states = outputs.hidden_states
feature_maps = ()
for idx, stage in enumerate(self.stage_names):
if stage in self.out_features:
feature_maps += (hidden_states[idx],)
if not return_dict:
output = (feature_maps,)
if output_hidden_states:
output += (outputs.hidden_states,)
return output
return BackboneOutput(
feature_maps=feature_maps,
hidden_states=outputs.hidden_states if output_hidden_states else None,
attentions=None,
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/resnet/__init__.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_import_structure = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_resnet"] = [
"RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"ResNetForImageClassification",
"ResNetModel",
"ResNetPreTrainedModel",
"ResNetBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_resnet"] = [
"TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFResNetForImageClassification",
"TFResNetModel",
"TFResNetPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_resnet"] = [
"FlaxResNetForImageClassification",
"FlaxResNetModel",
"FlaxResNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/resnet/configuration_resnet.py | # coding=utf-8
# Copyright 2022 Microsoft Research, Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" ResNet model configuration"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
logger = logging.get_logger(__name__)
RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}
class ResNetConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ResNetModel`]. It is used to instantiate an
ResNet model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the ResNet
[microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
embedding_size (`int`, *optional*, defaults to 64):
Dimensionality (hidden size) for the embedding layer.
hidden_sizes (`List[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`):
Dimensionality (hidden size) at each stage.
depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 3]`):
Depth (number of layers) for each stage.
layer_type (`str`, *optional*, defaults to `"bottleneck"`):
The layer to use, it can be either `"basic"` (used for smaller models, like resnet-18 or resnet-34) or
`"bottleneck"` (used for larger models like resnet-50 and above).
hidden_act (`str`, *optional*, defaults to `"relu"`):
The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"`
are supported.
downsample_in_first_stage (`bool`, *optional*, defaults to `False`):
If `True`, the first stage will downsample the inputs using a `stride` of 2.
downsample_in_bottleneck (`bool`, *optional*, defaults to `False`):
If `True`, the first conv 1x1 in ResNetBottleNeckLayer will downsample the inputs using a `stride` of 2.
out_features (`List[str]`, *optional*):
If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
(depending on how many stages the model has). If unset and `out_indices` is set, will default to the
corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute.
out_indices (`List[int]`, *optional*):
If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
If unset and `out_features` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute.
Example:
```python
>>> from transformers import ResNetConfig, ResNetModel
>>> # Initializing a ResNet resnet-50 style configuration
>>> configuration = ResNetConfig()
>>> # Initializing a model (with random weights) from the resnet-50 style configuration
>>> model = ResNetModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "resnet"
layer_types = ["basic", "bottleneck"]
def __init__(
self,
num_channels=3,
embedding_size=64,
hidden_sizes=[256, 512, 1024, 2048],
depths=[3, 4, 6, 3],
layer_type="bottleneck",
hidden_act="relu",
downsample_in_first_stage=False,
downsample_in_bottleneck=False,
out_features=None,
out_indices=None,
**kwargs,
):
super().__init__(**kwargs)
if layer_type not in self.layer_types:
raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types)}")
self.num_channels = num_channels
self.embedding_size = embedding_size
self.hidden_sizes = hidden_sizes
self.depths = depths
self.layer_type = layer_type
self.hidden_act = hidden_act
self.downsample_in_first_stage = downsample_in_first_stage
self.downsample_in_bottleneck = downsample_in_bottleneck
self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
)
class ResNetOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
]
)
@property
def atol_for_validation(self) -> float:
return 1e-3
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/markuplm/modeling_markuplm.py | # coding=utf-8
# Copyright 2022 Microsoft Research Asia and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch MarkupLM model."""
import math
import os
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...file_utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
MaskedLMOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import (
PreTrainedModel,
apply_chunking_to_forward,
find_pruneable_heads_and_indices,
prune_linear_layer,
)
from ...utils import logging
from .configuration_markuplm import MarkupLMConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "microsoft/markuplm-base"
_CONFIG_FOR_DOC = "MarkupLMConfig"
MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST = [
"microsoft/markuplm-base",
"microsoft/markuplm-large",
]
class XPathEmbeddings(nn.Module):
"""Construct the embeddings from xpath tags and subscripts.
We drop tree-id in this version, as its info can be covered by xpath.
"""
def __init__(self, config):
super(XPathEmbeddings, self).__init__()
self.max_depth = config.max_depth
self.xpath_unitseq2_embeddings = nn.Linear(config.xpath_unit_hidden_size * self.max_depth, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.activation = nn.ReLU()
self.xpath_unitseq2_inner = nn.Linear(config.xpath_unit_hidden_size * self.max_depth, 4 * config.hidden_size)
self.inner2emb = nn.Linear(4 * config.hidden_size, config.hidden_size)
self.xpath_tag_sub_embeddings = nn.ModuleList(
[
nn.Embedding(config.max_xpath_tag_unit_embeddings, config.xpath_unit_hidden_size)
for _ in range(self.max_depth)
]
)
self.xpath_subs_sub_embeddings = nn.ModuleList(
[
nn.Embedding(config.max_xpath_subs_unit_embeddings, config.xpath_unit_hidden_size)
for _ in range(self.max_depth)
]
)
def forward(self, xpath_tags_seq=None, xpath_subs_seq=None):
xpath_tags_embeddings = []
xpath_subs_embeddings = []
for i in range(self.max_depth):
xpath_tags_embeddings.append(self.xpath_tag_sub_embeddings[i](xpath_tags_seq[:, :, i]))
xpath_subs_embeddings.append(self.xpath_subs_sub_embeddings[i](xpath_subs_seq[:, :, i]))
xpath_tags_embeddings = torch.cat(xpath_tags_embeddings, dim=-1)
xpath_subs_embeddings = torch.cat(xpath_subs_embeddings, dim=-1)
xpath_embeddings = xpath_tags_embeddings + xpath_subs_embeddings
xpath_embeddings = self.inner2emb(self.dropout(self.activation(self.xpath_unitseq2_inner(xpath_embeddings))))
return xpath_embeddings
# Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`.
Args:
x: torch.Tensor x:
Returns: torch.Tensor
"""
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
mask = input_ids.ne(padding_idx).int()
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
return incremental_indices.long() + padding_idx
class MarkupLMEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super(MarkupLMEmbeddings, self).__init__()
self.config = config
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.max_depth = config.max_depth
self.xpath_embeddings = XPathEmbeddings(config)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.padding_idx = config.pad_token_id
self.position_embeddings = nn.Embedding(
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
)
# Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings.create_position_ids_from_inputs_embeds
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
"""
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
"""
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
)
return position_ids.unsqueeze(0).expand(input_shape)
def forward(
self,
input_ids=None,
xpath_tags_seq=None,
xpath_subs_seq=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
past_key_values_length=0,
):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
device = input_ids.device if input_ids is not None else inputs_embeds.device
if position_ids is None:
if input_ids is not None:
# Create the position ids from the input token ids. Any padded tokens remain padded.
position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
else:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
# prepare xpath seq
if xpath_tags_seq is None:
xpath_tags_seq = self.config.tag_pad_id * torch.ones(
tuple(list(input_shape) + [self.max_depth]), dtype=torch.long, device=device
)
if xpath_subs_seq is None:
xpath_subs_seq = self.config.subs_pad_id * torch.ones(
tuple(list(input_shape) + [self.max_depth]), dtype=torch.long, device=device
)
words_embeddings = inputs_embeds
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
xpath_embeddings = self.xpath_embeddings(xpath_tags_seq, xpath_subs_seq)
embeddings = words_embeddings + position_embeddings + token_type_embeddings + xpath_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->MarkupLM
class MarkupLMSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
class MarkupLMIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->MarkupLM
class MarkupLMOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertPooler
class MarkupLMPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->MarkupLM
class MarkupLMPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->MarkupLM
class MarkupLMLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = MarkupLMPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->MarkupLM
class MarkupLMOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = MarkupLMLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores
# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->MarkupLM
class MarkupLMSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(
config, "position_embedding_type", "absolute"
)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
use_cache = past_key_value is not None
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
query_length, key_length = query_layer.shape[2], key_layer.shape[2]
if use_cache:
position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
-1, 1
)
else:
position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in MarkupLMModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->MarkupLM
class MarkupLMAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = MarkupLMSelfAttention(config, position_embedding_type=position_embedding_type)
self.output = MarkupLMSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->MarkupLM
class MarkupLMLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = MarkupLMAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
self.crossattention = MarkupLMAttention(config, position_embedding_type="absolute")
self.intermediate = MarkupLMIntermediate(config)
self.output = MarkupLMOutput(config)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and encoder_hidden_states is not None:
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
" by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
cross_attn_past_key_value,
output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->MarkupLM
class MarkupLMEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([MarkupLMLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_values[i] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
class MarkupLMPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MarkupLMConfig
pretrained_model_archive_map = MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST
base_model_prefix = "markuplm"
# Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights with Bert->MarkupLM
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
return super(MarkupLMPreTrainedModel, cls).from_pretrained(
pretrained_model_name_or_path, *model_args, **kwargs
)
MARKUPLM_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MarkupLMConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
MARKUPLM_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
xpath_tags_seq (`torch.LongTensor` of shape `({0}, config.max_depth)`, *optional*):
Tag IDs for each token in the input sequence, padded up to config.max_depth.
xpath_subs_seq (`torch.LongTensor` of shape `({0}, config.max_depth)`, *optional*):
Subscript IDs for each token in the input sequence, padded up to config.max_depth.
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: `1` for
tokens that are NOT MASKED, `0` for MASKED tokens.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`: `0` corresponds to a *sentence A* token, `1` corresponds to a *sentence B* token
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: `1`
indicates the head is **not masked**, `0` indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
If set to `True`, the attentions tensors of all attention layers are returned. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
If set to `True`, the hidden states of all layers are returned. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
If set to `True`, the model will return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare MarkupLM Model transformer outputting raw hidden-states without any specific head on top.",
MARKUPLM_START_DOCSTRING,
)
class MarkupLMModel(MarkupLMPreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->MarkupLM
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = MarkupLMEmbeddings(config)
self.encoder = MarkupLMEncoder(config)
self.pooler = MarkupLMPooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(MARKUPLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
xpath_tags_seq: Optional[torch.LongTensor] = None,
xpath_subs_seq: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPoolingAndCrossAttentions]:
r"""
Returns:
Examples:
```python
>>> from transformers import AutoProcessor, MarkupLMModel
>>> processor = AutoProcessor.from_pretrained("microsoft/markuplm-base")
>>> model = MarkupLMModel.from_pretrained("microsoft/markuplm-base")
>>> html_string = "<html> <head> <title>Page Title</title> </head> </html>"
>>> encoding = processor(html_string, return_tensors="pt")
>>> outputs = model(**encoding)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 4, 768]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
elif head_mask.dim() == 2:
head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
head_mask = head_mask.to(dtype=next(self.parameters()).dtype)
else:
head_mask = [None] * self.config.num_hidden_layers
embedding_output = self.embeddings(
input_ids=input_ids,
xpath_tags_seq=xpath_tags_seq,
xpath_subs_seq=xpath_subs_seq,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
encoder_outputs = self.encoder(
embedding_output,
extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertModel.prepare_inputs_for_generation
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, use_cache=True, **model_kwargs
):
input_shape = input_ids.shape
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_shape)
# cut decoder_input_ids if past_key_values is used
if past_key_values is not None:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_length:]
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"past_key_values": past_key_values,
"use_cache": use_cache,
}
# Copied from transformers.models.bert.modeling_bert.BertModel._reorder_cache
def _reorder_cache(self, past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
@add_start_docstrings(
"""
MarkupLM Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
MARKUPLM_START_DOCSTRING,
)
class MarkupLMForQuestionAnswering(MarkupLMPreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering.__init__ with bert->markuplm, Bert->MarkupLM
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.markuplm = MarkupLMModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MARKUPLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
xpath_tags_seq: Optional[torch.Tensor] = None,
xpath_subs_seq: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
start_positions: Optional[torch.Tensor] = None,
end_positions: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
Returns:
Examples:
```python
>>> from transformers import AutoProcessor, MarkupLMForQuestionAnswering
>>> import torch
>>> processor = AutoProcessor.from_pretrained("microsoft/markuplm-base-finetuned-websrc")
>>> model = MarkupLMForQuestionAnswering.from_pretrained("microsoft/markuplm-base-finetuned-websrc")
>>> html_string = "<html> <head> <title>My name is Niels</title> </head> </html>"
>>> question = "What's his name?"
>>> encoding = processor(html_string, questions=question, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**encoding)
>>> answer_start_index = outputs.start_logits.argmax()
>>> answer_end_index = outputs.end_logits.argmax()
>>> predict_answer_tokens = encoding.input_ids[0, answer_start_index : answer_end_index + 1]
>>> processor.decode(predict_answer_tokens).strip()
'Niels'
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.markuplm(
input_ids,
xpath_tags_seq=xpath_tags_seq,
xpath_subs_seq=xpath_subs_seq,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions.clamp_(0, ignored_index)
end_positions.clamp_(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings("""MarkupLM Model with a `token_classification` head on top.""", MARKUPLM_START_DOCSTRING)
class MarkupLMForTokenClassification(MarkupLMPreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForTokenClassification.__init__ with bert->markuplm, Bert->MarkupLM
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.markuplm = MarkupLMModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MARKUPLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
xpath_tags_seq: Optional[torch.Tensor] = None,
xpath_subs_seq: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
Returns:
Examples:
```python
>>> from transformers import AutoProcessor, AutoModelForTokenClassification
>>> import torch
>>> processor = AutoProcessor.from_pretrained("microsoft/markuplm-base")
>>> processor.parse_html = False
>>> model = AutoModelForTokenClassification.from_pretrained("microsoft/markuplm-base", num_labels=7)
>>> nodes = ["hello", "world"]
>>> xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"]
>>> node_labels = [1, 2]
>>> encoding = processor(nodes=nodes, xpaths=xpaths, node_labels=node_labels, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**encoding)
>>> loss = outputs.loss
>>> logits = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.markuplm(
input_ids,
xpath_tags_seq=xpath_tags_seq,
xpath_subs_seq=xpath_subs_seq,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.classifier(sequence_output) # (batch_size, seq_length, node_type_size)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(
prediction_scores.view(-1, self.config.num_labels),
labels.view(-1),
)
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
MarkupLM Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
""",
MARKUPLM_START_DOCSTRING,
)
class MarkupLMForSequenceClassification(MarkupLMPreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification.__init__ with bert->markuplm, Bert->MarkupLM
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.markuplm = MarkupLMModel(config)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MARKUPLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
xpath_tags_seq: Optional[torch.Tensor] = None,
xpath_subs_seq: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
Returns:
Examples:
```python
>>> from transformers import AutoProcessor, AutoModelForSequenceClassification
>>> import torch
>>> processor = AutoProcessor.from_pretrained("microsoft/markuplm-base")
>>> model = AutoModelForSequenceClassification.from_pretrained("microsoft/markuplm-base", num_labels=7)
>>> html_string = "<html> <head> <title>Page Title</title> </head> </html>"
>>> encoding = processor(html_string, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**encoding)
>>> loss = outputs.loss
>>> logits = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.markuplm(
input_ids,
xpath_tags_seq=xpath_tags_seq,
xpath_subs_seq=xpath_subs_seq,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/markuplm/feature_extraction_markuplm.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Feature extractor class for MarkupLM.
"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bs4_available, logging, requires_backends
if is_bs4_available():
import bs4
from bs4 import BeautifulSoup
logger = logging.get_logger(__name__)
class MarkupLMFeatureExtractor(FeatureExtractionMixin):
r"""
Constructs a MarkupLM feature extractor. This can be used to get a list of nodes and corresponding xpaths from HTML
strings.
This feature extractor inherits from [`~feature_extraction_utils.PreTrainedFeatureExtractor`] which contains most
of the main methods. Users should refer to this superclass for more information regarding those methods.
"""
def __init__(self, **kwargs):
requires_backends(self, ["bs4"])
super().__init__(**kwargs)
def xpath_soup(self, element):
xpath_tags = []
xpath_subscripts = []
child = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
siblings = parent.find_all(child.name, recursive=False)
xpath_tags.append(child.name)
xpath_subscripts.append(
0 if 1 == len(siblings) else next(i for i, s in enumerate(siblings, 1) if s is child)
)
child = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def get_three_from_single(self, html_string):
html_code = BeautifulSoup(html_string, "html.parser")
all_doc_strings = []
string2xtag_seq = []
string2xsubs_seq = []
for element in html_code.descendants:
if isinstance(element, bs4.element.NavigableString):
if type(element.parent) != bs4.element.Tag:
continue
text_in_this_tag = html.unescape(element).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(text_in_this_tag)
xpath_tags, xpath_subscripts = self.xpath_soup(element)
string2xtag_seq.append(xpath_tags)
string2xsubs_seq.append(xpath_subscripts)
if len(all_doc_strings) != len(string2xtag_seq):
raise ValueError("Number of doc strings and xtags does not correspond")
if len(all_doc_strings) != len(string2xsubs_seq):
raise ValueError("Number of doc strings and xsubs does not correspond")
return all_doc_strings, string2xtag_seq, string2xsubs_seq
def construct_xpath(self, xpath_tags, xpath_subscripts):
xpath = ""
for tagname, subs in zip(xpath_tags, xpath_subscripts):
xpath += f"/{tagname}"
if subs != 0:
xpath += f"[{subs}]"
return xpath
def __call__(self, html_strings) -> BatchFeature:
"""
Main method to prepare for the model one or several HTML strings.
Args:
html_strings (`str`, `List[str]`):
The HTML string or batch of HTML strings from which to extract nodes and corresponding xpaths.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **nodes** -- Nodes.
- **xpaths** -- Corresponding xpaths.
Examples:
```python
>>> from transformers import MarkupLMFeatureExtractor
>>> page_name_1 = "page1.html"
>>> page_name_2 = "page2.html"
>>> page_name_3 = "page3.html"
>>> with open(page_name_1) as f:
... single_html_string = f.read()
>>> feature_extractor = MarkupLMFeatureExtractor()
>>> # single example
>>> encoding = feature_extractor(single_html_string)
>>> print(encoding.keys())
>>> # dict_keys(['nodes', 'xpaths'])
>>> # batched example
>>> multi_html_strings = []
>>> with open(page_name_2) as f:
... multi_html_strings.append(f.read())
>>> with open(page_name_3) as f:
... multi_html_strings.append(f.read())
>>> encoding = feature_extractor(multi_html_strings)
>>> print(encoding.keys())
>>> # dict_keys(['nodes', 'xpaths'])
```"""
# Input type checking for clearer error
valid_strings = False
# Check that strings has a valid type
if isinstance(html_strings, str):
valid_strings = True
elif isinstance(html_strings, (list, tuple)):
if len(html_strings) == 0 or isinstance(html_strings[0], str):
valid_strings = True
if not valid_strings:
raise ValueError(
"HTML strings must of type `str`, `List[str]` (batch of examples), "
f"but is of type {type(html_strings)}."
)
is_batched = bool(isinstance(html_strings, (list, tuple)) and (isinstance(html_strings[0], str)))
if not is_batched:
html_strings = [html_strings]
# Get nodes + xpaths
nodes = []
xpaths = []
for html_string in html_strings:
all_doc_strings, string2xtag_seq, string2xsubs_seq = self.get_three_from_single(html_string)
nodes.append(all_doc_strings)
xpath_strings = []
for node, tag_list, sub_list in zip(all_doc_strings, string2xtag_seq, string2xsubs_seq):
xpath_string = self.construct_xpath(tag_list, sub_list)
xpath_strings.append(xpath_string)
xpaths.append(xpath_strings)
# return as Dict
data = {"nodes": nodes, "xpaths": xpaths}
encoded_inputs = BatchFeature(data=data, tensor_type=None)
return encoded_inputs
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/markuplm/__init__.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_import_structure = {
"configuration_markuplm": ["MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarkupLMConfig"],
"feature_extraction_markuplm": ["MarkupLMFeatureExtractor"],
"processing_markuplm": ["MarkupLMProcessor"],
"tokenization_markuplm": ["MarkupLMTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_markuplm_fast"] = ["MarkupLMTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_markuplm"] = [
"MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"MarkupLMForQuestionAnswering",
"MarkupLMForSequenceClassification",
"MarkupLMForTokenClassification",
"MarkupLMModel",
"MarkupLMPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
from .feature_extraction_markuplm import MarkupLMFeatureExtractor
from .processing_markuplm import MarkupLMProcessor
from .tokenization_markuplm import MarkupLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_markuplm_fast import MarkupLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_markuplm import (
MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
MarkupLMForQuestionAnswering,
MarkupLMForSequenceClassification,
MarkupLMForTokenClassification,
MarkupLMModel,
MarkupLMPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/markuplm/processing_markuplm.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for MarkupLM.
"""
from typing import Optional, Union
from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class MarkupLMProcessor(ProcessorMixin):
r"""
Constructs a MarkupLM processor which combines a MarkupLM feature extractor and a MarkupLM tokenizer into a single
processor.
[`MarkupLMProcessor`] offers all the functionalities you need to prepare data for the model.
It first uses [`MarkupLMFeatureExtractor`] to extract nodes and corresponding xpaths from one or more HTML strings.
Next, these are provided to [`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`], which turns them into token-level
`input_ids`, `attention_mask`, `token_type_ids`, `xpath_tags_seq` and `xpath_subs_seq`.
Args:
feature_extractor (`MarkupLMFeatureExtractor`):
An instance of [`MarkupLMFeatureExtractor`]. The feature extractor is a required input.
tokenizer (`MarkupLMTokenizer` or `MarkupLMTokenizerFast`):
An instance of [`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`]. The tokenizer is a required input.
parse_html (`bool`, *optional*, defaults to `True`):
Whether or not to use `MarkupLMFeatureExtractor` to parse HTML strings into nodes and corresponding xpaths.
"""
feature_extractor_class = "MarkupLMFeatureExtractor"
tokenizer_class = ("MarkupLMTokenizer", "MarkupLMTokenizerFast")
parse_html = True
def __call__(
self,
html_strings=None,
nodes=None,
xpaths=None,
node_labels=None,
questions=None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs,
) -> BatchEncoding:
"""
This method first forwards the `html_strings` argument to [`~MarkupLMFeatureExtractor.__call__`]. Next, it
passes the `nodes` and `xpaths` along with the additional arguments to [`~MarkupLMTokenizer.__call__`] and
returns the output.
Optionally, one can also provide a `text` argument which is passed along as first sequence.
Please refer to the docstring of the above two methods for more information.
"""
# first, create nodes and xpaths
if self.parse_html:
if html_strings is None:
raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`")
if nodes is not None or xpaths is not None or node_labels is not None:
raise ValueError(
"Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"
)
features = self.feature_extractor(html_strings)
nodes = features["nodes"]
xpaths = features["xpaths"]
else:
if html_strings is not None:
raise ValueError("You have passed HTML strings but `parse_html` is set to `False`.")
if nodes is None or xpaths is None:
raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`")
# # second, apply the tokenizer
if questions is not None and self.parse_html:
if isinstance(questions, str):
questions = [questions] # add batch dimension (as the feature extractor always adds a batch dimension)
encoded_inputs = self.tokenizer(
text=questions if questions is not None else nodes,
text_pair=nodes if questions is not None else None,
xpaths=xpaths,
node_labels=node_labels,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
return_tensors=return_tensors,
**kwargs,
)
return encoded_inputs
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to TrOCRTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer
to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to TrOCRTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the
docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
return tokenizer_input_names
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/markuplm/configuration_markuplm.py | # coding=utf-8
# Copyright 2021, The Microsoft Research Asia MarkupLM Team authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" MarkupLM model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}
class MarkupLMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MarkupLMModel`]. It is used to instantiate a
MarkupLM model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the MarkupLM
[microsoft/markuplm-base](https://huggingface.co/microsoft/markuplm-base) architecture.
Configuration objects inherit from [`BertConfig`] and can be used to control the model outputs. Read the
documentation from [`BertConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the MarkupLM model. Defines the different tokens that can be represented by the
*inputs_ids* passed to the forward method of [`MarkupLMModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed into [`MarkupLMModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
max_tree_id_unit_embeddings (`int`, *optional*, defaults to 1024):
The maximum value that the tree id unit embedding might ever use. Typically set this to something large
just in case (e.g., 1024).
max_xpath_tag_unit_embeddings (`int`, *optional*, defaults to 256):
The maximum value that the xpath tag unit embedding might ever use. Typically set this to something large
just in case (e.g., 256).
max_xpath_subs_unit_embeddings (`int`, *optional*, defaults to 1024):
The maximum value that the xpath subscript unit embedding might ever use. Typically set this to something
large just in case (e.g., 1024).
tag_pad_id (`int`, *optional*, defaults to 216):
The id of the padding token in the xpath tags.
subs_pad_id (`int`, *optional*, defaults to 1001):
The id of the padding token in the xpath subscripts.
xpath_tag_unit_hidden_size (`int`, *optional*, defaults to 32):
The hidden size of each tree id unit. One complete tree index will have
(50*xpath_tag_unit_hidden_size)-dim.
max_depth (`int`, *optional*, defaults to 50):
The maximum depth in xpath.
Examples:
```python
>>> from transformers import MarkupLMModel, MarkupLMConfig
>>> # Initializing a MarkupLM microsoft/markuplm-base style configuration
>>> configuration = MarkupLMConfig()
>>> # Initializing a model from the microsoft/markuplm-base style configuration
>>> model = MarkupLMModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "markuplm"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
bos_token_id=0,
eos_token_id=2,
max_xpath_tag_unit_embeddings=256,
max_xpath_subs_unit_embeddings=1024,
tag_pad_id=216,
subs_pad_id=1001,
xpath_unit_hidden_size=32,
max_depth=50,
position_embedding_type="absolute",
use_cache=True,
classifier_dropout=None,
**kwargs,
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.use_cache = use_cache
self.classifier_dropout = classifier_dropout
# additional properties
self.max_depth = max_depth
self.max_xpath_tag_unit_embeddings = max_xpath_tag_unit_embeddings
self.max_xpath_subs_unit_embeddings = max_xpath_subs_unit_embeddings
self.tag_pad_id = tag_pad_id
self.subs_pad_id = subs_pad_id
self.xpath_unit_hidden_size = xpath_unit_hidden_size
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/markuplm/tokenization_markuplm.py | # coding=utf-8
# Copyright Microsoft Research and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization class for MarkupLM."""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import (
ENCODE_KWARGS_DOCSTRING,
BatchEncoding,
EncodedInput,
PreTokenizedInput,
TextInput,
TextInputPair,
TruncationStrategy,
)
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/vocab.json",
"microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/vocab.json",
},
"merges_file": {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/merges.txt",
"microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/merges.txt",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"microsoft/markuplm-base": 512,
"microsoft/markuplm-large": 512,
}
MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
add_special_tokens (`bool`, *optional*, defaults to `True`):
Whether or not to encode the sequences with the special tokens relative to their model.
padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`):
Activates and controls padding. Accepts the following values:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
Activates and controls truncation. Accepts the following values:
- `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.
- `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
max_length (`int`, *optional*):
Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to
`None`, this will use the predefined model maximum length if a maximum length is required by one of the
truncation/padding parameters. If the model has no specific maximum input length (like XLNet)
truncation/padding to a maximum length will be deactivated.
stride (`int`, *optional*, defaults to 0):
If set to a number along with `max_length`, the overflowing tokens returned when
`return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.
pad_to_multiple_of (`int`, *optional*):
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
"""
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large #
of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset
you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe
vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
"""
bs = (
list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
)
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8 + n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
def get_pairs(word):
"""
Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length
strings).
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
class MarkupLMTokenizer(PreTrainedTokenizer):
r"""
Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE). [`MarkupLMTokenizer`] can be used to
turn HTML strings into to token-level `input_ids`, `attention_mask`, `token_type_ids`, `xpath_tags_seq` and
`xpath_tags_seq`. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods.
Users should refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(
self,
vocab_file,
merges_file,
tags_dict,
errors="replace",
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
add_prefix_space=False,
max_depth=50,
max_width=1000,
pad_width=1001,
pad_token_label=-100,
only_label_first_subword=True,
**kwargs,
):
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)
self.tags_dict = tags_dict
self.decoder = {v: k for k, v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
with open(merges_file, encoding="utf-8") as merges_handle:
bpe_merges = merges_handle.read().split("\n")[1:-1]
bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.cache = {}
self.add_prefix_space = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
# additional properties
self.max_depth = max_depth
self.max_width = max_width
self.pad_width = pad_width
self.unk_tag_id = len(self.tags_dict)
self.pad_tag_id = self.unk_tag_id + 1
self.pad_xpath_tags_seq = [self.pad_tag_id] * self.max_depth
self.pad_xpath_subs_seq = [self.pad_width] * self.max_depth
super().__init__(
vocab_file=vocab_file,
merges_file=merges_file,
tags_dict=tags_dict,
errors=errors,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_token=sep_token,
cls_token=cls_token,
pad_token=pad_token,
mask_token=mask_token,
add_prefix_space=add_prefix_space,
max_depth=max_depth,
max_width=max_width,
pad_width=pad_width,
pad_token_label=pad_token_label,
only_label_first_subword=only_label_first_subword,
**kwargs,
)
self.pad_token_label = pad_token_label
self.only_label_first_subword = only_label_first_subword
def get_xpath_seq(self, xpath):
"""
Given the xpath expression of one particular node (like "/html/body/div/li[1]/div/span[2]"), return a list of
tag IDs and corresponding subscripts, taking into account max depth.
"""
xpath_tags_list = []
xpath_subs_list = []
xpath_units = xpath.split("/")
for unit in xpath_units:
if not unit.strip():
continue
name_subs = unit.strip().split("[")
tag_name = name_subs[0]
sub = 0 if len(name_subs) == 1 else int(name_subs[1][:-1])
xpath_tags_list.append(self.tags_dict.get(tag_name, self.unk_tag_id))
xpath_subs_list.append(min(self.max_width, sub))
xpath_tags_list = xpath_tags_list[: self.max_depth]
xpath_subs_list = xpath_subs_list[: self.max_depth]
xpath_tags_list += [self.pad_tag_id] * (self.max_depth - len(xpath_tags_list))
xpath_subs_list += [self.pad_width] * (self.max_depth - len(xpath_subs_list))
return xpath_tags_list, xpath_subs_list
@property
def vocab_size(self):
return len(self.encoder)
def get_vocab(self):
vocab = self.encoder.copy()
vocab.update(self.added_tokens_encoder)
return vocab
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token)
pairs = get_pairs(word)
if not pairs:
return token
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
except ValueError:
new_word.extend(word[i:])
break
else:
new_word.extend(word[i:j])
i = j
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = " ".join(word)
self.cache[token] = word
return word
def _tokenize(self, text):
"""Tokenize a string."""
bpe_tokens = []
for token in re.findall(self.pat, text):
token = "".join(
self.byte_encoder[b] for b in token.encode("utf-8")
) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
return bpe_tokens
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decoder.get(index)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
logger.warning(
"MarkupLM now does not support generative tasks, decoding is experimental and subject to change."
)
text = "".join(tokens)
text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
return text
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
merge_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
)
# save vocab_file
with open(vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
# save merge_file
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write("#version: 0.2\n")
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!"
)
index = token_index
writer.write(" ".join(bpe_tokens) + "\n")
index += 1
return vocab_file, merge_file
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
text = " " + text
return (text, kwargs)
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A RoBERTa sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + token_ids_1 + sep
def build_xpath_tags_with_special_tokens(
self, xpath_tags_0: List[int], xpath_tags_1: Optional[List[int]] = None
) -> List[int]:
pad = [self.pad_xpath_tags_seq]
if len(xpath_tags_1) == 0:
return pad + xpath_tags_0 + pad
return pad + xpath_tags_0 + pad + xpath_tags_1 + pad
def build_xpath_subs_with_special_tokens(
self, xpath_subs_0: List[int], xpath_subs_1: Optional[List[int]] = None
) -> List[int]:
pad = [self.pad_xpath_subs_seq]
if len(xpath_subs_1) == 0:
return pad + xpath_subs_0 + pad
return pad + xpath_subs_0 + pad + xpath_subs_1 + pad
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Args:
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + token_ids_1 + sep) * [0]
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
xpaths: Union[List[List[int]], List[List[List[int]]]] = None,
node_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences with node-level xpaths and optional labels.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
(nodes of a single example or questions of a batch of examples) or a list of list of strings (batch of
nodes).
text_pair (`List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
(pretokenized string).
xpaths (`List[List[int]]`, `List[List[List[int]]]`):
Node-level xpaths.
node_labels (`List[int]`, `List[List[int]]`, *optional*):
Node-level integer labels (for token classification tasks).
"""
# Input type checking for clearer error
def _is_valid_text_input(t):
if isinstance(t, str):
# Strings are fine
return True
elif isinstance(t, (list, tuple)):
# List are fine as long as they are...
if len(t) == 0:
# ... empty
return True
elif isinstance(t[0], str):
# ... list of strings
return True
elif isinstance(t[0], (list, tuple)):
# ... list with an empty list or with a list of strings
return len(t[0]) == 0 or isinstance(t[0][0], str)
else:
return False
else:
return False
if text_pair is not None:
# in case text + text_pair are provided, text = questions, text_pair = nodes
if not _is_valid_text_input(text):
raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
if not isinstance(text_pair, (list, tuple)):
raise ValueError(
"Nodes must be of type `List[str]` (single pretokenized example), "
"or `List[List[str]]` (batch of pretokenized examples)."
)
else:
# in case only text is provided => must be nodes
if not isinstance(text, (list, tuple)):
raise ValueError(
"Nodes must be of type `List[str]` (single pretokenized example), "
"or `List[List[str]]` (batch of pretokenized examples)."
)
if text_pair is not None:
is_batched = isinstance(text, (list, tuple))
else:
is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
nodes = text if text_pair is None else text_pair
assert xpaths is not None, "You must provide corresponding xpaths"
if is_batched:
assert len(nodes) == len(xpaths), "You must provide nodes and xpaths for an equal amount of examples"
for nodes_example, xpaths_example in zip(nodes, xpaths):
assert len(nodes_example) == len(xpaths_example), "You must provide as many nodes as there are xpaths"
else:
assert len(nodes) == len(xpaths), "You must provide as many nodes as there are xpaths"
if is_batched:
if text_pair is not None and len(text) != len(text_pair):
raise ValueError(
f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
f" {len(text_pair)}."
)
batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
is_pair = bool(text_pair is not None)
return self.batch_encode_plus(
batch_text_or_text_pairs=batch_text_or_text_pairs,
is_pair=is_pair,
xpaths=xpaths,
node_labels=node_labels,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
else:
return self.encode_plus(
text=text,
text_pair=text_pair,
xpaths=xpaths,
node_labels=node_labels,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
],
is_pair: bool = None,
xpaths: Optional[List[List[List[int]]]] = None,
node_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
verbose=verbose,
**kwargs,
)
return self._batch_encode_plus(
batch_text_or_text_pairs=batch_text_or_text_pairs,
is_pair=is_pair,
xpaths=xpaths,
node_labels=node_labels,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
def _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
],
is_pair: bool = None,
xpaths: Optional[List[List[List[int]]]] = None,
node_labels: Optional[List[List[int]]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast."
)
batch_outputs = self._batch_prepare_for_model(
batch_text_or_text_pairs=batch_text_or_text_pairs,
is_pair=is_pair,
xpaths=xpaths,
node_labels=node_labels,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
return_tensors=return_tensors,
verbose=verbose,
)
return BatchEncoding(batch_outputs)
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def _batch_prepare_for_model(
self,
batch_text_or_text_pairs,
is_pair: bool = None,
xpaths: Optional[List[List[int]]] = None,
node_labels: Optional[List[List[int]]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[str] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_length: bool = False,
verbose: bool = True,
) -> BatchEncoding:
"""
Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
manages a moving window (with user defined stride) for overflowing tokens.
Args:
batch_ids_pairs: list of tokenized input ids or input ids pairs
"""
batch_outputs = {}
for idx, example in enumerate(zip(batch_text_or_text_pairs, xpaths)):
batch_text_or_text_pair, xpaths_example = example
outputs = self.prepare_for_model(
batch_text_or_text_pair[0] if is_pair else batch_text_or_text_pair,
batch_text_or_text_pair[1] if is_pair else None,
xpaths_example,
node_labels=node_labels[idx] if node_labels is not None else None,
add_special_tokens=add_special_tokens,
padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward
truncation=truncation_strategy.value,
max_length=max_length,
stride=stride,
pad_to_multiple_of=None, # we pad in batch afterward
return_attention_mask=False, # we pad in batch afterward
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
return_tensors=None, # We convert the whole batch to tensors at the end
prepend_batch_axis=False,
verbose=verbose,
)
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
batch_outputs = self.pad(
batch_outputs,
padding=padding_strategy.value,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
return batch_outputs
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING)
def encode(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
xpaths: Optional[List[List[int]]] = None,
node_labels: Optional[List[int]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> List[int]:
encoded_inputs = self.encode_plus(
text=text,
text_pair=text_pair,
xpaths=xpaths,
node_labels=node_labels,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
return encoded_inputs["input_ids"]
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def encode_plus(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
xpaths: Optional[List[List[int]]] = None,
node_labels: Optional[List[int]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
`__call__` should be used instead.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
text_pair (`List[str]` or `List[int]`, *optional*):
Optional second sequence to be encoded. This can be a list of strings (nodes of a single example) or a
list of list of strings (nodes of a batch of examples).
"""
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
verbose=verbose,
**kwargs,
)
return self._encode_plus(
text=text,
xpaths=xpaths,
text_pair=text_pair,
node_labels=node_labels,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
def _encode_plus(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
xpaths: Optional[List[List[int]]] = None,
node_labels: Optional[List[int]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast. "
"More information on available tokenizers at "
"https://github.com/huggingface/transformers/pull/2674"
)
return self.prepare_for_model(
text=text,
text_pair=text_pair,
xpaths=xpaths,
node_labels=node_labels,
add_special_tokens=add_special_tokens,
padding=padding_strategy.value,
truncation=truncation_strategy.value,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
prepend_batch_axis=True,
return_attention_mask=return_attention_mask,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
verbose=verbose,
)
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def prepare_for_model(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
xpaths: Optional[List[List[int]]] = None,
node_labels: Optional[List[int]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
prepend_batch_axis: bool = False,
**kwargs,
) -> BatchEncoding:
"""
Prepares a sequence or a pair of sequences so that it can be used by the model. It adds special tokens,
truncates sequences if overflowing while taking into account the special tokens and manages a moving window
(with user defined stride) for overflowing tokens. Please Note, for *text_pair* different than `None` and
*truncation_strategy = longest_first* or `True`, it is not possible to return overflowing tokens. Such a
combination of arguments will raise an error.
Node-level `xpaths` are turned into token-level `xpath_tags_seq` and `xpath_subs_seq`. If provided, node-level
`node_labels` are turned into token-level `labels`. The node label is used for the first token of the node,
while remaining tokens are labeled with -100, such that they will be ignored by the loss function.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
text_pair (`List[str]` or `List[int]`, *optional*):
Optional second sequence to be encoded. This can be a list of strings (nodes of a single example) or a
list of list of strings (nodes of a batch of examples).
"""
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
verbose=verbose,
**kwargs,
)
tokens = []
pair_tokens = []
xpath_tags_seq = []
xpath_subs_seq = []
pair_xpath_tags_seq = []
pair_xpath_subs_seq = []
labels = []
if text_pair is None:
if node_labels is None:
# CASE 1: web page classification (training + inference) + CASE 2: token classification (inference)
for word, xpath in zip(text, xpaths):
if len(word) < 1: # skip empty nodes
continue
word_tokens = self.tokenize(word)
tokens.extend(word_tokens)
xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpath)
xpath_tags_seq.extend([xpath_tags_list] * len(word_tokens))
xpath_subs_seq.extend([xpath_subs_list] * len(word_tokens))
else:
# CASE 2: token classification (training)
for word, xpath, label in zip(text, xpaths, node_labels):
if len(word) < 1: # skip empty nodes
continue
word_tokens = self.tokenize(word)
tokens.extend(word_tokens)
xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpath)
xpath_tags_seq.extend([xpath_tags_list] * len(word_tokens))
xpath_subs_seq.extend([xpath_subs_list] * len(word_tokens))
if self.only_label_first_subword:
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
labels.extend([label] + [self.pad_token_label] * (len(word_tokens) - 1))
else:
labels.extend([label] * len(word_tokens))
else:
# CASE 3: web page question answering (inference)
# text = question
# text_pair = nodes
tokens = self.tokenize(text)
xpath_tags_seq = [self.pad_xpath_tags_seq for _ in range(len(tokens))]
xpath_subs_seq = [self.pad_xpath_subs_seq for _ in range(len(tokens))]
for word, xpath in zip(text_pair, xpaths):
if len(word) < 1: # skip empty nodes
continue
word_tokens = self.tokenize(word)
pair_tokens.extend(word_tokens)
xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpath)
pair_xpath_tags_seq.extend([xpath_tags_list] * len(word_tokens))
pair_xpath_subs_seq.extend([xpath_subs_list] * len(word_tokens))
# Create ids + pair_ids
ids = self.convert_tokens_to_ids(tokens)
pair_ids = self.convert_tokens_to_ids(pair_tokens) if pair_tokens else None
if (
return_overflowing_tokens
and truncation_strategy == TruncationStrategy.LONGEST_FIRST
and pair_ids is not None
):
raise ValueError(
"Not possible to return overflowing tokens for pair of sequences with the "
"`longest_first`. Please select another truncation strategy than `longest_first`, "
"for instance `only_second` or `only_first`."
)
# Compute the total size of the returned encodings
pair = bool(pair_ids is not None)
len_ids = len(ids)
len_pair_ids = len(pair_ids) if pair else 0
total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
# Truncation: Handle max sequence length
overflowing_tokens = []
overflowing_xpath_tags_seq = []
overflowing_xpath_subs_seq = []
overflowing_labels = []
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
(
ids,
xpath_tags_seq,
xpath_subs_seq,
pair_ids,
pair_xpath_tags_seq,
pair_xpath_subs_seq,
labels,
overflowing_tokens,
overflowing_xpath_tags_seq,
overflowing_xpath_subs_seq,
overflowing_labels,
) = self.truncate_sequences(
ids,
xpath_tags_seq=xpath_tags_seq,
xpath_subs_seq=xpath_subs_seq,
pair_ids=pair_ids,
pair_xpath_tags_seq=pair_xpath_tags_seq,
pair_xpath_subs_seq=pair_xpath_subs_seq,
labels=labels,
num_tokens_to_remove=total_len - max_length,
truncation_strategy=truncation_strategy,
stride=stride,
)
if return_token_type_ids and not add_special_tokens:
raise ValueError(
"Asking to return token_type_ids while setting add_special_tokens to False "
"results in an undefined behavior. Please set add_special_tokens to True or "
"set return_token_type_ids to None."
)
# Load from model defaults
if return_token_type_ids is None:
return_token_type_ids = "token_type_ids" in self.model_input_names
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
encoded_inputs = {}
if return_overflowing_tokens:
encoded_inputs["overflowing_tokens"] = overflowing_tokens
encoded_inputs["overflowing_xpath_tags_seq"] = overflowing_xpath_tags_seq
encoded_inputs["overflowing_xpath_subs_seq"] = overflowing_xpath_subs_seq
encoded_inputs["overflowing_labels"] = overflowing_labels
encoded_inputs["num_truncated_tokens"] = total_len - max_length
# Add special tokens
if add_special_tokens:
sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
xpath_tags_ids = self.build_xpath_tags_with_special_tokens(xpath_tags_seq, pair_xpath_tags_seq)
xpath_subs_ids = self.build_xpath_subs_with_special_tokens(xpath_subs_seq, pair_xpath_subs_seq)
if labels:
labels = [self.pad_token_label] + labels + [self.pad_token_label]
else:
sequence = ids + pair_ids if pair else ids
token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
xpath_tags_ids = xpath_tags_seq + pair_xpath_tags_seq if pair else xpath_tags_seq
xpath_subs_ids = xpath_subs_seq + pair_xpath_subs_seq if pair else xpath_subs_seq
# Build output dictionary
encoded_inputs["input_ids"] = sequence
encoded_inputs["xpath_tags_seq"] = xpath_tags_ids
encoded_inputs["xpath_subs_seq"] = xpath_subs_ids
if return_token_type_ids:
encoded_inputs["token_type_ids"] = token_type_ids
if return_special_tokens_mask:
if add_special_tokens:
encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
else:
encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
if labels:
encoded_inputs["labels"] = labels
# Check lengths
self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose)
# Padding
if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
encoded_inputs = self.pad(
encoded_inputs,
max_length=max_length,
padding=padding_strategy.value,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
if return_length:
encoded_inputs["length"] = len(encoded_inputs["input_ids"])
batch_outputs = BatchEncoding(
encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
)
return batch_outputs
def truncate_sequences(
self,
ids: List[int],
xpath_tags_seq: List[List[int]],
xpath_subs_seq: List[List[int]],
pair_ids: Optional[List[int]] = None,
pair_xpath_tags_seq: Optional[List[List[int]]] = None,
pair_xpath_subs_seq: Optional[List[List[int]]] = None,
labels: Optional[List[int]] = None,
num_tokens_to_remove: int = 0,
truncation_strategy: Union[str, TruncationStrategy] = "longest_first",
stride: int = 0,
) -> Tuple[List[int], List[int], List[int]]:
"""
Args:
Truncates a sequence pair in-place following the strategy.
ids (`List[int]`):
Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and
`convert_tokens_to_ids` methods.
xpath_tags_seq (`List[List[int]]`):
XPath tag IDs of the first sequence.
xpath_subs_seq (`List[List[int]]`):
XPath sub IDs of the first sequence.
pair_ids (`List[int]`, *optional*):
Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize`
and `convert_tokens_to_ids` methods.
pair_xpath_tags_seq (`List[List[int]]`, *optional*):
XPath tag IDs of the second sequence.
pair_xpath_subs_seq (`List[List[int]]`, *optional*):
XPath sub IDs of the second sequence.
num_tokens_to_remove (`int`, *optional*, defaults to 0):
Number of tokens to remove using the truncation strategy.
truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to
`False`):
The strategy to follow for truncation. Can be:
- `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
maximum acceptable input length for the model if that argument is not provided. This will truncate
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a
batch of pairs) is provided.
- `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater
than the model maximum admissible input size).
stride (`int`, *optional*, defaults to 0):
If set to a positive number, the overflowing tokens returned will contain some tokens from the main
sequence returned. The value of this argument defines the number of additional tokens.
Returns:
`Tuple[List[int], List[int], List[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of
overflowing tokens. Note: The *longest_first* strategy returns empty list of overflowing tokens if a pair
of sequences (or a batch of pairs) is provided.
"""
if num_tokens_to_remove <= 0:
return ids, xpath_tags_seq, xpath_subs_seq, pair_ids, pair_xpath_tags_seq, pair_xpath_subs_seq, [], [], []
if not isinstance(truncation_strategy, TruncationStrategy):
truncation_strategy = TruncationStrategy(truncation_strategy)
overflowing_tokens = []
overflowing_xpath_tags_seq = []
overflowing_xpath_subs_seq = []
overflowing_labels = []
if truncation_strategy == TruncationStrategy.ONLY_FIRST or (
truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is None
):
if len(ids) > num_tokens_to_remove:
window_len = min(len(ids), stride + num_tokens_to_remove)
overflowing_tokens = ids[-window_len:]
overflowing_xpath_tags_seq = xpath_tags_seq[-window_len:]
overflowing_xpath_subs_seq = xpath_subs_seq[-window_len:]
ids = ids[:-num_tokens_to_remove]
xpath_tags_seq = xpath_tags_seq[:-num_tokens_to_remove]
xpath_subs_seq = xpath_subs_seq[:-num_tokens_to_remove]
labels = labels[:-num_tokens_to_remove]
else:
error_msg = (
f"We need to remove {num_tokens_to_remove} to truncate the input "
f"but the first sequence has a length {len(ids)}. "
)
if truncation_strategy == TruncationStrategy.ONLY_FIRST:
error_msg = (
error_msg + "Please select another truncation strategy than "
f"{truncation_strategy}, for instance 'longest_first' or 'only_second'."
)
logger.error(error_msg)
elif truncation_strategy == TruncationStrategy.LONGEST_FIRST:
logger.warning(
"Be aware, overflowing tokens are not returned for the setting you have chosen,"
f" i.e. sequence pairs with the '{TruncationStrategy.LONGEST_FIRST.value}' "
"truncation strategy. So the returned list will always be empty even if some "
"tokens have been removed."
)
for _ in range(num_tokens_to_remove):
if pair_ids is None or len(ids) > len(pair_ids):
ids = ids[:-1]
xpath_tags_seq = xpath_tags_seq[:-1]
xpath_subs_seq = xpath_subs_seq[:-1]
labels = labels[:-1]
else:
pair_ids = pair_ids[:-1]
pair_xpath_tags_seq = pair_xpath_tags_seq[:-1]
pair_xpath_subs_seq = pair_xpath_subs_seq[:-1]
elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None:
if len(pair_ids) > num_tokens_to_remove:
window_len = min(len(pair_ids), stride + num_tokens_to_remove)
overflowing_tokens = pair_ids[-window_len:]
overflowing_xpath_tags_seq = pair_xpath_tags_seq[-window_len:]
overflowing_xpath_subs_seq = pair_xpath_subs_seq[-window_len:]
pair_ids = pair_ids[:-num_tokens_to_remove]
pair_xpath_tags_seq = pair_xpath_tags_seq[:-num_tokens_to_remove]
pair_xpath_subs_seq = pair_xpath_subs_seq[:-num_tokens_to_remove]
else:
logger.error(
f"We need to remove {num_tokens_to_remove} to truncate the input "
f"but the second sequence has a length {len(pair_ids)}. "
f"Please select another truncation strategy than {truncation_strategy}, "
"for instance 'longest_first' or 'only_first'."
)
return (
ids,
xpath_tags_seq,
xpath_subs_seq,
pair_ids,
pair_xpath_tags_seq,
pair_xpath_subs_seq,
labels,
overflowing_tokens,
overflowing_xpath_tags_seq,
overflowing_xpath_subs_seq,
overflowing_labels,
)
def _pad(
self,
encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
) -> dict:
"""
Args:
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
encoded_inputs:
Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The tokenizer padding sides are defined in self.padding_side:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
`>= 7.5` (Volta).
return_attention_mask:
(optional) Set to False to avoid returning attention mask (default: set to model specifics)
"""
# Load from model defaults
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
required_input = encoded_inputs[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(required_input)
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
# Initialize attention mask if not present.
if return_attention_mask and "attention_mask" not in encoded_inputs:
encoded_inputs["attention_mask"] = [1] * len(required_input)
if needs_to_be_padded:
difference = max_length - len(required_input)
if self.padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = (
encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
)
if "xpath_tags_seq" in encoded_inputs:
encoded_inputs["xpath_tags_seq"] = (
encoded_inputs["xpath_tags_seq"] + [self.pad_xpath_tags_seq] * difference
)
if "xpath_subs_seq" in encoded_inputs:
encoded_inputs["xpath_subs_seq"] = (
encoded_inputs["xpath_subs_seq"] + [self.pad_xpath_subs_seq] * difference
)
if "labels" in encoded_inputs:
encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
elif self.padding_side == "left":
if return_attention_mask:
encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
"token_type_ids"
]
if "xpath_tags_seq" in encoded_inputs:
encoded_inputs["xpath_tags_seq"] = [self.pad_xpath_tags_seq] * difference + encoded_inputs[
"xpath_tags_seq"
]
if "xpath_subs_seq" in encoded_inputs:
encoded_inputs["xpath_subs_seq"] = [self.pad_xpath_subs_seq] * difference + encoded_inputs[
"xpath_subs_seq"
]
if "labels" in encoded_inputs:
encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side))
return encoded_inputs
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/markuplm/tokenization_markuplm_fast.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fast tokenization class for MarkupLM. It overwrites 2 methods of the slow tokenizer class, namely _batch_encode_plus
and _encode_plus, in which the Rust tokenizer is used.
"""
import json
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings
from ...tokenization_utils_base import (
ENCODE_KWARGS_DOCSTRING,
AddedToken,
BatchEncoding,
EncodedInput,
PreTokenizedInput,
TextInput,
TextInputPair,
TruncationStrategy,
)
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_markuplm import MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING, MarkupLMTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/vocab.json",
"microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/vocab.json",
},
"merges_file": {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/merges.txt",
"microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/merges.txt",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"microsoft/markuplm-base": 512,
"microsoft/markuplm-large": 512,
}
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large #
of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset
you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe
vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
"""
bs = (
list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
)
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8 + n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
def get_pairs(word):
"""
Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length
strings).
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
class MarkupLMTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE).
[`MarkupLMTokenizerFast`] can be used to turn HTML strings into to token-level `input_ids`, `attention_mask`,
`token_type_ids`, `xpath_tags_seq` and `xpath_tags_seq`. This tokenizer inherits from [`PreTrainedTokenizer`] which
contains most of the main methods.
Users should refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
slow_tokenizer_class = MarkupLMTokenizer
def __init__(
self,
vocab_file,
merges_file,
tags_dict,
tokenizer_file=None,
errors="replace",
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
add_prefix_space=False,
max_depth=50,
max_width=1000,
pad_width=1001,
pad_token_label=-100,
only_label_first_subword=True,
trim_offsets=False,
**kwargs,
):
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
super().__init__(
vocab_file=vocab_file,
merges_file=merges_file,
tags_dict=tags_dict,
tokenizer_file=tokenizer_file,
errors=errors,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_token=sep_token,
cls_token=cls_token,
pad_token=pad_token,
mask_token=mask_token,
add_prefix_space=add_prefix_space,
trim_offsets=trim_offsets,
max_depth=max_depth,
max_width=max_width,
pad_width=pad_width,
pad_token_label=pad_token_label,
only_label_first_subword=only_label_first_subword,
**kwargs,
)
if trim_offsets:
# Not implemented yet, because we need to chain two post processors which is not possible yet
# We need to wait for https://github.com/huggingface/tokenizers/pull/1005
# With `trim_offsets=False` we don't need to do add `processors.ByteLevel(trim_offsets=False)`
# because it's not doing anything
raise NotImplementedError(
"`trim_offsets=True` is not implemented for MarkupLMTokenizerFast. Please set it to False."
)
self.tags_dict = tags_dict
pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type"))
pre_tok_state["add_prefix_space"] = add_prefix_space
self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state)
self.add_prefix_space = add_prefix_space
tokenizer_component = "post_processor"
tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None)
if tokenizer_component_instance:
state = json.loads(tokenizer_component_instance.__getstate__())
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
state["sep"] = tuple(state["sep"])
if "cls" in state:
state["cls"] = tuple(state["cls"])
changes_to_apply = False
if state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
state["add_prefix_space"] = add_prefix_space
changes_to_apply = True
if changes_to_apply:
component_class = getattr(processors, state.pop("type"))
new_value = component_class(**state)
setattr(self.backend_tokenizer, tokenizer_component, new_value)
# additional properties
self.max_depth = max_depth
self.max_width = max_width
self.pad_width = pad_width
self.unk_tag_id = len(self.tags_dict)
self.pad_tag_id = self.unk_tag_id + 1
self.pad_xpath_tags_seq = [self.pad_tag_id] * self.max_depth
self.pad_xpath_subs_seq = [self.pad_width] * self.max_depth
self.pad_token_label = pad_token_label
self.only_label_first_subword = only_label_first_subword
def get_xpath_seq(self, xpath):
"""
Given the xpath expression of one particular node (like "/html/body/div/li[1]/div/span[2]"), return a list of
tag IDs and corresponding subscripts, taking into account max depth.
"""
xpath_tags_list = []
xpath_subs_list = []
xpath_units = xpath.split("/")
for unit in xpath_units:
if not unit.strip():
continue
name_subs = unit.strip().split("[")
tag_name = name_subs[0]
sub = 0 if len(name_subs) == 1 else int(name_subs[1][:-1])
xpath_tags_list.append(self.tags_dict.get(tag_name, self.unk_tag_id))
xpath_subs_list.append(min(self.max_width, sub))
xpath_tags_list = xpath_tags_list[: self.max_depth]
xpath_subs_list = xpath_subs_list[: self.max_depth]
xpath_tags_list += [self.pad_tag_id] * (self.max_depth - len(xpath_tags_list))
xpath_subs_list += [self.pad_width] * (self.max_depth - len(xpath_subs_list))
return xpath_tags_list, xpath_subs_list
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
xpaths: Union[List[List[int]], List[List[List[int]]]] = None,
node_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences with nodes, xpaths and optional labels.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
(words of a single example or questions of a batch of examples) or a list of list of strings (batch of
words).
text_pair (`List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
(pretokenized string).
xpaths (`List[List[int]]`, `List[List[List[int]]]`):
Node-level xpaths. Each bounding box should be normalized to be on a 0-1000 scale.
node_labels (`List[int]`, `List[List[int]]`, *optional*):
Node-level integer labels (for token classification tasks).
"""
# Input type checking for clearer error
def _is_valid_text_input(t):
if isinstance(t, str):
# Strings are fine
return True
elif isinstance(t, (list, tuple)):
# List are fine as long as they are...
if len(t) == 0:
# ... empty
return True
elif isinstance(t[0], str):
# ... list of strings
return True
elif isinstance(t[0], (list, tuple)):
# ... list with an empty list or with a list of strings
return len(t[0]) == 0 or isinstance(t[0][0], str)
else:
return False
else:
return False
if text_pair is not None:
# in case text + text_pair are provided, text = questions, text_pair = nodes
if not _is_valid_text_input(text):
raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
if not isinstance(text_pair, (list, tuple)):
raise ValueError(
"Nodes must be of type `List[str]` (single pretokenized example), "
"or `List[List[str]]` (batch of pretokenized examples)."
)
else:
# in case only text is provided => must be nodes
if not isinstance(text, (list, tuple)):
raise ValueError(
"Nodes must be of type `List[str]` (single pretokenized example), "
"or `List[List[str]]` (batch of pretokenized examples)."
)
if text_pair is not None:
is_batched = isinstance(text, (list, tuple))
else:
is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
nodes = text if text_pair is None else text_pair
assert xpaths is not None, "You must provide corresponding xpaths"
if is_batched:
assert len(nodes) == len(xpaths), "You must provide nodes and xpaths for an equal amount of examples"
for nodes_example, xpaths_example in zip(nodes, xpaths):
assert len(nodes_example) == len(xpaths_example), "You must provide as many nodes as there are xpaths"
else:
assert len(nodes) == len(xpaths), "You must provide as many nodes as there are xpaths"
if is_batched:
if text_pair is not None and len(text) != len(text_pair):
raise ValueError(
f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
f" {len(text_pair)}."
)
batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
is_pair = bool(text_pair is not None)
return self.batch_encode_plus(
batch_text_or_text_pairs=batch_text_or_text_pairs,
is_pair=is_pair,
xpaths=xpaths,
node_labels=node_labels,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
else:
return self.encode_plus(
text=text,
text_pair=text_pair,
xpaths=xpaths,
node_labels=node_labels,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
],
is_pair: bool = None,
xpaths: Optional[List[List[List[int]]]] = None,
node_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
verbose=verbose,
**kwargs,
)
return self._batch_encode_plus(
batch_text_or_text_pairs=batch_text_or_text_pairs,
is_pair=is_pair,
xpaths=xpaths,
node_labels=node_labels,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> List[str]:
batched_input = [(text, pair)] if pair else [text]
encodings = self._tokenizer.encode_batch(
batched_input, add_special_tokens=add_special_tokens, is_pretokenized=False, **kwargs
)
return encodings[0].tokens
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def encode_plus(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
xpaths: Optional[List[List[int]]] = None,
node_labels: Optional[List[int]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
`__call__` should be used instead.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
text_pair (`List[str]` or `List[int]`, *optional*):
Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
list of list of strings (words of a batch of examples).
"""
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
verbose=verbose,
**kwargs,
)
return self._encode_plus(
text=text,
xpaths=xpaths,
text_pair=text_pair,
node_labels=node_labels,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
def _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
],
is_pair: bool = None,
xpaths: Optional[List[List[List[int]]]] = None,
node_labels: Optional[List[List[int]]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[str] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
) -> BatchEncoding:
if not isinstance(batch_text_or_text_pairs, list):
raise TypeError(f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})")
# Set the truncation and padding strategy and restore the initial configuration
self.set_truncation_and_padding(
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
)
if is_pair:
batch_text_or_text_pairs = [([text], text_pair) for text, text_pair in batch_text_or_text_pairs]
encodings = self._tokenizer.encode_batch(
batch_text_or_text_pairs,
add_special_tokens=add_special_tokens,
is_pretokenized=True, # we set this to True as MarkupLM always expects pretokenized inputs
)
# Convert encoding to dict
# `Tokens` is a tuple of (List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]],
# List[EncodingFast]) with nested dimensions corresponding to batch, overflows, sequence length
tokens_and_encodings = [
self._convert_encoding(
encoding=encoding,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=True
if node_labels is not None
else return_offsets_mapping, # we use offsets to create the labels
return_length=return_length,
verbose=verbose,
)
for encoding in encodings
]
# Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension
# From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length)
# (we say ~ because the number of overflow varies with the example in the batch)
#
# To match each overflowing sample with the original sample in the batch
# we add an overflow_to_sample_mapping array (see below)
sanitized_tokens = {}
for key in tokens_and_encodings[0][0].keys():
stack = [e for item, _ in tokens_and_encodings for e in item[key]]
sanitized_tokens[key] = stack
sanitized_encodings = [e for _, item in tokens_and_encodings for e in item]
# If returning overflowing tokens, we need to return a mapping
# from the batch idx to the original sample
if return_overflowing_tokens:
overflow_to_sample_mapping = []
for i, (toks, _) in enumerate(tokens_and_encodings):
overflow_to_sample_mapping += [i] * len(toks["input_ids"])
sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping
for input_ids in sanitized_tokens["input_ids"]:
self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose)
# create the token-level xpaths tags and subscripts
xpath_tags_seq = []
xpath_subs_seq = []
for batch_index in range(len(sanitized_tokens["input_ids"])):
if return_overflowing_tokens:
original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
else:
original_index = batch_index
xpath_tags_seq_example = []
xpath_subs_seq_example = []
for id, sequence_id, word_id in zip(
sanitized_tokens["input_ids"][batch_index],
sanitized_encodings[batch_index].sequence_ids,
sanitized_encodings[batch_index].word_ids,
):
if word_id is not None:
if is_pair and sequence_id == 0:
xpath_tags_seq_example.append(self.pad_xpath_tags_seq)
xpath_subs_seq_example.append(self.pad_xpath_subs_seq)
else:
xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpaths[original_index][word_id])
xpath_tags_seq_example.extend([xpath_tags_list])
xpath_subs_seq_example.extend([xpath_subs_list])
else:
if id in [self.cls_token_id, self.sep_token_id, self.pad_token_id]:
xpath_tags_seq_example.append(self.pad_xpath_tags_seq)
xpath_subs_seq_example.append(self.pad_xpath_subs_seq)
else:
raise ValueError("Id not recognized")
xpath_tags_seq.append(xpath_tags_seq_example)
xpath_subs_seq.append(xpath_subs_seq_example)
sanitized_tokens["xpath_tags_seq"] = xpath_tags_seq
sanitized_tokens["xpath_subs_seq"] = xpath_subs_seq
# optionally, create the labels
if node_labels is not None:
labels = []
for batch_index in range(len(sanitized_tokens["input_ids"])):
if return_overflowing_tokens:
original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
else:
original_index = batch_index
labels_example = []
for id, offset, word_id in zip(
sanitized_tokens["input_ids"][batch_index],
sanitized_tokens["offset_mapping"][batch_index],
sanitized_encodings[batch_index].word_ids,
):
if word_id is not None:
if self.only_label_first_subword:
if offset[0] == 0:
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
labels_example.append(node_labels[original_index][word_id])
else:
labels_example.append(self.pad_token_label)
else:
labels_example.append(node_labels[original_index][word_id])
else:
labels_example.append(self.pad_token_label)
labels.append(labels_example)
sanitized_tokens["labels"] = labels
# finally, remove offsets if the user didn't want them
if not return_offsets_mapping:
del sanitized_tokens["offset_mapping"]
return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors)
def _encode_plus(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
xpaths: Optional[List[List[int]]] = None,
node_labels: Optional[List[int]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[bool] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
# make it a batched input
# 2 options:
# 1) only text, in case text must be a list of str
# 2) text + text_pair, in which case text = str and text_pair a list of str
batched_input = [(text, text_pair)] if text_pair else [text]
batched_xpaths = [xpaths]
batched_node_labels = [node_labels] if node_labels is not None else None
batched_output = self._batch_encode_plus(
batched_input,
is_pair=bool(text_pair is not None),
xpaths=batched_xpaths,
node_labels=batched_node_labels,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
# Return tensor is None, then we can remove the leading batch axis
# Overflowing tokens are returned as a batch of output so we keep them in this case
if return_tensors is None and not return_overflowing_tokens:
batched_output = BatchEncoding(
{
key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
for key, value in batched_output.items()
},
batched_output.encodings,
)
self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose)
return batched_output
def _pad(
self,
encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
) -> dict:
"""
Args:
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
encoded_inputs:
Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The tokenizer padding sides are defined in self.padding_side:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
`>= 7.5` (Volta).
return_attention_mask:
(optional) Set to False to avoid returning attention mask (default: set to model specifics)
"""
# Load from model defaults
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
required_input = encoded_inputs[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(required_input)
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
# Initialize attention mask if not present.
if return_attention_mask and "attention_mask" not in encoded_inputs:
encoded_inputs["attention_mask"] = [1] * len(required_input)
if needs_to_be_padded:
difference = max_length - len(required_input)
if self.padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = (
encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
)
if "xpath_tags_seq" in encoded_inputs:
encoded_inputs["xpath_tags_seq"] = (
encoded_inputs["xpath_tags_seq"] + [self.pad_xpath_tags_seq] * difference
)
if "xpath_subs_seq" in encoded_inputs:
encoded_inputs["xpath_subs_seq"] = (
encoded_inputs["xpath_subs_seq"] + [self.pad_xpath_subs_seq] * difference
)
if "labels" in encoded_inputs:
encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
elif self.padding_side == "left":
if return_attention_mask:
encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
"token_type_ids"
]
if "xpath_tags_seq" in encoded_inputs:
encoded_inputs["xpath_tags_seq"] = [self.pad_xpath_tags_seq] * difference + encoded_inputs[
"xpath_tags_seq"
]
if "xpath_subs_seq" in encoded_inputs:
encoded_inputs["xpath_subs_seq"] = [self.pad_xpath_subs_seq] * difference + encoded_inputs[
"xpath_subs_seq"
]
if "labels" in encoded_inputs:
encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side))
return encoded_inputs
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A RoBERTa sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + token_ids_1 + sep
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + token_ids_1 + sep) * [0]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
files = self._tokenizer.model.save(save_directory, name=filename_prefix)
return tuple(files)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/imagegpt/configuration_imagegpt.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" OpenAI ImageGPT configuration"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
logger = logging.get_logger(__name__)
IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"openai/imagegpt-small": "",
"openai/imagegpt-medium": "",
"openai/imagegpt-large": "",
}
class ImageGPTConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`ImageGPTModel`] or a [`TFImageGPTModel`]. It is
used to instantiate a GPT-2 model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the ImageGPT
[openai/imagegpt-small](https://huggingface.co/openai/imagegpt-small) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 512):
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`ImageGPTModel`] or [`TFImageGPTModel`].
n_positions (`int`, *optional*, defaults to 32*32):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
n_embd (`int`, *optional*, defaults to 512):
Dimensionality of the embeddings and hidden states.
n_layer (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer encoder.
n_inner (`int`, *optional*, defaults to None):
Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
activation_function (`str`, *optional*, defaults to `"quick_gelu"`):
Activation function (can be one of the activation functions defined in src/transformers/activations.py).
Defaults to "quick_gelu".
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`int`, *optional*, defaults to 0.1):
The dropout ratio for the embeddings.
attn_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon to use in the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
scale_attn_weights (`bool`, *optional*, defaults to `True`):
Scale attention weights by dividing by sqrt(hidden_size)..
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
Whether to additionally scale attention weights by `1 / layer_idx + 1`.
reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
dot-product/softmax to float() when training with mixed precision.
Example:
```python
>>> from transformers import ImageGPTConfig, ImageGPTModel
>>> # Initializing a ImageGPT configuration
>>> configuration = ImageGPTConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = ImageGPTModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "imagegpt"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
vocab_size=512 + 1, # add one for start of sentence (sos) token
n_positions=32 * 32,
n_embd=512,
n_layer=24,
n_head=8,
n_inner=None,
activation_function="quick_gelu",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
scale_attn_weights=True,
use_cache=True,
tie_word_embeddings=False,
scale_attn_by_inverse_layer_idx=False,
reorder_and_upcast_attn=False,
**kwargs,
):
self.vocab_size = vocab_size
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.n_inner = n_inner
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.scale_attn_weights = scale_attn_weights
self.use_cache = use_cache
self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
self.reorder_and_upcast_attn = reorder_and_upcast_attn
self.tie_word_embeddings = tie_word_embeddings
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
class ImageGPTOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
]
)
def generate_dummy_inputs(
self,
preprocessor: "FeatureExtractionMixin",
batch_size: int = 1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional["TensorType"] = None,
num_channels: int = 3,
image_width: int = 32,
image_height: int = 32,
) -> Mapping[str, Any]:
"""
Generate inputs to provide to the ONNX exporter for the specific framework
Args:
preprocessor ([`PreTrainedTokenizerBase`] or [`FeatureExtractionMixin`]):
The preprocessor associated with this model configuration.
batch_size (`int`, *optional*, defaults to -1):
The batch size to export the model for (-1 means dynamic axis).
num_choices (`int`, *optional*, defaults to -1):
The number of candidate answers provided for multiple choice task (-1 means dynamic axis).
seq_length (`int`, *optional*, defaults to -1):
The sequence length to export the model for (-1 means dynamic axis).
is_pair (`bool`, *optional*, defaults to `False`):
Indicate if the input is a pair (sentence 1, sentence 2)
framework (`TensorType`, *optional*, defaults to `None`):
The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for.
num_channels (`int`, *optional*, defaults to 3):
The number of channels of the generated images.
image_width (`int`, *optional*, defaults to 40):
The width of the generated images.
image_height (`int`, *optional*, defaults to 40):
The height of the generated images.
Returns:
Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
"""
input_image = self._generate_dummy_images(batch_size, num_channels, image_height, image_width)
inputs = dict(preprocessor(images=input_image, return_tensors=framework))
return inputs
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/imagegpt/convert_imagegpt_original_tf2_to_pytorch.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert OpenAI Image GPT checkpoints."""
import argparse
import torch
from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def convert_imagegpt_checkpoint_to_pytorch(imagegpt_checkpoint_path, model_size, pytorch_dump_folder_path):
# Construct configuration depending on size
MODELS = {"small": (512, 8, 24), "medium": (1024, 8, 36), "large": (1536, 16, 48)}
n_embd, n_head, n_layer = MODELS[model_size] # set model hyperparameters
config = ImageGPTConfig(n_embd=n_embd, n_layer=n_layer, n_head=n_head)
model = ImageGPTForCausalLM(config)
# Load weights from numpy
load_tf_weights_in_imagegpt(model, config, imagegpt_checkpoint_path)
# Save pytorch-model
pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(f"Save PyTorch model to {pytorch_weights_dump_path}")
torch.save(model.state_dict(), pytorch_weights_dump_path)
print(f"Save configuration file to {pytorch_config_dump_path}")
with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
f.write(config.to_json_string())
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--imagegpt_checkpoint_path",
default=None,
type=str,
required=True,
help="Path to the TensorFlow checkpoint path.",
)
parser.add_argument(
"--model_size",
default=None,
type=str,
required=True,
help="Size of the model (can be either 'small', 'medium' or 'large').",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
args = parser.parse_args()
convert_imagegpt_checkpoint_to_pytorch(
args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/imagegpt/feature_extraction_imagegpt.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Feature extractor class for ImageGPT."""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
logger = logging.get_logger(__name__)
class ImageGPTFeatureExtractor(ImageGPTImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use ImageGPTImageProcessor instead.",
FutureWarning,
)
super().__init__(*args, **kwargs)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/imagegpt/image_processing_imagegpt.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Image processor class for ImageGPT."""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
infer_channel_dimension_format,
is_scaled_image,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
logger = logging.get_logger(__name__)
def squared_euclidean_distance(a, b):
b = b.T
a2 = np.sum(np.square(a), axis=1)
b2 = np.sum(np.square(b), axis=0)
ab = np.matmul(a, b)
d = a2[:, None] - 2 * ab + b2[None, :]
return d
def color_quantize(x, clusters):
x = x.reshape(-1, 3)
d = squared_euclidean_distance(x, clusters)
return np.argmin(d, axis=1)
class ImageGPTImageProcessor(BaseImageProcessor):
r"""
Constructs a ImageGPT image processor. This image processor can be used to resize images to a smaller resolution
(such as 32x32 or 64x64), normalize them and finally color quantize them to obtain sequences of "pixel values"
(color clusters).
Args:
clusters (`np.ndarray` or `List[List[int]]`, *optional*):
The color clusters to use, of shape `(n_clusters, 3)` when color quantizing. Can be overriden by `clusters`
in `preprocess`.
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's dimensions to `(size["height"], size["width"])`. Can be overridden by
`do_resize` in `preprocess`.
size (`Dict[str, int]` *optional*, defaults to `{"height": 256, "width": 256}`):
Size of the image after resizing. Can be overridden by `size` in `preprocess`.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image pixel value to between [-1, 1]. Can be overridden by `do_normalize` in
`preprocess`.
do_color_quantize (`bool`, *optional*, defaults to `True`):
Whether to color quantize the image. Can be overridden by `do_color_quantize` in `preprocess`.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
# clusters is a first argument to maintain backwards compatibility with the old ImageGPTImageProcessor
clusters: Optional[Union[List[List[int]], np.ndarray]] = None,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_normalize: bool = True,
do_color_quantize: bool = True,
**kwargs,
) -> None:
super().__init__(**kwargs)
size = size if size is not None else {"height": 256, "width": 256}
size = get_size_dict(size)
self.clusters = np.array(clusters) if clusters is not None else None
self.do_resize = do_resize
self.size = size
self.resample = resample
self.do_normalize = do_normalize
self.do_color_quantize = do_color_quantize
# Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BILINEAR,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Resize an image to `(size["height"], size["width"])`.
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
`PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
Returns:
`np.ndarray`: The resized image.
"""
size = get_size_dict(size)
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
output_size = (size["height"], size["width"])
return resize(
image,
size=output_size,
resample=resample,
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
def normalize(
self,
image: np.ndarray,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
"""
Normalizes an images' pixel values to between [-1, 1].
Args:
image (`np.ndarray`):
Image to normalize.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
image = rescale(image=image, scale=1 / 127.5, data_format=data_format, input_data_format=input_data_format)
image = image - 1
return image
def preprocess(
self,
images: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_normalize: bool = None,
do_color_quantize: Optional[bool] = None,
clusters: Optional[Union[List[List[int]], np.ndarray]] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> PIL.Image.Image:
"""
Preprocess an image or batch of images.
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_normalize=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after resizing.
resample (`int`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only
has an effect if `do_resize` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image
do_color_quantize (`bool`, *optional*, defaults to `self.do_color_quantize`):
Whether to color quantize the image.
clusters (`np.ndarray` or `List[List[int]]`, *optional*, defaults to `self.clusters`):
Clusters used to quantize the image of shape `(n_clusters, 3)`. Only has an effect if
`do_color_quantize` is set to `True`.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `ChannelDimension.LAST`: image in (height, width, num_channels) format.
Only has an effect if `do_color_quantize` is set to `False`.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
do_resize = do_resize if do_resize is not None else self.do_resize
size = size if size is not None else self.size
size = get_size_dict(size)
resample = resample if resample is not None else self.resample
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
do_color_quantize = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
clusters = clusters if clusters is not None else self.clusters
clusters = np.array(clusters)
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True.")
if do_color_quantize and clusters is None:
raise ValueError("Clusters must be specified if do_color_quantize is True.")
# All transformations expect numpy arrays.
images = [to_numpy_array(image) for image in images]
if is_scaled_image(images[0]) and do_normalize:
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If you wish to do this, "
"make sure to set `do_normalize` to `False` and that pixel values are between [-1, 1].",
)
if input_data_format is None:
# We assume that all images have the same channel dimension format.
input_data_format = infer_channel_dimension_format(images[0])
if do_resize:
images = [
self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
for image in images
]
if do_normalize:
images = [self.normalize(image=image, input_data_format=input_data_format) for image in images]
if do_color_quantize:
images = [to_channel_dimension_format(image, ChannelDimension.LAST, input_data_format) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
images = np.array(images)
images = color_quantize(images, clusters).reshape(images.shape[:-1])
# flatten to (batch_size, height*width)
batch_size = images.shape[0]
images = images.reshape(batch_size, -1)
# We need to convert back to a list of images to keep consistent behaviour across processors.
images = list(images)
else:
images = [
to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
for image in images
]
data = {"input_ids": images}
return BatchFeature(data=data, tensor_type=return_tensors)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/imagegpt/__init__.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {
"configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["feature_extraction_imagegpt"] = ["ImageGPTFeatureExtractor"]
_import_structure["image_processing_imagegpt"] = ["ImageGPTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_imagegpt"] = [
"IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ImageGPTForCausalImageModeling",
"ImageGPTForImageClassification",
"ImageGPTModel",
"ImageGPTPreTrainedModel",
"load_tf_weights_in_imagegpt",
]
if TYPE_CHECKING:
from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
from .image_processing_imagegpt import ImageGPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_imagegpt import (
IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
ImageGPTForCausalImageModeling,
ImageGPTForImageClassification,
ImageGPTModel,
ImageGPTPreTrainedModel,
load_tf_weights_in_imagegpt,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/imagegpt/modeling_imagegpt.py | # coding=utf-8
# Copyright 2021 The OpenAI Team Authors and HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch OpenAI ImageGPT model."""
import math
import os
import warnings
from typing import Any, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.cuda.amp import autocast
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
SequenceClassifierOutputWithPast,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_imagegpt import ImageGPTConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "openai/imagegpt-small"
_CONFIG_FOR_DOC = "ImageGPTConfig"
IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"openai/imagegpt-small",
"openai/imagegpt-medium",
"openai/imagegpt-large",
# See all Image GPT models at https://huggingface.co/models?filter=imagegpt
]
def load_tf_weights_in_imagegpt(model, config, imagegpt_checkpoint_path):
"""
Load tf checkpoints in a pytorch model
"""
try:
import re
import tensorflow as tf
except ImportError:
logger.error(
"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions."
)
raise
tf_path = os.path.abspath(imagegpt_checkpoint_path)
logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
logger.info("Loading TF weight {} with shape {}".format(name, shape))
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array.squeeze())
for name, array in zip(names, arrays):
name = name[6:] # skip "model/"
name = name.split("/")
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model
if any(
n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
for n in name
) or name[-1] in ["_step"]:
logger.info("Skipping {}".format("/".join(name)))
continue
pointer = model
if name[-1] not in ["wtet"]:
pointer = getattr(pointer, "transformer")
for m_name in name:
if re.fullmatch(r"[A-Za-z]+\d+", m_name):
scope_names = re.split(r"(\d+)", m_name)
else:
scope_names = [m_name]
if scope_names[0] == "w" or scope_names[0] == "g":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "b":
pointer = getattr(pointer, "bias")
elif scope_names[0] == "wpe" or scope_names[0] == "wte":
pointer = getattr(pointer, scope_names[0])
pointer = getattr(pointer, "weight")
elif scope_names[0] in ["q_proj", "k_proj", "v_proj"]:
pointer = getattr(pointer, "c_attn")
pointer = getattr(pointer, "weight")
elif len(name) == 3 and name[1] == "attn" and scope_names[0] == "c_proj":
pointer = getattr(pointer, scope_names[0])
pointer = getattr(pointer, "weight")
elif scope_names[0] == "wtet":
pointer = getattr(pointer, "lm_head")
pointer = getattr(pointer, "weight")
elif scope_names[0] == "sos":
pointer = getattr(pointer, "wte")
pointer = getattr(pointer, "weight")
else:
pointer = getattr(pointer, scope_names[0])
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
if len(name) > 1 and name[1] == "attn" or name[-1] == "wtet" or name[-1] == "sos" or name[-1] == "wte":
pass # array is used to initialize only part of the pointer so sizes won't match
else:
try:
assert pointer.shape == array.shape
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
logger.info("Initialize PyTorch weight {}".format(name))
if name[-1] == "q_proj":
pointer.data[:, : config.n_embd] = torch.from_numpy(array.reshape(config.n_embd, config.n_embd)).T
elif name[-1] == "k_proj":
pointer.data[:, config.n_embd : 2 * config.n_embd] = torch.from_numpy(
array.reshape(config.n_embd, config.n_embd)
).T
elif name[-1] == "v_proj":
pointer.data[:, 2 * config.n_embd :] = torch.from_numpy(array.reshape(config.n_embd, config.n_embd)).T
elif len(name) == 3 and name[1] == "attn" and name[2] == "c_proj":
pointer.data = torch.from_numpy(array.reshape(config.n_embd, config.n_embd))
elif name[-1] == "wtet":
pointer.data = torch.from_numpy(array)
elif name[-1] == "wte":
pointer.data[: config.vocab_size - 1, :] = torch.from_numpy(array)
elif name[-1] == "sos":
pointer.data[-1] = torch.from_numpy(array)
else:
pointer.data = torch.from_numpy(array)
return model
class ImageGPTLayerNorm(nn.Module):
def __init__(self, hidden_size: Tuple[int], eps: float = 1e-5):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.Tensor(hidden_size))
def forward(self, tensor: torch.Tensor) -> tuple:
# input is not mean centered
return (
tensor
/ torch.sqrt(torch.mean(torch.square(tensor), axis=-1, keepdim=True) + self.eps)
* self.weight.data[..., :]
)
class ImageGPTAttention(nn.Module):
def __init__(self, config, is_cross_attention: Optional[bool] = False, layer_idx: Optional[int] = None):
super().__init__()
max_positions = config.max_position_embeddings
self.register_buffer(
"bias",
torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
1, 1, max_positions, max_positions
),
persistent=False,
)
self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False)
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
self.split_size = self.embed_dim
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale_attn_weights = config.scale_attn_weights
self.is_cross_attention = is_cross_attention
# Layer-wise attention scaling, reordering, and upcasting
self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx
self.layer_idx = layer_idx
self.reorder_and_upcast_attn = config.reorder_and_upcast_attn
if self.is_cross_attention:
self.c_attn = Conv1D(2 * self.embed_dim, self.embed_dim)
self.q_attn = Conv1D(self.embed_dim, self.embed_dim)
else:
self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim)
self.c_proj = Conv1D(self.embed_dim, self.embed_dim)
self.attn_dropout = nn.Dropout(config.attn_pdrop)
self.resid_dropout = nn.Dropout(config.resid_pdrop)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, self.head_dim, self.pruned_heads)
index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)])
# Prune conv1d layers
self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)
# Update hyper params
self.split_size = (self.split_size // self.num_heads) * (self.num_heads - len(heads))
self.num_heads = self.num_heads - len(heads)
self.pruned_heads = self.pruned_heads.union(heads)
def _attn(self, query, key, value, attention_mask=None, head_mask=None):
attn_weights = torch.matmul(query, key.transpose(-1, -2))
if self.scale_attn_weights:
attn_weights = attn_weights / (float(value.size(-1)) ** 0.5)
# Layer-wise attention scaling
if self.scale_attn_by_inverse_layer_idx:
attn_weights = attn_weights / float(self.layer_idx + 1)
if not self.is_cross_attention:
# if only "normal" attention layer implements causal mask
query_length, key_length = query.size(-2), key.size(-2)
causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
mask_value = torch.finfo(attn_weights.dtype).min
# Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
# Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
attn_weights = torch.where(causal_mask, attn_weights, mask_value)
if attention_mask is not None:
# Apply the attention mask
attn_weights = attn_weights + attention_mask
attn_weights = nn.Softmax(dim=-1)(attn_weights)
# Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op otherwise
attn_weights = attn_weights.type(value.dtype)
attn_weights = self.attn_dropout(attn_weights)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
def _upcast_and_reordered_attn(self, query, key, value, attention_mask=None, head_mask=None):
# Use `torch.baddbmm` (a bit more efficient w/ alpha param for scaling -- from Megatron-LM)
bsz, num_heads, q_seq_len, dk = query.size()
_, _, k_seq_len, _ = key.size()
# Preallocate attn_weights for `baddbmm`
attn_weights = torch.empty(bsz * num_heads, q_seq_len, k_seq_len, dtype=torch.float32, device=query.device)
# Compute Scale Factor
scale_factor = 1.0
if self.scale_attn_weights:
scale_factor /= float(value.size(-1)) ** 0.5
if self.scale_attn_by_inverse_layer_idx:
scale_factor /= float(self.layer_idx + 1)
# Upcast (turn off autocast) and reorder (Scale K by 1 / root(dk))
with autocast(enabled=False):
q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(-1, dk, k_seq_len)
attn_weights = torch.baddbmm(attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor)
attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len)
if not self.is_cross_attention:
# if only "normal" attention layer implements causal mask
query_length, key_length = query.size(-2), key.size(-2)
causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
mask_value = torch.finfo(attn_weights.dtype).min
# Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
# Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
attn_weights = torch.where(causal_mask, attn_weights, mask_value)
if attention_mask is not None:
# Apply the attention mask
attn_weights = attn_weights + attention_mask
attn_weights = nn.Softmax(dim=-1)(attn_weights)
# Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op if otherwise
if attn_weights.dtype != torch.float32:
raise RuntimeError("Error with upcasting, attn_weights does not have dtype torch.float32")
attn_weights = attn_weights.type(value.dtype)
attn_weights = self.attn_dropout(attn_weights)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
def _split_heads(self, tensor, num_heads, attn_head_size):
"""
Splits hidden_size dim into attn_head_size and num_heads
"""
new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
tensor = tensor.view(*new_shape)
return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
def _merge_heads(self, tensor, num_heads, attn_head_size):
"""
Merges attn_head_size dim and num_attn_heads dim into hidden_size
"""
tensor = tensor.permute(0, 2, 1, 3).contiguous()
new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
return tensor.view(new_shape)
def forward(
self,
hidden_states: torch.Tensor,
layer_past: Optional[bool] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
) -> tuple:
if encoder_hidden_states is not None:
if not hasattr(self, "q_attn"):
raise ValueError(
"If class is used as cross attention, the weights `q_attn` have to be defined. "
"Please make sure to instantiate class with `ImageGPTAttention(..., is_cross_attention=True)`."
)
query = self.q_attn(hidden_states)
key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2)
attention_mask = encoder_attention_mask
else:
query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2)
query = self._split_heads(query, self.num_heads, self.head_dim)
key = self._split_heads(key, self.num_heads, self.head_dim)
value = self._split_heads(value, self.num_heads, self.head_dim)
if layer_past is not None:
past_key, past_value = layer_past
key = torch.cat((past_key, key), dim=-2)
value = torch.cat((past_value, value), dim=-2)
if use_cache is True:
present = (key, value)
else:
present = None
if self.reorder_and_upcast_attn:
attn_output, attn_weights = self._upcast_and_reordered_attn(query, key, value, attention_mask, head_mask)
else:
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
attn_output = self.c_proj(attn_output)
attn_output = self.resid_dropout(attn_output)
outputs = (attn_output, present)
if output_attentions:
outputs += (attn_weights,)
return outputs # a, present, (attentions)
class ImageGPTMLP(nn.Module):
def __init__(self, intermediate_size, config):
super().__init__()
embed_dim = config.hidden_size
self.c_fc = Conv1D(intermediate_size, embed_dim)
self.c_proj = Conv1D(embed_dim, intermediate_size)
self.act = ACT2FN[config.activation_function]
self.dropout = nn.Dropout(config.resid_pdrop)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.c_fc(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.c_proj(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class ImageGPTBlock(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
hidden_size = config.hidden_size
inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
self.ln_1 = ImageGPTLayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.attn = ImageGPTAttention(config, layer_idx=layer_idx)
self.ln_2 = ImageGPTLayerNorm(hidden_size, eps=config.layer_norm_epsilon)
if config.add_cross_attention:
self.crossattention = ImageGPTAttention(config, is_cross_attention=True, layer_idx=layer_idx)
self.ln_cross_attn = ImageGPTLayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.mlp = ImageGPTMLP(inner_dim, config)
def forward(
self,
hidden_states: torch.Tensor,
layer_past: Optional[bool] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
) -> tuple:
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_outputs = self.attn(
hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
outputs = attn_outputs[1:]
# residual connection
hidden_states = attn_output + residual
if encoder_hidden_states is not None:
# add one self-attention block for cross-attention
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
"cross-attention layers by setting `config.add_cross_attention=True`"
)
residual = hidden_states
hidden_states = self.ln_cross_attn(hidden_states)
cross_attn_outputs = self.crossattention(
hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
)
attn_output = cross_attn_outputs[0]
# residual connection
hidden_states = residual + attn_output
outputs = outputs + cross_attn_outputs[2:] # add cross attentions if we output attention weights
residual = hidden_states
hidden_states = self.ln_2(hidden_states)
feed_forward_hidden_states = self.mlp(hidden_states)
# residual connection
hidden_states = residual + feed_forward_hidden_states
outputs = (hidden_states,) + (outputs if use_cache else outputs[1:])
return outputs # hidden_states, present, (attentions, cross_attentions)
class ImageGPTPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ImageGPTConfig
load_tf_weights = load_tf_weights_in_imagegpt
base_model_prefix = "transformer"
main_input_name = "input_ids"
supports_gradient_checkpointing = True
def __init__(self, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, (nn.Linear, Conv1D)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, ImageGPTLayerNorm):
module.weight.data.fill_(1.0)
# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
# > -- GPT-2 :: https://openai.com/blog/better-language-models/
#
# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
for name, p in module.named_parameters():
if "c_proj" in name and "weight" in name:
# Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
p.data.normal_(mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer)))
IMAGEGPT_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`ImageGPTConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
IMAGEGPT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
`input_ids_length` = `sequence_length` if `past_key_values` is `None` else
`past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input
sequence tokens in the vocabulary.
If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
`input_ids`.
Indices can be obtained using [`AutoImageProcessor`]. See [`ImageGPTImageProcessor.__call__`] for details.
past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.n_layers`):
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
`past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have
their past given to this model should not be passed as `input_ids` as they have already been computed.
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
`past_key_values`).
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare ImageGPT Model transformer outputting raw hidden-states without any specific head on top.",
IMAGEGPT_START_DOCSTRING,
)
class ImageGPTModel(ImageGPTPreTrainedModel):
def __init__(self, config: ImageGPTConfig):
super().__init__(config)
self.embed_dim = config.hidden_size
self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
self.drop = nn.Dropout(config.embd_pdrop)
self.h = nn.ModuleList([ImageGPTBlock(config, layer_idx=i) for i in range(config.num_hidden_layers)])
self.ln_f = ImageGPTLayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
# Model parallel
self.model_parallel = False
self.device_map = None
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.wte
def set_input_embeddings(self, new_embeddings):
self.wte = new_embeddings
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
"""
for layer, heads in heads_to_prune.items():
self.h[layer].attn.prune_heads(heads)
@add_start_docstrings_to_model_forward(IMAGEGPT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs: Any,
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, ImageGPTModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("openai/imagegpt-small")
>>> model = ImageGPTModel.from_pretrained("openai/imagegpt-small")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
```"""
if "pixel_values" in kwargs:
warnings.warn(
"The `pixel_values` argument is deprecated and will be removed in a future version, use `input_ids`"
" instead.",
FutureWarning,
)
if input_ids is not None:
raise ValueError(
"You cannot pass both `pixel_values` and `input_ids`. Please make sure to only pass `input_ids`."
)
input_ids = kwargs.pop("pixel_values")
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size = inputs_embeds.shape[0]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * len(self.h))
else:
past_length = past_key_values[0][0].size(-2)
if position_ids is None:
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0)
# ImageGPTAttention mask.
if attention_mask is not None:
if batch_size <= 0:
raise ValueError("batch_size has to be defined and > 0")
attention_mask = attention_mask.view(batch_size, -1)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask = attention_mask[:, None, None, :]
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and the dtype's smallest value for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.add_cross_attention and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# head_mask has shape n_layer x batch x n_heads x N x N
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
hidden_states = inputs_embeds + position_embeds
if token_type_ids is not None:
token_type_embeds = self.wte(token_type_ids)
hidden_states = hidden_states + token_type_embeds
hidden_states = self.drop(hidden_states)
output_shape = input_shape + (hidden_states.size(-1),)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
all_hidden_states = () if output_hidden_states else None
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
# Model parallel
if self.model_parallel:
torch.cuda.set_device(hidden_states.device)
# Ensure layer_past is on same device as hidden_states (might not be correct)
if layer_past is not None:
layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past)
# Ensure that attention_mask is always on the same device as hidden_states
if attention_mask is not None:
attention_mask = attention_mask.to(hidden_states.device)
if isinstance(head_mask, torch.Tensor):
head_mask = head_mask.to(hidden_states.device)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
outputs = self._gradient_checkpointing_func(
block.__call__,
hidden_states,
None,
attention_mask,
head_mask[i],
encoder_hidden_states,
encoder_attention_mask,
use_cache,
output_attentions,
)
else:
outputs = block(
hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask[i],
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)
# Model Parallel: If it's the last layer for that device, put things on the next device
if self.model_parallel:
for k, v in self.device_map.items():
if i == v[-1] and "cuda:" + str(k) != self.last_device:
hidden_states = hidden_states.to("cuda:" + str(k + 1))
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(*output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
@add_start_docstrings(
"""
The ImageGPT Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
""",
IMAGEGPT_START_DOCSTRING,
)
class ImageGPTForCausalImageModeling(ImageGPTPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: ImageGPTConfig):
super().__init__(config)
self.transformer = ImageGPTModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size - 1, bias=False)
# Model parallel
self.model_parallel = False
self.device_map = None
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def prepare_inputs_for_generation(self, input_ids: torch.Tensor, past_key_values: Optional[bool] = None, **kwargs):
token_type_ids = kwargs.get("token_type_ids", None)
# Omit tokens covered by past_key_values
if past_key_values:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_length:]
if token_type_ids is not None:
token_type_ids = token_type_ids[:, -input_ids.shape[1] :]
attention_mask = kwargs.get("attention_mask", None)
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_ids[:, -input_ids.shape[1] :]
else:
position_ids = None
return {
"input_ids": input_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"position_ids": position_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
@add_start_docstrings_to_model_forward(IMAGEGPT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs: Any,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, ImageGPTForCausalImageModeling
>>> import torch
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> image_processor = AutoImageProcessor.from_pretrained("openai/imagegpt-small")
>>> model = ImageGPTForCausalImageModeling.from_pretrained("openai/imagegpt-small")
>>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
>>> model.to(device) # doctest: +IGNORE_RESULT
>>> # unconditional generation of 8 images
>>> batch_size = 4
>>> context = torch.full((batch_size, 1), model.config.vocab_size - 1) # initialize with SOS token
>>> context = context.to(device)
>>> output = model.generate(
... input_ids=context, max_length=model.config.n_positions + 1, temperature=1.0, do_sample=True, top_k=40
... )
>>> clusters = image_processor.clusters
>>> height = image_processor.size["height"]
>>> width = image_processor.size["width"]
>>> samples = output[:, 1:].cpu().detach().numpy()
>>> samples_img = [
... np.reshape(np.rint(127.5 * (clusters[s] + 1.0)), [height, width, 3]).astype(np.uint8) for s in samples
... ] # convert color cluster tokens back to pixels
>>> f, axes = plt.subplots(1, batch_size, dpi=300)
>>> for img, ax in zip(samples_img, axes): # doctest: +IGNORE_RESULT
... ax.axis("off")
... ax.imshow(img)
```"""
if "pixel_values" in kwargs:
warnings.warn(
"The `pixel_values` argument is deprecated and will be removed in a future version, use `input_ids`"
" instead.",
FutureWarning,
)
if input_ids is not None:
raise ValueError(
"You cannot pass both `pixel_values` and `input_ids`. Please make sure to only pass `input_ids`."
)
input_ids = kwargs.pop("pixel_values")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
cross_attentions=transformer_outputs.cross_attentions,
)
@staticmethod
def _reorder_cache(
past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
) -> Tuple[Tuple[torch.Tensor]]:
"""
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
beam_idx at every generation step.
"""
return tuple(
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
for layer_past in past_key_values
)
@add_start_docstrings(
"""
The ImageGPT Model transformer with an image classification head on top (linear layer).
[`ImageGPTForImageClassification`] average-pools the hidden states in order to do the classification.
""",
IMAGEGPT_START_DOCSTRING,
)
class ImageGPTForImageClassification(ImageGPTPreTrainedModel):
def __init__(self, config: ImageGPTConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = ImageGPTModel(config)
self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(IMAGEGPT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=SequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs: Any,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, ImageGPTForImageClassification
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("openai/imagegpt-small")
>>> model = ImageGPTForImageClassification.from_pretrained("openai/imagegpt-small")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
```"""
if "pixel_values" in kwargs:
warnings.warn(
"The `pixel_values` argument is deprecated and will be removed in a future version, use `input_ids`"
" instead.",
FutureWarning,
)
if input_ids is not None:
raise ValueError(
"You cannot pass both `pixel_values` and `input_ids`. Please make sure to only pass `input_ids`."
)
input_ids = kwargs.pop("pixel_values")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
# average-pool the hidden states along the sequence dimension
pooled_hidden_states = hidden_states.mean(dim=1)
# project from (batch_size, hidden_size) to (batch_size, num_labels)
logits = self.score(pooled_hidden_states)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/layoutlm/modeling_layoutlm.py | # coding=utf-8
# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch LayoutLM model."""
import math
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
MaskedLMOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_layoutlm import LayoutLMConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "LayoutLMConfig"
_CHECKPOINT_FOR_DOC = "microsoft/layoutlm-base-uncased"
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = [
"layoutlm-base-uncased",
"layoutlm-large-uncased",
]
LayoutLMLayerNorm = nn.LayerNorm
class LayoutLMEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super(LayoutLMEmbeddings, self).__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
self.LayerNorm = LayoutLMLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
def forward(
self,
input_ids=None,
bbox=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
device = input_ids.device if input_ids is not None else inputs_embeds.device
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
words_embeddings = inputs_embeds
position_embeddings = self.position_embeddings(position_ids)
try:
left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2])
lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3])
except IndexError as e:
raise IndexError("The `bbox`coordinate values should be within 0-1000 range.") from e
h_position_embeddings = self.h_position_embeddings(bbox[:, :, 3] - bbox[:, :, 1])
w_position_embeddings = self.w_position_embeddings(bbox[:, :, 2] - bbox[:, :, 0])
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = (
words_embeddings
+ position_embeddings
+ left_position_embeddings
+ upper_position_embeddings
+ right_position_embeddings
+ lower_position_embeddings
+ h_position_embeddings
+ w_position_embeddings
+ token_type_embeddings
)
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->LayoutLM
class LayoutLMSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(
config, "position_embedding_type", "absolute"
)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
use_cache = past_key_value is not None
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
query_length, key_length = query_layer.shape[2], key_layer.shape[2]
if use_cache:
position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
-1, 1
)
else:
position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in LayoutLMModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->LayoutLM
class LayoutLMSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->LayoutLM
class LayoutLMAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = LayoutLMSelfAttention(config, position_embedding_type=position_embedding_type)
self.output = LayoutLMSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
class LayoutLMIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->LayoutLM
class LayoutLMOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->LayoutLM
class LayoutLMLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = LayoutLMAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
self.crossattention = LayoutLMAttention(config, position_embedding_type="absolute")
self.intermediate = LayoutLMIntermediate(config)
self.output = LayoutLMOutput(config)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and encoder_hidden_states is not None:
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
" by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
cross_attn_past_key_value,
output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->LayoutLM
class LayoutLMEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([LayoutLMLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_values[i] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertPooler
class LayoutLMPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->LayoutLM
class LayoutLMPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->LayoutLM
class LayoutLMLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = LayoutLMPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->LayoutLM
class LayoutLMOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = LayoutLMLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores
class LayoutLMPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LayoutLMConfig
pretrained_model_archive_map = LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST
base_model_prefix = "layoutlm"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, LayoutLMLayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
LAYOUTLM_START_DOCSTRING = r"""
The LayoutLM model was proposed in [LayoutLM: Pre-training of Text and Layout for Document Image
Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei and
Ming Zhou.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`LayoutLMConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
LAYOUTLM_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
bbox (`torch.LongTensor` of shape `({0}, 4)`, *optional*):
Bounding boxes of each input sequence tokens. Selected in the range `[0,
config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
y1) represents the position of the lower right corner. See [Overview](#Overview) for normalization.
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: `1` for
tokens that are NOT MASKED, `0` for MASKED tokens.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`: `0` corresponds to a *sentence A* token, `1` corresponds to a *sentence B* token
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: `1`
indicates the head is **not masked**, `0` indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
If set to `True`, the attentions tensors of all attention layers are returned. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
If set to `True`, the hidden states of all layers are returned. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
If set to `True`, the model will return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare LayoutLM Model transformer outputting raw hidden-states without any specific head on top.",
LAYOUTLM_START_DOCSTRING,
)
class LayoutLMModel(LayoutLMPreTrainedModel):
def __init__(self, config):
super(LayoutLMModel, self).__init__(config)
self.config = config
self.embeddings = LayoutLMEmbeddings(config)
self.encoder = LayoutLMEncoder(config)
self.pooler = LayoutLMPooler(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPoolingAndCrossAttentions]:
r"""
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, LayoutLMModel
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")
>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(" ".join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])
>>> outputs = model(
... input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids
... )
>>> last_hidden_states = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
if bbox is None:
bbox = torch.zeros(input_shape + (4,), dtype=torch.long, device=device)
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
elif head_mask.dim() == 2:
head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
head_mask = head_mask.to(dtype=next(self.parameters()).dtype)
else:
head_mask = [None] * self.config.num_hidden_layers
embedding_output = self.embeddings(
input_ids=input_ids,
bbox=bbox,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
encoder_outputs = self.encoder(
embedding_output,
extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output)
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
@add_start_docstrings("""LayoutLM Model with a `language modeling` head on top.""", LAYOUTLM_START_DOCSTRING)
class LayoutLMForMaskedLM(LayoutLMPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
self.layoutlm = LayoutLMModel(config)
self.cls = LayoutLMOnlyMLMHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.layoutlm.embeddings.word_embeddings
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, MaskedLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, LayoutLMForMaskedLM
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = LayoutLMForMaskedLM.from_pretrained("microsoft/layoutlm-base-uncased")
>>> words = ["Hello", "[MASK]"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(" ".join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])
>>> labels = tokenizer("Hello world", return_tensors="pt")["input_ids"]
>>> outputs = model(
... input_ids=input_ids,
... bbox=bbox,
... attention_mask=attention_mask,
... token_type_ids=token_type_ids,
... labels=labels,
... )
>>> loss = outputs.loss
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.layoutlm(
input_ids,
bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(
prediction_scores.view(-1, self.config.vocab_size),
labels.view(-1),
)
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
LayoutLM Model with a sequence classification head on top (a linear layer on top of the pooled output) e.g. for
document image classification tasks such as the [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset.
""",
LAYOUTLM_START_DOCSTRING,
)
class LayoutLMForSequenceClassification(LayoutLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.layoutlm = LayoutLMModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.layoutlm.embeddings.word_embeddings
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, LayoutLMForSequenceClassification
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased")
>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(" ".join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])
>>> sequence_label = torch.tensor([1])
>>> outputs = model(
... input_ids=input_ids,
... bbox=bbox,
... attention_mask=attention_mask,
... token_type_ids=token_type_ids,
... labels=sequence_label,
... )
>>> loss = outputs.loss
>>> logits = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
LayoutLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
sequence labeling (information extraction) tasks such as the [FUNSD](https://guillaumejaume.github.io/FUNSD/)
dataset and the [SROIE](https://rrc.cvc.uab.es/?ch=13) dataset.
""",
LAYOUTLM_START_DOCSTRING,
)
class LayoutLMForTokenClassification(LayoutLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.layoutlm = LayoutLMModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.layoutlm.embeddings.word_embeddings
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, LayoutLMForTokenClassification
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased")
>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(" ".join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])
>>> token_labels = torch.tensor([1, 1, 0, 0]).unsqueeze(0) # batch size of 1
>>> outputs = model(
... input_ids=input_ids,
... bbox=bbox,
... attention_mask=attention_mask,
... token_type_ids=token_type_ids,
... labels=token_labels,
... )
>>> loss = outputs.loss
>>> logits = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
LayoutLM Model with a span classification head on top for extractive question-answering tasks such as
[DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the final hidden-states output to compute `span
start logits` and `span end logits`).
""",
LAYOUTLM_START_DOCSTRING,
)
class LayoutLMForQuestionAnswering(LayoutLMPreTrainedModel):
def __init__(self, config, has_visual_segment_embedding=True):
super().__init__(config)
self.num_labels = config.num_labels
self.layoutlm = LayoutLMModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.layoutlm.embeddings.word_embeddings
@replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
Returns:
Example:
In the example below, we prepare a question + context pair for the LayoutLM model. It will give us a prediction
of what it thinks the answer is (the span of the answer within the texts parsed from the image).
```python
>>> from transformers import AutoTokenizer, LayoutLMForQuestionAnswering
>>> from datasets import load_dataset
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa", add_prefix_space=True)
>>> model = LayoutLMForQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="1e3ebac")
>>> dataset = load_dataset("nielsr/funsd", split="train")
>>> example = dataset[0]
>>> question = "what's his name?"
>>> words = example["words"]
>>> boxes = example["bboxes"]
>>> encoding = tokenizer(
... question.split(), words, is_split_into_words=True, return_token_type_ids=True, return_tensors="pt"
... )
>>> bbox = []
>>> for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)):
... if s == 1:
... bbox.append(boxes[w])
... elif i == tokenizer.sep_token_id:
... bbox.append([1000] * 4)
... else:
... bbox.append([0] * 4)
>>> encoding["bbox"] = torch.tensor([bbox])
>>> word_ids = encoding.word_ids(0)
>>> outputs = model(**encoding)
>>> loss = outputs.loss
>>> start_scores = outputs.start_logits
>>> end_scores = outputs.end_logits
>>> start, end = word_ids[start_scores.argmax(-1)], word_ids[end_scores.argmax(-1)]
>>> print(" ".join(words[start : end + 1]))
M. Hamann P. Harper, P. Martinez
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | # coding=utf-8
# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 LayoutLM model."""
from __future__ import annotations
import math
import warnings
from typing import Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutputWithPastAndCrossAttentions,
TFBaseModelOutputWithPoolingAndCrossAttentions,
TFMaskedLMOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTokenClassifierOutput,
)
from ...modeling_tf_utils import (
TFMaskedLanguageModelingLoss,
TFModelInputType,
TFPreTrainedModel,
TFQuestionAnsweringLoss,
TFSequenceClassificationLoss,
TFTokenClassificationLoss,
get_initializer,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_layoutlm import LayoutLMConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "LayoutLMConfig"
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = [
"microsoft/layoutlm-base-uncased",
"microsoft/layoutlm-large-uncased",
]
class TFLayoutLMEmbeddings(tf.keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.max_position_embeddings = config.max_position_embeddings
self.max_2d_position_embeddings = config.max_2d_position_embeddings
self.initializer_range = config.initializer_range
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
def build(self, input_shape=None):
with tf.name_scope("word_embeddings"):
self.weight = self.add_weight(
name="weight",
shape=[self.config.vocab_size, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_scope("token_type_embeddings"):
self.token_type_embeddings = self.add_weight(
name="embeddings",
shape=[self.config.type_vocab_size, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_scope("position_embeddings"):
self.position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_position_embeddings, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_scope("x_position_embeddings"):
self.x_position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_2d_position_embeddings, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_scope("y_position_embeddings"):
self.y_position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_2d_position_embeddings, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_scope("h_position_embeddings"):
self.h_position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_2d_position_embeddings, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_scope("w_position_embeddings"):
self.w_position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_2d_position_embeddings, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
if self.built:
return
self.built = True
if getattr(self, "LayerNorm", None) is not None:
with tf.name_scope(self.LayerNorm.name):
self.LayerNorm.build([None, None, self.config.hidden_size])
def call(
self,
input_ids: tf.Tensor = None,
bbox: tf.Tensor = None,
position_ids: tf.Tensor = None,
token_type_ids: tf.Tensor = None,
inputs_embeds: tf.Tensor = None,
training: bool = False,
) -> tf.Tensor:
"""
Applies embedding based on inputs tensor.
Returns:
final_embeddings (`tf.Tensor`): output embedding tensor.
"""
assert not (input_ids is None and inputs_embeds is None)
if input_ids is not None:
check_embeddings_within_bounds(input_ids, self.config.vocab_size)
inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
input_shape = shape_list(inputs_embeds)[:-1]
if token_type_ids is None:
token_type_ids = tf.fill(dims=input_shape, value=0)
if position_ids is None:
position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
if position_ids is None:
position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
if bbox is None:
bbox = bbox = tf.fill(input_shape + [4], value=0)
try:
left_position_embeddings = tf.gather(self.x_position_embeddings, bbox[:, :, 0])
upper_position_embeddings = tf.gather(self.y_position_embeddings, bbox[:, :, 1])
right_position_embeddings = tf.gather(self.x_position_embeddings, bbox[:, :, 2])
lower_position_embeddings = tf.gather(self.y_position_embeddings, bbox[:, :, 3])
except IndexError as e:
raise IndexError("The `bbox`coordinate values should be within 0-1000 range.") from e
h_position_embeddings = tf.gather(self.h_position_embeddings, bbox[:, :, 3] - bbox[:, :, 1])
w_position_embeddings = tf.gather(self.w_position_embeddings, bbox[:, :, 2] - bbox[:, :, 0])
position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
final_embeddings = (
inputs_embeds
+ position_embeds
+ token_type_embeds
+ left_position_embeddings
+ upper_position_embeddings
+ right_position_embeddings
+ lower_position_embeddings
+ h_position_embeddings
+ w_position_embeddings
)
final_embeddings = self.LayerNorm(inputs=final_embeddings)
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
return final_embeddings
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->LayoutLM
class TFLayoutLMSelfAttention(tf.keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number "
f"of attention heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
self.query = tf.keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
)
self.key = tf.keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
)
self.value = tf.keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
)
self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
self.is_decoder = config.is_decoder
self.config = config
def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
# Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
# Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
return tf.transpose(tensor, perm=[0, 2, 1, 3])
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor,
encoder_attention_mask: tf.Tensor,
past_key_value: Tuple[tf.Tensor],
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
batch_size = shape_list(hidden_states)[0]
mixed_query_layer = self.query(inputs=hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(inputs=encoder_hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=encoder_hidden_states), batch_size)
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size)
key_layer = tf.concat([past_key_value[0], key_layer], axis=2)
value_layer = tf.concat([past_key_value[1], value_layer], axis=2)
else:
key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
if self.is_decoder:
# if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
attention_scores = tf.divide(attention_scores, dk)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in TFLayoutLMModel call() function)
attention_scores = tf.add(attention_scores, attention_mask)
# Normalize the attention scores to probabilities.
attention_probs = stable_softmax(logits=attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(inputs=attention_probs, training=training)
# Mask heads if we want to
if head_mask is not None:
attention_probs = tf.multiply(attention_probs, head_mask)
attention_output = tf.matmul(attention_probs, value_layer)
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
# (batch_size, seq_len_q, all_head_size)
attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "query", None) is not None:
with tf.name_scope(self.query.name):
self.query.build([None, None, self.config.hidden_size])
if getattr(self, "key", None) is not None:
with tf.name_scope(self.key.name):
self.key.build([None, None, self.config.hidden_size])
if getattr(self, "value", None) is not None:
with tf.name_scope(self.value.name):
self.value.build([None, None, self.config.hidden_size])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->LayoutLM
class TFLayoutLMSelfOutput(tf.keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.config = config
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
if getattr(self, "LayerNorm", None) is not None:
with tf.name_scope(self.LayerNorm.name):
self.LayerNorm.build([None, None, self.config.hidden_size])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->LayoutLM
class TFLayoutLMAttention(tf.keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFLayoutLMSelfAttention(config, name="self")
self.dense_output = TFLayoutLMSelfOutput(config, name="output")
def prune_heads(self, heads):
raise NotImplementedError
def call(
self,
input_tensor: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor,
encoder_attention_mask: tf.Tensor,
past_key_value: Tuple[tf.Tensor],
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
self_outputs = self.self_attention(
hidden_states=input_tensor,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_value=past_key_value,
output_attentions=output_attentions,
training=training,
)
attention_output = self.dense_output(
hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
)
# add attentions (possibly with past_key_value) if we output them
outputs = (attention_output,) + self_outputs[1:]
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self_attention", None) is not None:
with tf.name_scope(self.self_attention.name):
self.self_attention.build(None)
if getattr(self, "dense_output", None) is not None:
with tf.name_scope(self.dense_output.name):
self.dense_output.build(None)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->LayoutLM
class TFLayoutLMIntermediate(tf.keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = get_tf_activation(config.hidden_act)
else:
self.intermediate_act_fn = config.hidden_act
self.config = config
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->LayoutLM
class TFLayoutLMOutput(tf.keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.config = config
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.intermediate_size])
if getattr(self, "LayerNorm", None) is not None:
with tf.name_scope(self.LayerNorm.name):
self.LayerNorm.build([None, None, self.config.hidden_size])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->LayoutLM
class TFLayoutLMLayer(tf.keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFLayoutLMAttention(config, name="attention")
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
self.crossattention = TFLayoutLMAttention(config, name="crossattention")
self.intermediate = TFLayoutLMIntermediate(config, name="intermediate")
self.bert_output = TFLayoutLMOutput(config, name="output")
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor | None,
encoder_attention_mask: tf.Tensor | None,
past_key_value: Tuple[tf.Tensor] | None,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
input_tensor=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=self_attn_past_key_value,
output_attentions=output_attentions,
training=training,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and encoder_hidden_states is not None:
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
" by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
input_tensor=attention_output,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
training=training,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
intermediate_output = self.intermediate(hidden_states=attention_output)
layer_output = self.bert_output(
hidden_states=intermediate_output, input_tensor=attention_output, training=training
)
outputs = (layer_output,) + outputs # add attentions if we output them
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "attention", None) is not None:
with tf.name_scope(self.attention.name):
self.attention.build(None)
if getattr(self, "intermediate", None) is not None:
with tf.name_scope(self.intermediate.name):
self.intermediate.build(None)
if getattr(self, "bert_output", None) is not None:
with tf.name_scope(self.bert_output.name):
self.bert_output.build(None)
if getattr(self, "crossattention", None) is not None:
with tf.name_scope(self.crossattention.name):
self.crossattention.build(None)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->LayoutLM
class TFLayoutLMEncoder(tf.keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.layer = [TFLayoutLMLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor | None,
encoder_attention_mask: tf.Tensor | None,
past_key_values: Tuple[Tuple[tf.Tensor]] | None,
use_cache: Optional[bool],
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
past_key_value = past_key_values[i] if past_key_values is not None else None
layer_outputs = layer_module(
hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask[i],
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_value=past_key_value,
output_attentions=output_attentions,
training=training,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if self.config.add_cross_attention and encoder_hidden_states is not None:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v for v in [hidden_states, all_hidden_states, all_attentions, all_cross_attentions] if v is not None
)
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_attentions,
cross_attentions=all_cross_attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layer", None) is not None:
for layer in self.layer:
with tf.name_scope(layer.name):
layer.build(None)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->LayoutLM
class TFLayoutLMPooler(tf.keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
name="dense",
)
self.config = config
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(inputs=first_token_tensor)
return pooled_output
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->LayoutLM
class TFLayoutLMPredictionHeadTransform(tf.keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name="dense",
)
if isinstance(config.hidden_act, str):
self.transform_act_fn = get_tf_activation(config.hidden_act)
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.config = config
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(inputs=hidden_states)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
if getattr(self, "LayerNorm", None) is not None:
with tf.name_scope(self.LayerNorm.name):
self.LayerNorm.build([None, None, self.config.hidden_size])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->LayoutLM
class TFLayoutLMLMPredictionHead(tf.keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.transform = TFLayoutLMPredictionHeadTransform(config, name="transform")
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.input_embeddings = input_embeddings
def build(self, input_shape=None):
self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
if self.built:
return
self.built = True
if getattr(self, "transform", None) is not None:
with tf.name_scope(self.transform.name):
self.transform.build(None)
def get_output_embeddings(self) -> tf.keras.layers.Layer:
return self.input_embeddings
def set_output_embeddings(self, value: tf.Variable):
self.input_embeddings.weight = value
self.input_embeddings.vocab_size = shape_list(value)[0]
def get_bias(self) -> Dict[str, tf.Variable]:
return {"bias": self.bias}
def set_bias(self, value: tf.Variable):
self.bias = value["bias"]
self.config.vocab_size = shape_list(value["bias"])[0]
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.transform(hidden_states=hidden_states)
seq_length = shape_list(hidden_states)[1]
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size])
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
return hidden_states
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->LayoutLM
class TFLayoutLMMLMHead(tf.keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.predictions = TFLayoutLMLMPredictionHead(config, input_embeddings, name="predictions")
def call(self, sequence_output: tf.Tensor) -> tf.Tensor:
prediction_scores = self.predictions(hidden_states=sequence_output)
return prediction_scores
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "predictions", None) is not None:
with tf.name_scope(self.predictions.name):
self.predictions.build(None)
@keras_serializable
class TFLayoutLMMainLayer(tf.keras.layers.Layer):
config_class = LayoutLMConfig
def __init__(self, config: LayoutLMConfig, add_pooling_layer: bool = True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embeddings = TFLayoutLMEmbeddings(config, name="embeddings")
self.encoder = TFLayoutLMEncoder(config, name="encoder")
self.pooler = TFLayoutLMPooler(config, name="pooler") if add_pooling_layer else None
def get_input_embeddings(self) -> tf.keras.layers.Layer:
return self.embeddings
def set_input_embeddings(self, value: tf.Variable):
self.embeddings.weight = value
self.embeddings.vocab_size = shape_list(value)[0]
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
raise NotImplementedError
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
bbox: np.ndarray | tf.Tensor | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]:
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if attention_mask is None:
attention_mask = tf.fill(dims=input_shape, value=1)
if token_type_ids is None:
token_type_ids = tf.fill(dims=input_shape, value=0)
if bbox is None:
bbox = tf.fill(dims=input_shape + [4], value=0)
embedding_output = self.embeddings(
input_ids=input_ids,
bbox=bbox,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
training=training,
)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
extended_attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1]))
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype)
one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.config.num_hidden_layers
encoder_outputs = self.encoder(
hidden_states=embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
# Need to pass these required positional arguments to `Encoder`
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=None,
past_key_values=None,
use_cache=False,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None
if not return_dict:
return (
sequence_output,
pooled_output,
) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embeddings", None) is not None:
with tf.name_scope(self.embeddings.name):
self.embeddings.build(None)
if getattr(self, "encoder", None) is not None:
with tf.name_scope(self.encoder.name):
self.encoder.build(None)
if getattr(self, "pooler", None) is not None:
with tf.name_scope(self.pooler.name):
self.pooler.build(None)
class TFLayoutLMPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LayoutLMConfig
base_model_prefix = "layoutlm"
LAYOUTLM_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
<Tip>
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Note that when creating models and layers with
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
</Tip>
Args:
config ([`LayoutLMConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""
LAYOUTLM_INPUTS_DOCSTRING = r"""
Args:
input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
[`PreTrainedTokenizer.encode`] for details.
[What are input IDs?](../glossary#input-ids)
bbox (`Numpy array` or `tf.Tensor` of shape `({0}, 4)`, *optional*):
Bounding Boxes of each input sequence tokens. Selected in the range `[0, config.max_2d_position_embeddings-
1]`.
attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@add_start_docstrings(
"The bare LayoutLM Model transformer outputting raw hidden-states without any specific head on top.",
LAYOUTLM_START_DOCSTRING,
)
class TFLayoutLMModel(TFLayoutLMPreTrainedModel):
def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.layoutlm = TFLayoutLMMainLayer(config, name="layoutlm")
@unpack_inputs
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(
output_type=TFBaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC
)
def call(
self,
input_ids: TFModelInputType | None = None,
bbox: np.ndarray | tf.Tensor | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]:
r"""
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, TFLayoutLMModel
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")
>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(" ".join(words), return_tensors="tf")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = tf.convert_to_tensor([token_boxes])
>>> outputs = model(
... input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids
... )
>>> last_hidden_states = outputs.last_hidden_state
```"""
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layoutlm", None) is not None:
with tf.name_scope(self.layoutlm.name):
self.layoutlm.build(None)
@add_start_docstrings("""LayoutLM Model with a `language modeling` head on top.""", LAYOUTLM_START_DOCSTRING)
class TFLayoutLMForMaskedLM(TFLayoutLMPreTrainedModel, TFMaskedLanguageModelingLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"pooler",
r"cls.seq_relationship",
r"cls.predictions.decoder.weight",
r"nsp___cls",
]
def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
if config.is_decoder:
logger.warning(
"If you want to use `TFLayoutLMForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
self.layoutlm = TFLayoutLMMainLayer(config, add_pooling_layer=True, name="layoutlm")
self.mlm = TFLayoutLMMLMHead(config, input_embeddings=self.layoutlm.embeddings, name="mlm___cls")
def get_lm_head(self) -> tf.keras.layers.Layer:
return self.mlm.predictions
def get_prefix_bias_name(self) -> str:
warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
@unpack_inputs
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
bbox: np.ndarray | tf.Tensor | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, TFLayoutLMForMaskedLM
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = TFLayoutLMForMaskedLM.from_pretrained("microsoft/layoutlm-base-uncased")
>>> words = ["Hello", "[MASK]"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(" ".join(words), return_tensors="tf")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = tf.convert_to_tensor([token_boxes])
>>> labels = tokenizer("Hello world", return_tensors="tf")["input_ids"]
>>> outputs = model(
... input_ids=input_ids,
... bbox=bbox,
... attention_mask=attention_mask,
... token_type_ids=token_type_ids,
... labels=labels,
... )
>>> loss = outputs.loss
```"""
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
prediction_scores = self.mlm(sequence_output=sequence_output, training=training)
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=prediction_scores)
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFMaskedLMOutput(
loss=loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layoutlm", None) is not None:
with tf.name_scope(self.layoutlm.name):
self.layoutlm.build(None)
if getattr(self, "mlm", None) is not None:
with tf.name_scope(self.mlm.name):
self.mlm.build(None)
@add_start_docstrings(
"""
LayoutLM Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
""",
LAYOUTLM_START_DOCSTRING,
)
class TFLayoutLMForSequenceClassification(TFLayoutLMPreTrainedModel, TFSequenceClassificationLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"mlm___cls", r"nsp___cls", r"cls.predictions", r"cls.seq_relationship"]
_keys_to_ignore_on_load_missing = [r"dropout"]
def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.layoutlm = TFLayoutLMMainLayer(config, name="layoutlm")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
units=config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name="classifier",
)
self.config = config
@unpack_inputs
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
bbox: np.ndarray | tf.Tensor | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, TFLayoutLMForSequenceClassification
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased")
>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(" ".join(words), return_tensors="tf")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = tf.convert_to_tensor([token_boxes])
>>> sequence_label = tf.convert_to_tensor([1])
>>> outputs = model(
... input_ids=input_ids,
... bbox=bbox,
... attention_mask=attention_mask,
... token_type_ids=token_type_ids,
... labels=sequence_label,
... )
>>> loss = outputs.loss
>>> logits = outputs.logits
```"""
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
pooled_output = outputs[1]
pooled_output = self.dropout(inputs=pooled_output, training=training)
logits = self.classifier(inputs=pooled_output)
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layoutlm", None) is not None:
with tf.name_scope(self.layoutlm.name):
self.layoutlm.build(None)
if getattr(self, "classifier", None) is not None:
with tf.name_scope(self.classifier.name):
self.classifier.build([None, None, self.config.hidden_size])
@add_start_docstrings(
"""
LayoutLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
LAYOUTLM_START_DOCSTRING,
)
class TFLayoutLMForTokenClassification(TFLayoutLMPreTrainedModel, TFTokenClassificationLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"pooler",
r"mlm___cls",
r"nsp___cls",
r"cls.predictions",
r"cls.seq_relationship",
]
_keys_to_ignore_on_load_missing = [r"dropout"]
def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.layoutlm = TFLayoutLMMainLayer(config, add_pooling_layer=True, name="layoutlm")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
units=config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name="classifier",
)
self.config = config
@unpack_inputs
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFTokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
bbox: np.ndarray | tf.Tensor | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
Returns:
Examples:
```python
>>> import tensorflow as tf
>>> from transformers import AutoTokenizer, TFLayoutLMForTokenClassification
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased")
>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(" ".join(words), return_tensors="tf")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = tf.convert_to_tensor([token_boxes])
>>> token_labels = tf.convert_to_tensor([1, 1, 0, 0])
>>> outputs = model(
... input_ids=input_ids,
... bbox=bbox,
... attention_mask=attention_mask,
... token_type_ids=token_type_ids,
... labels=token_labels,
... )
>>> loss = outputs.loss
>>> logits = outputs.logits
```"""
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
sequence_output = self.dropout(inputs=sequence_output, training=training)
logits = self.classifier(inputs=sequence_output)
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFTokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layoutlm", None) is not None:
with tf.name_scope(self.layoutlm.name):
self.layoutlm.build(None)
if getattr(self, "classifier", None) is not None:
with tf.name_scope(self.classifier.name):
self.classifier.build([None, None, self.config.hidden_size])
@add_start_docstrings(
"""
LayoutLM Model with a span classification head on top for extractive question-answering tasks such as
[DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the final hidden-states output to compute `span
start logits` and `span end logits`).
""",
LAYOUTLM_START_DOCSTRING,
)
class TFLayoutLMForQuestionAnswering(TFLayoutLMPreTrainedModel, TFQuestionAnsweringLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"pooler",
r"mlm___cls",
r"nsp___cls",
r"cls.predictions",
r"cls.seq_relationship",
]
def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.layoutlm = TFLayoutLMMainLayer(config, add_pooling_layer=True, name="layoutlm")
self.qa_outputs = tf.keras.layers.Dense(
units=config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name="qa_outputs",
)
self.config = config
@unpack_inputs
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
bbox: np.ndarray | tf.Tensor | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
start_positions: np.ndarray | tf.Tensor | None = None,
end_positions: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
r"""
start_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
Returns:
Examples:
```python
>>> import tensorflow as tf
>>> from transformers import AutoTokenizer, TFLayoutLMForQuestionAnswering
>>> from datasets import load_dataset
>>> tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa", add_prefix_space=True)
>>> model = TFLayoutLMForQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="1e3ebac")
>>> dataset = load_dataset("nielsr/funsd", split="train")
>>> example = dataset[0]
>>> question = "what's his name?"
>>> words = example["words"]
>>> boxes = example["bboxes"]
>>> encoding = tokenizer(
... question.split(), words, is_split_into_words=True, return_token_type_ids=True, return_tensors="tf"
... )
>>> bbox = []
>>> for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)):
... if s == 1:
... bbox.append(boxes[w])
... elif i == tokenizer.sep_token_id:
... bbox.append([1000] * 4)
... else:
... bbox.append([0] * 4)
>>> encoding["bbox"] = tf.convert_to_tensor([bbox])
>>> word_ids = encoding.word_ids(0)
>>> outputs = model(**encoding)
>>> loss = outputs.loss
>>> start_scores = outputs.start_logits
>>> end_scores = outputs.end_logits
>>> start, end = word_ids[tf.math.argmax(start_scores, -1)[0]], word_ids[tf.math.argmax(end_scores, -1)[0]]
>>> print(" ".join(words[start : end + 1]))
M. Hamann P. Harper, P. Martinez
```"""
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
logits = self.qa_outputs(inputs=sequence_output)
start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1)
start_logits = tf.squeeze(input=start_logits, axis=-1)
end_logits = tf.squeeze(input=end_logits, axis=-1)
loss = None
if start_positions is not None and end_positions is not None:
labels = {"start_position": start_positions}
labels["end_position"] = end_positions
loss = self.hf_compute_loss(labels=labels, logits=(start_logits, end_logits))
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFQuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layoutlm", None) is not None:
with tf.name_scope(self.layoutlm.name):
self.layoutlm.build(None)
if getattr(self, "qa_outputs", None) is not None:
with tf.name_scope(self.qa_outputs.name):
self.qa_outputs.build([None, None, self.config.hidden_size])
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/layoutlm/__init__.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
"configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
"tokenization_layoutlm": ["LayoutLMTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_layoutlm_fast"] = ["LayoutLMTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_layoutlm"] = [
"LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"LayoutLMForMaskedLM",
"LayoutLMForSequenceClassification",
"LayoutLMForTokenClassification",
"LayoutLMForQuestionAnswering",
"LayoutLMModel",
"LayoutLMPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_layoutlm"] = [
"TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFLayoutLMForMaskedLM",
"TFLayoutLMForSequenceClassification",
"TFLayoutLMForTokenClassification",
"TFLayoutLMForQuestionAnswering",
"TFLayoutLMMainLayer",
"TFLayoutLMModel",
"TFLayoutLMPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
from .tokenization_layoutlm import LayoutLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlm import (
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMForMaskedLM,
LayoutLMForQuestionAnswering,
LayoutLMForSequenceClassification,
LayoutLMForTokenClassification,
LayoutLMModel,
LayoutLMPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMMainLayer,
TFLayoutLMModel,
TFLayoutLMPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/layoutlm/configuration_layoutlm.py | # coding=utf-8
# Copyright 2010, The Microsoft Research Asia LayoutLM Team authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" LayoutLM model configuration"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging
logger = logging.get_logger(__name__)
LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/layoutlm-base-uncased": (
"https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"
),
"microsoft/layoutlm-large-uncased": (
"https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"
),
}
class LayoutLMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LayoutLMModel`]. It is used to instantiate a
LayoutLM model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the LayoutLM
[microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) architecture.
Configuration objects inherit from [`BertConfig`] and can be used to control the model outputs. Read the
documentation from [`BertConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the LayoutLM model. Defines the different tokens that can be represented by the
*inputs_ids* passed to the forward method of [`LayoutLMModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed into [`LayoutLMModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 0):
The value used to pad input_ids.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum value that the 2D position embedding might ever used. Typically set this to something large
just in case (e.g., 1024).
Examples:
```python
>>> from transformers import LayoutLMConfig, LayoutLMModel
>>> # Initializing a LayoutLM configuration
>>> configuration = LayoutLMConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = LayoutLMModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "layoutlm"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
position_embedding_type="absolute",
use_cache=True,
max_2d_position_embeddings=1024,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.use_cache = use_cache
self.max_2d_position_embeddings = max_2d_position_embeddings
class LayoutLMOnnxConfig(OnnxConfig):
def __init__(
self,
config: PretrainedConfig,
task: str = "default",
patching_specs: List[PatchingSpec] = None,
):
super().__init__(config, task=task, patching_specs=patching_specs)
self.max_2d_positions = config.max_2d_position_embeddings - 1
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("bbox", {0: "batch", 1: "sequence"}),
("attention_mask", {0: "batch", 1: "sequence"}),
("token_type_ids", {0: "batch", 1: "sequence"}),
]
)
def generate_dummy_inputs(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
"""
Generate inputs to provide to the ONNX exporter for the specific framework
Args:
tokenizer: The tokenizer associated with this model configuration
batch_size: The batch size (int) to export the model for (-1 means dynamic axis)
seq_length: The sequence length (int) to export the model for (-1 means dynamic axis)
is_pair: Indicate if the input is a pair (sentence 1, sentence 2)
framework: The framework (optional) the tokenizer will generate tensor for
Returns:
Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
"""
input_dict = super().generate_dummy_inputs(
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
)
# Generate a dummy bbox
box = [48, 84, 73, 128]
if not framework == TensorType.PYTORCH:
raise NotImplementedError("Exporting LayoutLM to ONNX is currently only supported for PyTorch.")
if not is_torch_available():
raise ValueError("Cannot generate dummy inputs without PyTorch installed.")
import torch
batch_size, seq_length = input_dict["input_ids"].shape
input_dict["bbox"] = torch.tensor([*[box] * seq_length]).tile(batch_size, 1, 1)
return input_dict
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py | # coding=utf-8
# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tokenization class for model LayoutLM."""
import collections
import os
import unicodedata
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"microsoft/layoutlm-base-uncased": (
"https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt"
),
"microsoft/layoutlm-large-uncased": (
"https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt"
),
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"microsoft/layoutlm-base-uncased": 512,
"microsoft/layoutlm-large-uncased": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"microsoft/layoutlm-base-uncased": {"do_lower_case": True},
"microsoft/layoutlm-large-uncased": {"do_lower_case": True},
}
# Copied from transformers.models.bert.tokenization_bert.load_vocab
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
with open(vocab_file, "r", encoding="utf-8") as reader:
tokens = reader.readlines()
for index, token in enumerate(tokens):
token = token.rstrip("\n")
vocab[token] = index
return vocab
# Copied from transformers.models.bert.tokenization_bert.whitespace_tokenize
def whitespace_tokenize(text):
"""Runs basic whitespace cleaning and splitting on a piece of text."""
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens
# Copied from transformers.models.bert.tokenization_bert.BertTokenizer with Bert->LayoutLM,BERT->LayoutLM
class LayoutLMTokenizer(PreTrainedTokenizer):
r"""
Construct a LayoutLM tokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
do_basic_tokenize (`bool`, *optional*, defaults to `True`):
Whether or not to do basic tokenization before WordPiece.
never_split (`Iterable`, *optional*):
Collection of tokens which will never be split during tokenization. Only has an effect when
`do_basic_tokenize=True`
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
Whether or not to tokenize Chinese characters.
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original LayoutLM).
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(
self,
vocab_file,
do_lower_case=True,
do_basic_tokenize=True,
never_split=None,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
tokenize_chinese_chars=True,
strip_accents=None,
**kwargs,
):
if not os.path.isfile(vocab_file):
raise ValueError(
f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
" model use `tokenizer = LayoutLMTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
)
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
self.do_basic_tokenize = do_basic_tokenize
if do_basic_tokenize:
self.basic_tokenizer = BasicTokenizer(
do_lower_case=do_lower_case,
never_split=never_split,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))
super().__init__(
do_lower_case=do_lower_case,
do_basic_tokenize=do_basic_tokenize,
never_split=never_split,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
**kwargs,
)
@property
def do_lower_case(self):
return self.basic_tokenizer.do_lower_case
@property
def vocab_size(self):
return len(self.vocab)
def get_vocab(self):
return dict(self.vocab, **self.added_tokens_encoder)
def _tokenize(self, text, split_special_tokens=False):
split_tokens = []
if self.do_basic_tokenize:
for token in self.basic_tokenizer.tokenize(
text, never_split=self.all_special_tokens if not split_special_tokens else None
):
# If the token is part of the never_split set
if token in self.basic_tokenizer.never_split:
split_tokens.append(token)
else:
split_tokens += self.wordpiece_tokenizer.tokenize(token)
else:
split_tokens = self.wordpiece_tokenizer.tokenize(text)
return split_tokens
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.vocab.get(token, self.vocab.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.ids_to_tokens.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A LayoutLM sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A LayoutLM sequence
pair mask has the following format:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
else:
vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
with open(vocab_file, "w", encoding="utf-8") as writer:
for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!"
)
index = token_index
writer.write(token + "\n")
index += 1
return (vocab_file,)
# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
class BasicTokenizer(object):
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *optional*):
Collection of tokens which will never be split during tokenization. Only has an effect when
`do_basic_tokenize=True`
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
Whether or not to tokenize Chinese characters.
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original BERT).
do_split_on_punc (`bool`, *optional*, defaults to `True`):
In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
the full context of the words, such as contractions.
"""
def __init__(
self,
do_lower_case=True,
never_split=None,
tokenize_chinese_chars=True,
strip_accents=None,
do_split_on_punc=True,
):
if never_split is None:
never_split = []
self.do_lower_case = do_lower_case
self.never_split = set(never_split)
self.tokenize_chinese_chars = tokenize_chinese_chars
self.strip_accents = strip_accents
self.do_split_on_punc = do_split_on_punc
def tokenize(self, text, never_split=None):
"""
Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.
Args:
never_split (`List[str]`, *optional*)
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
[`PreTrainedTokenizer.tokenize`]) List of token not to split.
"""
# union() returns a new set by concatenating the two sets.
never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
text = self._clean_text(text)
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
# characters in the vocabulary because Wikipedia does have some Chinese
# words in the English Wikipedia.).
if self.tokenize_chinese_chars:
text = self._tokenize_chinese_chars(text)
# prevents treating the same character with different unicode codepoints as different characters
unicode_normalized_text = unicodedata.normalize("NFC", text)
orig_tokens = whitespace_tokenize(unicode_normalized_text)
split_tokens = []
for token in orig_tokens:
if token not in never_split:
if self.do_lower_case:
token = token.lower()
if self.strip_accents is not False:
token = self._run_strip_accents(token)
elif self.strip_accents:
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token, never_split))
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output)
def _run_split_on_punc(self, text, never_split=None):
"""Splits punctuation on a piece of text."""
if not self.do_split_on_punc or (never_split is not None and text in never_split):
return [text]
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if _is_punctuation(char):
output.append([char])
start_new_word = True
else:
if start_new_word:
output.append([])
start_new_word = False
output[-1].append(char)
i += 1
return ["".join(x) for x in output]
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
else:
output.append(char)
return "".join(output)
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
or (cp >= 0x3400 and cp <= 0x4DBF) #
or (cp >= 0x20000 and cp <= 0x2A6DF) #
or (cp >= 0x2A700 and cp <= 0x2B73F) #
or (cp >= 0x2B740 and cp <= 0x2B81F) #
or (cp >= 0x2B820 and cp <= 0x2CEAF) #
or (cp >= 0xF900 and cp <= 0xFAFF)
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xFFFD or _is_control(char):
continue
if _is_whitespace(char):
output.append(" ")
else:
output.append(char)
return "".join(output)
# Copied from transformers.models.bert.tokenization_bert.WordpieceTokenizer
class WordpieceTokenizer(object):
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
tokenization using the given vocabulary.
For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.
Args:
text: A single token or whitespace separated tokens. This should have
already been passed through *BasicTokenizer*.
Returns:
A list of wordpiece tokens.
"""
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
while start < len(chars):
end = len(chars)
cur_substr = None
while start < end:
substr = "".join(chars[start:end])
if start > 0:
substr = "##" + substr
if substr in self.vocab:
cur_substr = substr
break
end -= 1
if cur_substr is None:
is_bad = True
break
sub_tokens.append(cur_substr)
start = end
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py | # coding=utf-8
# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tokenization class for model LayoutLM."""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"microsoft/layoutlm-base-uncased": (
"https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt"
),
"microsoft/layoutlm-large-uncased": (
"https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"microsoft/layoutlm-base-uncased": (
"https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json"
),
"microsoft/layoutlm-large-uncased": (
"https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json"
),
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"microsoft/layoutlm-base-uncased": 512,
"microsoft/layoutlm-large-uncased": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"microsoft/layoutlm-base-uncased": {"do_lower_case": True},
"microsoft/layoutlm-large-uncased": {"do_lower_case": True},
}
# Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast with Bert->LayoutLM,BERT->LayoutLM
class LayoutLMTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" LayoutLM tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
clean_text (`bool`, *optional*, defaults to `True`):
Whether or not to clean the text before tokenization by removing any control characters and replacing all
whitespaces by the classic one.
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this
issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original LayoutLM).
wordpieces_prefix (`str`, *optional*, defaults to `"##"`):
The prefix for subwords.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
slow_tokenizer_class = LayoutLMTokenizer
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
do_lower_case=True,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
tokenize_chinese_chars=True,
strip_accents=None,
**kwargs,
):
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
do_lower_case=do_lower_case,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
**kwargs,
)
normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get("lowercase", do_lower_case) != do_lower_case
or normalizer_state.get("strip_accents", strip_accents) != strip_accents
or normalizer_state.get("handle_chinese_chars", tokenize_chinese_chars) != tokenize_chinese_chars
):
normalizer_class = getattr(normalizers, normalizer_state.pop("type"))
normalizer_state["lowercase"] = do_lower_case
normalizer_state["strip_accents"] = strip_accents
normalizer_state["handle_chinese_chars"] = tokenize_chinese_chars
self.backend_tokenizer.normalizer = normalizer_class(**normalizer_state)
self.do_lower_case = do_lower_case
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A LayoutLM sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
if token_ids_1 is not None:
output += token_ids_1 + [self.sep_token_id]
return output
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A LayoutLM sequence
pair mask has the following format:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
files = self._tokenizer.model.save(save_directory, name=filename_prefix)
return tuple(files)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/vision_encoder_decoder/configuration_vision_encoder_decoder.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
logger = logging.get_logger(__name__)
class VisionEncoderDecoderConfig(PretrainedConfig):
r"""
[`VisionEncoderDecoderConfig`] is the configuration class to store the configuration of a
[`VisionEncoderDecoderModel`]. It is used to instantiate a Vision-Encoder-Text-Decoder model according to the
specified arguments, defining the encoder and decoder configs.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
kwargs (*optional*):
Dictionary of keyword arguments. Notably:
- **encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
the encoder config.
- **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
the decoder config.
Examples:
```python
>>> from transformers import BertConfig, ViTConfig, VisionEncoderDecoderConfig, VisionEncoderDecoderModel
>>> # Initializing a ViT & BERT style configuration
>>> config_encoder = ViTConfig()
>>> config_decoder = BertConfig()
>>> config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
>>> # Initializing a ViTBert model (with random weights) from a ViT & bert-base-uncased style configurations
>>> model = VisionEncoderDecoderModel(config=config)
>>> # Accessing the model configuration
>>> config_encoder = model.config.encoder
>>> config_decoder = model.config.decoder
>>> # set decoder config to causal lm
>>> config_decoder.is_decoder = True
>>> config_decoder.add_cross_attention = True
>>> # Saving the model, including its configuration
>>> model.save_pretrained("my-model")
>>> # loading model and config from pretrained folder
>>> encoder_decoder_config = VisionEncoderDecoderConfig.from_pretrained("my-model")
>>> model = VisionEncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config)
```"""
model_type = "vision-encoder-decoder"
is_composition = True
def __init__(self, **kwargs):
super().__init__(**kwargs)
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f"A configuraton of type {self.model_type} cannot be instantiated because "
f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"
)
encoder_config = kwargs.pop("encoder")
encoder_model_type = encoder_config.pop("model_type")
decoder_config = kwargs.pop("decoder")
decoder_model_type = decoder_config.pop("model_type")
self.encoder = AutoConfig.for_model(encoder_model_type, **encoder_config)
self.decoder = AutoConfig.for_model(decoder_model_type, **decoder_config)
self.is_encoder_decoder = True
@classmethod
def from_encoder_decoder_configs(
cls, encoder_config: PretrainedConfig, decoder_config: PretrainedConfig, **kwargs
) -> PretrainedConfig:
r"""
Instantiate a [`VisionEncoderDecoderConfig`] (or a derived class) from a pre-trained encoder model
configuration and decoder model configuration.
Returns:
[`VisionEncoderDecoderConfig`]: An instance of a configuration object
"""
logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
decoder_config.is_decoder = True
decoder_config.add_cross_attention = True
return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **kwargs)
class VisionEncoderDecoderEncoderOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
]
)
@property
def atol_for_validation(self) -> float:
return 1e-4
@property
def outputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}})
class VisionEncoderDecoderDecoderOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = OrderedDict()
common_inputs["input_ids"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
common_inputs["attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
common_inputs["encoder_hidden_states"] = {0: "batch", 1: "encoder_sequence"}
return common_inputs
def generate_dummy_inputs(
self,
tokenizer: "PreTrainedTokenizerBase",
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional["TensorType"] = None,
) -> Mapping[str, Any]:
import torch
common_inputs = OrderedDict()
dummy_input = super().generate_dummy_inputs(
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
)
batch, encoder_sequence = dummy_input["input_ids"].shape
encoder_hidden_states_shape = (batch, encoder_sequence, self._config.encoder_hidden_size)
common_inputs["input_ids"] = dummy_input.pop("input_ids")
common_inputs["attention_mask"] = dummy_input.pop("attention_mask")
common_inputs["encoder_hidden_states"] = torch.zeros(encoder_hidden_states_shape)
return common_inputs
class VisionEncoderDecoderOnnxConfig(OnnxConfig):
@property
def inputs(self) -> None:
pass
def get_encoder_config(self, encoder_config: PretrainedConfig) -> OnnxConfig:
r"""
Returns ONNX encoder config for `VisionEncoderDecoder` model.
Args:
encoder_config (`PretrainedConfig`):
The encoder model's configuration to use when exporting to ONNX.
Returns:
[`VisionEncoderDecoderEncoderOnnxConfig`]: An instance of the ONNX configuration object
"""
return VisionEncoderDecoderEncoderOnnxConfig(encoder_config)
def get_decoder_config(
self, encoder_config: PretrainedConfig, decoder_config: PretrainedConfig, feature: str = "default"
) -> OnnxConfig:
r"""
Returns ONNX decoder config for `VisionEncoderDecoder` model.
Args:
encoder_config (`PretrainedConfig`):
The encoder model's configuration to use when exporting to ONNX.
decoder_config (`PretrainedConfig`):
The decoder model's configuration to use when exporting to ONNX
feature (`str`, *optional*):
The type of feature to export the model with.
Returns:
[`VisionEncoderDecoderDecoderOnnxConfig`]: An instance of the ONNX configuration object.
"""
decoder_config.encoder_hidden_size = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(decoder_config, feature)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/vision_encoder_decoder/modeling_flax_vision_encoder_decoder.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Classes to support Vision-Encoder-Text-Decoder architectures"""
import os
from typing import Optional, Tuple, Union
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from jax.random import PRNGKey
from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutputWithCrossAttentions, FlaxSeq2SeqLMOutput
from ...modeling_flax_utils import FlaxPreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from ..auto.configuration_auto import AutoConfig
from ..auto.modeling_flax_auto import FlaxAutoModel, FlaxAutoModelForCausalLM
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "VisionEncoderDecoderConfig"
VISION_ENCODER_DECODER_START_DOCSTRING = r"""
This class can be used to initialize an image-to-text-sequence model with any pretrained vision autoencoding model
as the encoder and any pretrained text autoregressive model as the decoder. The encoder is loaded via
[`~AutoModel.from_pretrained`] function and the decoder is loaded via [`~AutoModelForCausalLM.from_pretrained`]
function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream
generative task, like image captioning.
The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation
tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation
Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi
Zhou, Wei Li, Peter J. Liu.
Additionally, in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained
Models](https://arxiv.org/abs/2109.10282) it is shown how leveraging large pretrained vision models for optical
character recognition (OCR) yields a significant performance improvement.
After such a Vision-Encoder-Text-Decoder model has been trained/fine-tuned, it can be saved/loaded just like any
other models (see the examples for more information).
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
Parameters:
config ([`VisionEncoderDecoderConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
`jax.numpy.bfloat16` (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.**
If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
[`~FlaxPreTrainedModel.to_bf16`].
"""
VISION_ENCODER_DECODER_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using the vision model's image processor. For example, using
[`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] for details.
decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
decoder_position_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.decoder.max_position_embeddings - 1]`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
If set to `True`, the model will return a [`~utils.FlaxSeq2SeqLMOutput`] instead of a plain tuple.
"""
VISION_ENCODER_DECODER_ENCODE_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using the vision model's image processor. For example, using
[`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
If set to `True`, the model will return a [`~utils.FlaxBaseModelOutput`] instead of a plain tuple.
"""
VISION_ENCODER_DECODER_DECODE_INPUTS_DOCSTRING = r"""
Args:
decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
For sequence to sequence training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is
provided, the model will create this tensor by shifting the `input_ids` to the right for denoising
pre-training.
encoder_outputs (`tuple(tuple(jnp.ndarray)`):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
decoder_position_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.decoder.max_position_embeddings - 1]`.
past_key_values (`Dict[str, jnp.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
If set to `True`, the model will return a [`~utils.FlaxCausalLMOutputWithCrossAttentions`] instead of a
plain tuple.
"""
class FlaxVisionEncoderDecoderModule(nn.Module):
config: VisionEncoderDecoderConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
encoder_config = self.config.encoder
decoder_config = self.config.decoder
# Copied from `modeling_hybrid_clip.py` with modifications.
from ...models.auto.modeling_flax_auto import FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, FLAX_MODEL_MAPPING
encoder_module = FLAX_MODEL_MAPPING[encoder_config.__class__].module_class
decoder_module = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING[decoder_config.__class__].module_class
self.encoder = encoder_module(encoder_config, dtype=self.dtype)
self.decoder = decoder_module(decoder_config, dtype=self.dtype)
# encoder outputs might need to be projected to different dimension for decoder
if (
self.encoder.config.hidden_size != self.decoder.config.hidden_size
and self.decoder.config.cross_attention_hidden_size is None
):
self.enc_to_dec_proj = nn.Dense(
self.decoder.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.decoder.config.initializer_range),
dtype=self.dtype,
)
else:
self.enc_to_dec_proj = None
def _get_encoder_module(self):
return self.encoder
def _get_projection_module(self):
return self.enc_to_dec_proj
def _get_decoder_module(self):
return self.decoder
def __call__(
self,
pixel_values,
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
encoder_outputs = self.encoder(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
encoder_hidden_states = encoder_outputs[0]
# optionally project encoder_hidden_states
if self.enc_to_dec_proj is not None:
encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)
# The advantage of explicitly setting this is TPU XLA compiler knows as soon as possible what shape this
# variable has and can better optimize. Also passing `None` can lead to some problems when jitting the model.
# In Flax/JAX, we only want to pass `None` for non-tensor function inputs. For all tensor function inputs, we
# should always pass a tensor and not `None`.
batch_size, sequence_length = encoder_hidden_states.shape[:2]
encoder_attention_mask = jnp.ones((batch_size, sequence_length))
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return FlaxSeq2SeqLMOutput(
logits=decoder_outputs.logits,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
@add_start_docstrings(VISION_ENCODER_DECODER_START_DOCSTRING)
class FlaxVisionEncoderDecoderModel(FlaxPreTrainedModel):
r"""
[`FlaxVisionEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture
with the module (flax.nn.Module) of one of the base vision model classes of the library as encoder module and
another one as decoder module when created with the :meth*~transformers.FlaxAutoModel.from_pretrained* class method
for the encoder and :meth*~transformers.FlaxAutoModelForCausalLM.from_pretrained* class method for the decoder.
"""
config_class = VisionEncoderDecoderConfig
base_model_prefix = "vision_encoder_decoder"
main_input_name = "pixel_values"
module_class = FlaxVisionEncoderDecoderModule
def __init__(
self,
config: VisionEncoderDecoderConfig,
input_shape: Optional[Tuple] = None,
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs,
):
if not _do_init:
raise ValueError(
"`FlaxVisionEncoderDecoderModel` cannot be created without initializing, `_do_init` must be `True`."
)
if input_shape is None:
num_channels = getattr(config.encoder, "num_channels", 3)
input_shape = (
(1, config.encoder.image_size, config.encoder.image_size, num_channels),
(1, 1),
)
if config.decoder.cross_attention_hidden_size is not None:
if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size:
raise ValueError(
"If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal"
f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for"
f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for"
" `config.encoder.hidden_size`."
)
module = self.module_class(config=config, dtype=dtype, **kwargs)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
encoder_input_shape, decoder_input_shape = input_shape
# init input tensors
pixel_values = jnp.zeros(encoder_input_shape, dtype=self.dtype)
decoder_input_ids = jnp.zeros(decoder_input_shape, dtype="i4")
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
batch_size, _, _, _ = pixel_values.shape
decoder_batch_size, decoder_sequence_length = decoder_input_ids.shape
if not decoder_batch_size == batch_size:
raise ValueError(
f"The inputs of encoder and decoder should have the same batch size, but got {batch_size} for encoder "
f"and {decoder_batch_size} for decoder."
)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(decoder_sequence_length)[None, :], (decoder_batch_size, decoder_sequence_length)
)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
random_params = self.module.init(
rngs,
pixel_values,
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
)["params"]
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freeze(unflatten_dict(params))
else:
return random_params
def init_cache(self, batch_size, max_length, encoder_outputs):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
cache.
encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`):
`encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*:
`attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*)
is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
cross-attention of the decoder.
"""
# init input variables to retrieve cache
decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape
)
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
return decoder_module(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
**kwargs,
)
init_variables = self.module.init(
jax.random.PRNGKey(0),
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
init_cache=True,
method=_decoder_forward, # we only need to call the decoder to init the cache
)
return unfreeze(init_variables["cache"])
@add_start_docstrings(VISION_ENCODER_DECODER_ENCODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=_CONFIG_FOR_DOC)
def encode(
self,
pixel_values: jnp.ndarray,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> from transformers import AutoImageProcessor, FlaxVisionEncoderDecoderModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
>>> # initialize a vit-gpt2 from pretrained ViT and GPT2 models. Note that the cross-attention layers will be randomly initialized
>>> model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
... "google/vit-base-patch16-224-in21k", "gpt2"
... )
>>> pixel_values = image_processor(images=image, return_tensors="np").pixel_values
>>> encoder_outputs = model.encode(pixel_values)
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
# `FlaxViTModel` expects channel first format, but `FlaxViTModule` expects channel last format.
# Currently, we assume this holds for all Flax vision models, and perform a transpose here.
pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
def _encoder_forward(module, pixel_values, **kwargs):
encode_module = module._get_encoder_module()
return encode_module(pixel_values, **kwargs)
outputs = self.module.apply(
{"params": params or self.params},
pixel_values=jnp.array(pixel_values, dtype=self.dtype),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
method=_encoder_forward,
)
if return_dict:
outputs = FlaxBaseModelOutput(
last_hidden_state=outputs.last_hidden_state,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
return outputs
@add_start_docstrings(VISION_ENCODER_DECODER_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def decode(
self,
decoder_input_ids,
encoder_outputs,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> from transformers import AutoImageProcessor, FlaxVisionEncoderDecoderModel
>>> import jax.numpy as jnp
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
>>> # initialize a vit-gpt2 from pretrained ViT and GPT2 models. Note that the cross-attention layers will be randomly initialized
>>> model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
... "google/vit-base-patch16-224-in21k", "gpt2"
... )
>>> pixel_values = image_processor(images=image, return_tensors="np").pixel_values
>>> encoder_outputs = model.encode(pixel_values)
>>> decoder_start_token_id = model.config.decoder.bos_token_id
>>> decoder_input_ids = jnp.ones((pixel_values.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> logits = outputs.logits
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
encoder_hidden_states = encoder_outputs[0]
batch_size, sequence_length = encoder_hidden_states.shape[:2]
encoder_attention_mask = jnp.ones((batch_size, sequence_length))
batch_size, sequence_length = decoder_input_ids.shape
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones((batch_size, sequence_length))
if decoder_position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
inputs = {"params": params or self.params}
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be
# passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
# it can be changed by FlaxBartAttention module
if past_key_values:
inputs["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
def _decoder_forward(
module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, encoder_hidden_states, **kwargs
):
projection_module = module._get_projection_module()
decoder_module = module._get_decoder_module()
# optionally project encoder_hidden_states
if projection_module is not None:
encoder_hidden_states = projection_module(encoder_hidden_states)
return decoder_module(
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
encoder_hidden_states,
**kwargs,
)
outputs = self.module.apply(
inputs,
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
mutable=mutable,
method=_decoder_forward,
)
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs, past = outputs
outputs["past_key_values"] = unfreeze(past["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs, past = outputs
outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
return outputs
@add_start_docstrings_to_model_forward(VISION_ENCODER_DECODER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def __call__(
self,
pixel_values: jnp.ndarray,
decoder_input_ids: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Examples:
```python
>>> from transformers import FlaxVisionEncoderDecoderModel, AutoImageProcessor, AutoTokenizer
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
>>> # load output tokenizer
>>> tokenizer_output = AutoTokenizer.from_pretrained("gpt2")
>>> # initialize a vit-gpt2 from pretrained ViT and GPT2 models. Note that the cross-attention layers will be randomly initialized
>>> model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
... "google/vit-base-patch16-224-in21k", "gpt2"
... )
>>> pixel_values = image_processor(images=image, return_tensors="np").pixel_values
>>> # use GPT2's eos_token as the pad as well as eos token
>>> model.config.eos_token_id = model.config.decoder.eos_token_id
>>> model.config.pad_token_id = model.config.eos_token_id
>>> # generation
>>> sequences = model.generate(pixel_values, num_beams=4, max_length=12).sequences
>>> captions = tokenizer_output.batch_decode(sequences, skip_special_tokens=True)
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
# prepare encoder inputs
# `FlaxViTModel` expects channel first format, but `FlaxViTModule` expects channel last format.
# Currently, we assume this holds for all Flax vision models, and perform a transpose here.
pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
# prepare decoder inputs
if decoder_input_ids is None:
raise ValueError("`decoder_input_ids` can't be `None`.")
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
if decoder_position_ids is None:
batch_size, sequence_length = decoder_input_ids.shape
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
# Handle any PRNG if needed
rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
return self.module.apply(
{"params": params or self.params},
pixel_values=jnp.array(pixel_values, dtype=self.dtype),
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
)
def prepare_inputs_for_generation(
self,
decoder_input_ids,
max_length,
decoder_attention_mask: Optional[jax.Array] = None,
encoder_outputs=None,
**kwargs,
):
# initializing the cache
batch_size, seq_length = decoder_input_ids.shape
past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
# Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
# But since the decoder uses a causal mask, those positions are masked anyways.
# Thus we can create a single static attention_mask here, which is more efficient for compilation
extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
if decoder_attention_mask is not None:
decoder_position_ids = decoder_attention_mask.cumsum(axis=-1) - 1
extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0))
else:
decoder_position_ids = jnp.broadcast_to(
jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)
)
return {
"past_key_values": past_key_values,
"encoder_outputs": encoder_outputs,
"decoder_attention_mask": extended_attention_mask,
"decoder_position_ids": decoder_position_ids,
}
def update_inputs_for_generation(self, model_outputs, model_kwargs):
model_kwargs["past_key_values"] = model_outputs.past_key_values
model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1
return model_kwargs
@classmethod
def from_encoder_decoder_pretrained(
cls,
encoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None,
decoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None,
*model_args,
**kwargs,
) -> FlaxPreTrainedModel:
r"""
Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model
checkpoints.
Params:
encoder_pretrained_model_name_or_path (`Union[str, os.PathLike]`, *optional*):
Information necessary to initiate the encoder. Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. An
example is `google/vit-base-patch16-224-in21k`.
- A path to a *directory* containing model weights saved using
[`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
decoder_pretrained_model_name_or_path (`Union[str, os.PathLike]`, *optional*, defaults to `None`):
Information necessary to initiate the decoder. Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
user or organization name, like `dbmdz/bert-base-german-cased`.
- A path to a *directory* containing model weights saved using
[`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
model_args (remaining positional arguments, *optional*):
All remaning positional arguments will be passed to the underlying model's `__init__` method.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
`output_attentions=True`).
- To update the encoder configuration, use the prefix *encoder_* for each configuration parameter.
- To update the decoder configuration, use the prefix *decoder_* for each configuration parameter.
- To update the parent model configuration, do not use a prefix for each configuration parameter.
Behaves differently depending on whether a `config` is provided or automatically loaded.
Example:
```python
>>> from transformers import FlaxVisionEncoderDecoderModel
>>> # initialize a vit-gpt2 from a pretrained ViT and a pretrained GPT2 model. Note that the cross-attention layers will be randomly initialized
>>> model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
... "google/vit-base-patch16-224-in21k", "gpt2"
... )
>>> # saving model after fine-tuning
>>> model.save_pretrained("./vit-gpt2")
>>> # load fine-tuned model
>>> model = FlaxVisionEncoderDecoderModel.from_pretrained("./vit-gpt2")
```"""
kwargs_encoder = {
argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_")
}
kwargs_decoder = {
argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
}
# remove encoder, decoder kwargs from kwargs
for key in kwargs_encoder.keys():
del kwargs["encoder_" + key]
for key in kwargs_decoder.keys():
del kwargs["decoder_" + key]
# Load and initialize the encoder and decoder
# The distinction between encoder and decoder at the model level is made
# by the value of the flag `is_decoder` that we need to set correctly.
encoder = kwargs_encoder.pop("model", None)
if encoder is None:
if encoder_pretrained_model_name_or_path is None:
raise ValueError(
"If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has "
"to be defined."
)
if "config" not in kwargs_encoder:
encoder_config = AutoConfig.from_pretrained(encoder_pretrained_model_name_or_path)
if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
logger.info(
f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model "
"from a decoder model. Cross-attention and casual mask are disabled."
)
encoder_config.is_decoder = False
encoder_config.add_cross_attention = False
kwargs_encoder["config"] = encoder_config
encoder = FlaxAutoModel.from_pretrained(
encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder
)
decoder = kwargs_decoder.pop("model", None)
if decoder is None:
if decoder_pretrained_model_name_or_path is None:
raise ValueError(
"If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has "
"to be defined."
)
if "config" not in kwargs_decoder:
decoder_config = AutoConfig.from_pretrained(decoder_pretrained_model_name_or_path)
if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
logger.info(
f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention"
f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if"
f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers."
)
decoder_config.is_decoder = True
decoder_config.add_cross_attention = True
kwargs_decoder["config"] = decoder_config
if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False:
logger.warning(
f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. "
f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, "
"make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` "
"passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a "
"`decoder_config` to `.from_encoder_decoder_pretrained(...)`"
)
decoder = FlaxAutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
# instantiate config with corresponding kwargs
dtype = kwargs.pop("dtype", jnp.float32)
config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs)
# init model
model = cls(config, dtype=dtype)
model.params["encoder"] = encoder.params
model.params["decoder"] = decoder.params
return model
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py | # coding=utf-8
# Copyright 2022 HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Classes to support TF Vision-Encoder-Text-Decoder architectures"""
from __future__ import annotations
import re
import warnings
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...configuration_utils import PretrainedConfig
from ...modeling_tf_outputs import TFBaseModelOutput, TFSeq2SeqLMOutput
from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFPreTrainedModel, get_initializer, unpack_inputs
from ...tf_utils import shape_list
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ..auto.configuration_auto import AutoConfig
from ..auto.modeling_tf_auto import TFAutoModel, TFAutoModelForCausalLM
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "VisionEncoderDecoderConfig"
DEPRECATION_WARNING = (
"Version v4.17.0 introduces a better way to train encoder-decoder models by computing the loss inside the"
" encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if"
" fine-tuning a model trained with versions anterior to 4.17.0. The decoder_input_ids are now created based on the"
" labels, no need to pass them yourself anymore."
)
VISION_ENCODER_DECODER_START_DOCSTRING = r"""
This class can be used to initialize an image-to-text-sequence model with any pretrained vision autoencoding model
as the encoder and any pretrained text autoregressive model as the decoder. The encoder is loaded via
[`~TFAutoModel.from_pretrained`] function and the decoder is loaded via [`~TFAutoModelForCausalLM.from_pretrained`]
function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream
generative task, like image captioning.
The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation
tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation
Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi
Zhou, Wei Li, Peter J. Liu.
Additionally, in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained
Models](https://arxiv.org/abs/2109.10282) it is shown how leveraging large pretrained vision models for optical
character recognition (OCR) yields a significant performance improvement.
After such a Vision-Encoder-Text-Decoder model has been trained/fine-tuned, it can be saved/loaded just like any
other models (see the examples for more information).
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
Parameters:
config ([`VisionEncoderDecoderConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""
VISION_ENCODER_DECODER_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using the vision's model's image processor. For example, using
[`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] for details.
decoder_input_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
Provide for sequence to sequence training to the decoder. Indices can be obtained using
[`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for
details.
decoder_attention_mask (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
encoder_outputs (`tuple(tuple(tf.Tensor)`, *optional*):
This tuple must consist of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`last_hidden_state` (`tf.Tensor` of shape `({0}, hidden_size)`) is a tensor of hidden-states at the output
of the last layer of the encoder. Used in the cross-attention of the decoder.
past_key_values (`tuple(tuple(tf.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `({0})`.
decoder_inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
representation. This is useful if you want more control over how to convert `decoder_input_ids` indices
into associated vectors than the model's internal embedding lookup matrix.
labels (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Labels for computing the masked language modeling loss for the decoder. Indices should be in `[-100, 0,
..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
If set to `True`, the model will return a [`~utils.Seq2SeqLMOutput`] instead of a plain tuple.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
kwargs (*optional*): Remaining dictionary of keyword arguments. Keyword arguments come in two flavors:
- Without a prefix which will be input as `**encoder_kwargs` for the encoder forward function.
- With a *decoder_* prefix which will be input as `**decoder_kwargs` for the decoder forward function.
"""
# Copied from transformers.models.encoder_decoder.modeling_tf_encoder_decoder.shift_tokens_right
def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int):
if pad_token_id is None:
raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.")
pad_token_id = tf.cast(pad_token_id, input_ids.dtype)
if decoder_start_token_id is None:
raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.")
decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype)
start_tokens = tf.fill((shape_list(input_ids)[0], 1), decoder_start_token_id)
shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1)
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids = tf.where(
shifted_input_ids == -100, tf.fill(shape_list(shifted_input_ids), pad_token_id), shifted_input_ids
)
# "Verify that `labels` has only positive values and -100"
assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype))
# Make sure the assertion op is called by wrapping the result in an identity no-op
with tf.control_dependencies([assert_gte0]):
shifted_input_ids = tf.identity(shifted_input_ids)
return shifted_input_ids
@add_start_docstrings(VISION_ENCODER_DECODER_START_DOCSTRING)
class TFVisionEncoderDecoderModel(TFPreTrainedModel, TFCausalLanguageModelingLoss):
r"""
[`TFVisionEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture
with one of the base vision model classes of the library as encoder and another one of the base model classes as
decoder when created with the [`~TFAutoModel.from_pretrained`] class method for the encoder and
[`~TFAutoModelForCausalLM.from_pretrained`] class method for the decoder.
"""
config_class = VisionEncoderDecoderConfig
base_model_prefix = "vision_encoder_decoder"
load_weight_prefix = "tf_vision_encoder_decoder_model"
main_input_name = "pixel_values"
def __init__(
self,
config: Optional[PretrainedConfig] = None,
encoder: Optional[TFPreTrainedModel] = None,
decoder: Optional[TFPreTrainedModel] = None,
):
if config is None and (encoder is None or decoder is None):
raise ValueError("Either a configuration or an encoder and a decoder has to be provided.")
if config is None:
config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config)
else:
if not isinstance(config, self.config_class):
raise ValueError(f"config: {config} has to be of type {self.config_class}")
if config.decoder.cross_attention_hidden_size is not None:
if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size:
raise ValueError(
"If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal"
f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for"
f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for"
" `config.encoder.hidden_size`."
)
# initialize with config
super().__init__(config)
if encoder is None:
encoder = TFAutoModel.from_config(config.encoder, name="encoder")
if decoder is None:
decoder = TFAutoModelForCausalLM.from_config(config.decoder, name="decoder")
self.encoder = encoder
self.decoder = decoder
if self.encoder.config.to_dict() != self.config.encoder.to_dict():
logger.warning(
f"Config of the encoder: {self.encoder.__class__} is overwritten by shared encoder config:"
f" {self.config.encoder}"
)
if self.decoder.config.to_dict() != self.config.decoder.to_dict():
logger.warning(
f"Config of the decoder: {self.decoder.__class__} is overwritten by shared decoder config:"
f" {self.config.decoder}"
)
# make sure that the individual model's config refers to the shared config
# so that the updates to the config will be synced
self.encoder.config = self.config.encoder
self.decoder.config = self.config.decoder
# encoder outputs might need to be projected to different dimension for decoder
if (
self.encoder.config.hidden_size != self.decoder.config.hidden_size
and self.decoder.config.cross_attention_hidden_size is None
):
self.enc_to_dec_proj = tf.keras.layers.Dense(
units=self.decoder.config.hidden_size,
kernel_initializer=get_initializer(config.encoder.initializer_range),
name="enc_to_dec_proj",
)
if self.encoder.get_output_embeddings() is not None:
raise ValueError(
f"The encoder {self.encoder} should not have a LM Head. Please use a model without LM Head"
)
@property
def input_signature(self):
vision_config = self.config.encoder
if hasattr(vision_config, "vision_config"):
vision_config = vision_config.vision_config
if hasattr(vision_config, "image_size"):
image_size = vision_config.image_size
else:
image_size = vision_config.input_size
return {
"pixel_values": tf.TensorSpec(
shape=(
None,
vision_config.num_channels,
image_size,
image_size,
),
dtype=tf.float32,
),
"decoder_input_ids": tf.TensorSpec(shape=(None, None), dtype=tf.int32, name="decoder_input_ids"),
}
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
def get_input_embeddings(self):
return self.encoder.get_input_embeddings()
def get_output_embeddings(self):
return self.decoder.get_output_embeddings()
def set_output_embeddings(self, new_embeddings):
return self.decoder.set_output_embeddings(new_embeddings)
def tf_to_pt_weight_rename(self, tf_weight):
# Matt: The TF and PT weights don't align because our TF base classes have an extra layer compared to PT models
# (the main model stem is in the MainLayer class). If we remove that layer, then weight names sync up as normal.
# However, the name of that extra layer is the name of the MainLayer in the base model. We make the assumption
# here that the config model_type is the same as the name of the MainLayer. I don't know of anywhere that's
# not the case, and I wasn't sure how else to go from the config to the correct MainLayer name!
# This override is only needed in the case where we're crossloading weights from PT. However, since weights are
# often safetensors now, we don't know if we're going to be crossloading until we sniff the weights file.
# Therefore, we specify tf_to_pt_weight_rename anyway, and let the super method figure out if it needs it
# or not.
encoder_model_type = self.config.encoder.model_type
if "encoder" in tf_weight and "decoder" not in tf_weight:
return (re.sub(rf"encoder\.{encoder_model_type}\.", "encoder.", tf_weight),)
else:
return (tf_weight,)
@classmethod
def from_encoder_decoder_pretrained(
cls,
encoder_pretrained_model_name_or_path: str = None,
decoder_pretrained_model_name_or_path: str = None,
*model_args,
**kwargs,
) -> TFPreTrainedModel:
r"""
Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model
checkpoints.
Params:
encoder_pretrained_model_name_or_path (`str`, *optional*):
Information necessary to initiate the encoder. Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. An
example is `google/vit-base-patch16-224-in21k`.
- A path to a *directory* containing model weights saved using
[`~TFPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- A path or url to a *pytorch index checkpoint file* (e.g, `./pt_model/`). In this case,
`encoder_from_pt` should be set to `True`.
decoder_pretrained_model_name_or_path (`str`, *optional*, defaults to *None*):
Information necessary to initiate the decoder. Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
user or organization name, like `dbmdz/bert-base-german-cased`.
- A path to a *directory* containing model weights saved using
[`~TFPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- A path or url to a *pytorch checkpoint file* (e.g, `./pt_model/`). In this case,
`decoder_from_pt` should be set to `True`.
model_args (remaining positional arguments, *optional*):
All remaning positional arguments will be passed to the underlying model's `__init__` method.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
`output_attentions=True`).
- To update the encoder configuration, use the prefix *encoder_* for each configuration parameter.
- To update the decoder configuration, use the prefix *decoder_* for each configuration parameter.
- To update the parent model configuration, do not use a prefix for each configuration parameter.
Behaves differently depending on whether a `config` is provided or automatically loaded.
Example:
```python
>>> from transformers import TFVisionEncoderDecoderModel
>>> # initialize a vit-bert from a pretrained ViT and a pretrained BERT model. Note that the cross-attention layers will be randomly initialized
>>> model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
... "google/vit-base-patch16-224-in21k", "bert-base-uncased"
... )
>>> # saving model after fine-tuning
>>> model.save_pretrained("./vit-bert")
>>> # load fine-tuned model
>>> model = TFVisionEncoderDecoderModel.from_pretrained("./vit-bert")
```"""
kwargs_encoder = {
argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_")
}
kwargs_decoder = {
argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
}
# remove encoder, decoder kwargs from kwargs
for key in kwargs_encoder.keys():
del kwargs["encoder_" + key]
for key in kwargs_decoder.keys():
del kwargs["decoder_" + key]
# Load and initialize the encoder and decoder
# The distinction between encoder and decoder at the model level is made
# by the value of the flag `is_decoder` that we need to set correctly.
encoder = kwargs_encoder.pop("model", None)
if encoder is None:
if encoder_pretrained_model_name_or_path is None:
raise ValueError(
"If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has "
"to be defined."
)
if "config" not in kwargs_encoder:
encoder_config = AutoConfig.from_pretrained(encoder_pretrained_model_name_or_path)
if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
logger.info(
f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model "
"from a decoder model. Cross-attention and casual mask are disabled."
)
encoder_config.is_decoder = False
encoder_config.add_cross_attention = False
kwargs_encoder["config"] = encoder_config
kwargs_encoder["name"] = "encoder"
kwargs_encoder["load_weight_prefix"] = cls.load_weight_prefix
encoder = TFAutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder)
decoder = kwargs_decoder.pop("model", None)
if decoder is None:
if decoder_pretrained_model_name_or_path is None:
raise ValueError(
"If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has "
"to be defined."
)
if "config" not in kwargs_decoder:
decoder_config = AutoConfig.from_pretrained(decoder_pretrained_model_name_or_path)
if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
logger.info(
f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention"
f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if"
f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers."
)
decoder_config.is_decoder = True
decoder_config.add_cross_attention = True
kwargs_decoder["config"] = decoder_config
if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False:
logger.warning(
f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. "
f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, "
"make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` "
"passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a "
"`decoder_config` to `.from_encoder_decoder_pretrained(...)`"
)
kwargs_decoder["name"] = "decoder"
kwargs_decoder["load_weight_prefix"] = cls.load_weight_prefix
decoder = TFAutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
# Make sure these 2 `tf.keras.Model` have fixed names so `from_pretrained` could load model weights correctly.
if encoder.name != "encoder":
raise ValueError("encoder model must be created with the name `encoder`.")
if decoder.name != "decoder":
raise ValueError("decoder model must be created with the name `decoder`.")
# instantiate config with corresponding kwargs
config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs)
return cls(encoder=encoder, decoder=decoder, config=config)
@unpack_inputs
@add_start_docstrings_to_model_forward(
VISION_ENCODER_DECODER_INPUTS_DOCSTRING.format("batch_size, sequence_length")
)
@replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
pixel_values: np.ndarray | tf.Tensor | None = None,
decoder_input_ids: np.ndarray | tf.Tensor | None = None,
decoder_attention_mask: np.ndarray | tf.Tensor | None = None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
decoder_inputs_embeds: np.ndarray | tf.Tensor | None = None,
labels: np.ndarray | tf.Tensor | None = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
**kwargs,
) -> Union[TFSeq2SeqLMOutput, Tuple[tf.Tensor]]:
r"""
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, AutoTokenizer, TFVisionEncoderDecoderModel
>>> from PIL import Image
>>> import requests
>>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
>>> decoder_tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> # initialize a bert2gpt2 from a pretrained BERT and GPT2 models. Note that the cross-attention layers will be randomly initialized
>>> model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
... "google/vit-base-patch16-224-in21k", "gpt2"
... )
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> img = Image.open(requests.get(url, stream=True).raw)
>>> # forward
>>> pixel_values = image_processor(images=img, return_tensors="tf").pixel_values # Batch size 1
>>> decoder_input_ids = decoder_tokenizer("Linda Davis", return_tensors="tf").input_ids # Batch size 1
>>> outputs = model(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids)
>>> # training
>>> outputs = model(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, labels=decoder_input_ids)
>>> loss, logits = outputs.loss, outputs.logits
>>> # save and load from pretrained
>>> model.save_pretrained("vit-gpt2")
>>> model = TFVisionEncoderDecoderModel.from_pretrained("vit-gpt2")
>>> # generation
>>> generated = model.generate(pixel_values, decoder_start_token_id=model.config.decoder.bos_token_id)
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}
kwargs_decoder = {
argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
}
# Let the user be responsible for the expected format.
if encoder_outputs is not None:
if return_dict and not isinstance(encoder_outputs, ModelOutput):
raise ValueError(
"If `return_dict=True` and `encoder_outputs` is provided, it should be an instance of "
f"`ModelOutput`. Got an instance {type(encoder_outputs)} for `encoder_outputs`."
)
if encoder_outputs is None:
encoder_inputs = {
"input_ids": pixel_values,
"output_attentions": output_attentions,
"output_hidden_states": output_hidden_states,
"return_dict": return_dict,
"training": training,
}
# Add arguments to encoder from `kwargs_encoder`
encoder_inputs.update(kwargs_encoder)
if "input_ids" in encoder_inputs:
encoder_inputs["pixel_values"] = encoder_inputs.pop("input_ids")
if encoder_inputs["pixel_values"] is None:
raise ValueError("You have to specify pixel_values")
# Handle the case where the inputs are passed as a single dict which contains `labels`.
# The `labels` shouldn't be passed to `self.encoder` below, because it is a based model without this
# parameter (otherwise, an error occurs when `input_processing` is called inside `self.encoder.call()`).
if "labels" in encoder_inputs:
labels = encoder_inputs.pop("labels")
# handle the init case where `dummy_inputs` returns a dict containing `decoder_input_ids`.
if "decoder_input_ids" in encoder_inputs:
decoder_input_ids = encoder_inputs.pop("decoder_input_ids")
# handle the init case where `dummy_inputs` returns a dict containing `decoder_input_ids`.
if "decoder_attention_mask" in encoder_inputs:
decoder_attention_mask = encoder_inputs.pop("decoder_attention_mask")
encoder_outputs = self.encoder(**encoder_inputs)
encoder_hidden_states = encoder_outputs[0]
# optionally project encoder_hidden_states
if (
self.encoder.config.hidden_size != self.decoder.config.hidden_size
and self.decoder.config.cross_attention_hidden_size is None
):
encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)
if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
batch_size, sequence_length = shape_list(encoder_hidden_states)[:2]
encoder_attention_mask = tf.ones(shape=(batch_size, sequence_length), dtype=tf.int32)
decoder_inputs = {
"input_ids": decoder_input_ids,
"attention_mask": decoder_attention_mask,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
"inputs_embeds": decoder_inputs_embeds,
"output_attentions": output_attentions,
"output_hidden_states": output_hidden_states,
"use_cache": use_cache,
"past_key_values": past_key_values,
"return_dict": return_dict,
"training": training,
}
# Add arguments to decoder from `kwargs_decoder`
decoder_inputs.update(kwargs_decoder)
decoder_outputs = self.decoder(**decoder_inputs)
logits = decoder_outputs[0]
# Compute loss independent from decoder (as some shift the logits inside them)
loss = None
if labels is not None:
warnings.warn(DEPRECATION_WARNING, FutureWarning)
loss = self.hf_compute_loss(labels, logits)
if not return_dict:
past_key_values = None
if use_cache:
past_key_values = decoder_outputs[1]
# The starting index of the remaining elements in `decoder_outputs`
start_index = sum([1 if x is not None else 0 for x in (loss, logits, past_key_values)])
if not isinstance(encoder_outputs, tuple):
encoder_outputs = encoder_outputs.to_tuple()
output = (loss, logits, past_key_values) + decoder_outputs[start_index:] + encoder_outputs
output = tuple([x for x in output if x is not None])
return output
return TFSeq2SeqLMOutput(
loss=loss,
logits=decoder_outputs.logits,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
def serving_output(self, output):
pkv = tf.tuple(output.past_key_values)[1] if self.config.decoder.use_cache else None
dec_hs = (
tf.convert_to_tensor(output.decoder_hidden_states) if self.config.decoder.output_hidden_states else None
)
dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.decoder.output_attentions else None
enc_hs = (
tf.convert_to_tensor(output.encoder_hidden_states) if self.config.encoder.output_hidden_states else None
)
enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.encoder.output_attentions else None
cross_attns = (
tf.convert_to_tensor(output.cross_attentions)
if self.config.decoder.output_attentions and output.cross_attentions is not None
else None
)
return TFSeq2SeqLMOutput(
logits=output.logits,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
encoder_hidden_states=enc_hs,
encoder_attentions=enc_attns,
cross_attentions=cross_attns,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
):
decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, past_key_values=past_key_values)
decoder_attention_mask = decoder_inputs["attention_mask"] if "attention_mask" in decoder_inputs else None
past_key_values = decoder_inputs.get("past_key_values")
input_dict = {
"pixel_values": None, # needs to be passed to make Keras.layer.__call__ happy
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"decoder_input_ids": decoder_inputs["input_ids"],
# TODO (joao): the `TFBaseModelOutput` wrapper should not be needed after the generate refactor is complete
"encoder_outputs": TFBaseModelOutput(last_hidden_state=encoder_outputs[0]),
"past_key_values": past_key_values,
"use_cache": use_cache,
}
return input_dict
def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor):
return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
def resize_token_embeddings(self, *args, **kwargs):
raise NotImplementedError(
"Resizing the embedding layers via the TFVisionEncoderDecoderModel directly is not supported. "
"Please use the respective methods of the wrapped objects (model.decoder.resize_token_embeddings(...))"
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "enc_to_dec_proj", None) is not None:
with tf.name_scope(self.enc_to_dec_proj.name):
self.enc_to_dec_proj.build([None, None, self.encoder.config.hidden_size])
if getattr(self, "encoder", None) is not None:
with tf.name_scope(self.encoder.name):
self.encoder.build(None)
if getattr(self, "decoder", None) is not None:
with tf.name_scope(self.decoder.name):
self.decoder.build(None)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/vision_encoder_decoder/__init__.py | # Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_import_structure = {
"configuration_vision_encoder_decoder": ["VisionEncoderDecoderConfig", "VisionEncoderDecoderOnnxConfig"]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_vision_encoder_decoder"] = ["VisionEncoderDecoderModel"]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_vision_encoder_decoder"] = ["TFVisionEncoderDecoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_vision_encoder_decoder"] = ["FlaxVisionEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Classes to support Vision-Encoder-Text-Decoder architectures"""
import gc
import os
import tempfile
from typing import Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ...configuration_utils import PretrainedConfig
from ...modeling_outputs import BaseModelOutput, Seq2SeqLMOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from ..auto.configuration_auto import AutoConfig
from ..auto.modeling_auto import AutoModel, AutoModelForCausalLM
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig
# Copied from transformers.models.encoder_decoder.modeling_encoder_decoder.shift_tokens_right
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
"""
Shift input ids one token to the right.
"""
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
if decoder_start_token_id is None:
raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.")
shifted_input_ids[:, 0] = decoder_start_token_id
if pad_token_id is None:
raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.")
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "VisionEncoderDecoderConfig"
VISION_ENCODER_DECODER_START_DOCSTRING = r"""
This class can be used to initialize an image-to-text-sequence model with any pretrained vision autoencoding model
as the encoder and any pretrained text autoregressive model as the decoder. The encoder is loaded via
[`~AutoModel.from_pretrained`] function and the decoder is loaded via [`~AutoModelForCausalLM.from_pretrained`]
function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream
generative task, like image captioning.
The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation
tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation
Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi
Zhou, Wei Li, Peter J. Liu.
Additionally, in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained
Models](https://arxiv.org/abs/2109.10282) it is shown how leveraging large pretrained vision models for optical
character recognition (OCR) yields a significant performance improvement.
After such a Vision-Encoder-Text-Decoder model has been trained/fine-tuned, it can be saved/loaded just like any
other models (see the examples for more information).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`VisionEncoderDecoderConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
VISION_ENCODER_DECODER_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using an image processor (e.g. if you use ViT as the encoder,
you should use [`AutoImageProcessor`]). See [`ViTImageProcessor.__call__`] for details.
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
For training, `decoder_input_ids` are automatically created by the model by shifting the `labels` to the
right, replacing -100 by the `pad_token_id` and prepending them with the `decoder_start_token_id`.
decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
encoder_outputs (`tuple(torch.FloatTensor)`, *optional*):
This tuple must consist of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`last_hidden_state` (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) is a tensor
of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the
decoder.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
representation. This is useful if you want more control over how to convert `decoder_input_ids` indices
into associated vectors than the model's internal embedding lookup matrix.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss for the decoder. Indices should be in `[-100, 0,
..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
If set to `True`, the model will return a [`~utils.Seq2SeqLMOutput`] instead of a plain tuple.
kwargs (*optional*): Remaining dictionary of keyword arguments. Keyword arguments come in two flavors:
- Without a prefix which will be input as `**encoder_kwargs` for the encoder forward function.
- With a *decoder_* prefix which will be input as `**decoder_kwargs` for the decoder forward function.
"""
@add_start_docstrings(VISION_ENCODER_DECODER_START_DOCSTRING)
class VisionEncoderDecoderModel(PreTrainedModel):
r"""
[`VisionEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture with
one of the base vision model classes of the library as encoder and another one as decoder when created with the
:meth*~transformers.AutoModel.from_pretrained* class method for the encoder and
:meth*~transformers.AutoModelForCausalLM.from_pretrained* class method for the decoder.
"""
config_class = VisionEncoderDecoderConfig
base_model_prefix = "vision_encoder_decoder"
main_input_name = "pixel_values"
supports_gradient_checkpointing = True
def __init__(
self,
config: Optional[PretrainedConfig] = None,
encoder: Optional[PreTrainedModel] = None,
decoder: Optional[PreTrainedModel] = None,
):
if config is None and (encoder is None or decoder is None):
raise ValueError("Either a configuration or an encoder and a decoder has to be provided.")
if config is None:
config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config)
else:
if not isinstance(config, self.config_class):
raise ValueError(f"Config: {config} has to be of type {self.config_class}")
if config.decoder.cross_attention_hidden_size is not None:
if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size:
raise ValueError(
"If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal"
f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for"
f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for"
" `config.encoder.hidden_size`."
)
# initialize with config
# make sure input & output embeddings is not tied
config.tie_word_embeddings = False
super().__init__(config)
if encoder is None:
encoder = AutoModel.from_config(config.encoder)
if decoder is None:
decoder = AutoModelForCausalLM.from_config(config.decoder)
self.encoder = encoder
self.decoder = decoder
if self.encoder.config.to_dict() != self.config.encoder.to_dict():
logger.warning(
f"Config of the encoder: {self.encoder.__class__} is overwritten by shared encoder config:"
f" {self.config.encoder}"
)
if self.decoder.config.to_dict() != self.config.decoder.to_dict():
logger.warning(
f"Config of the decoder: {self.decoder.__class__} is overwritten by shared decoder config:"
f" {self.config.decoder}"
)
# make sure that the individual model's config refers to the shared config
# so that the updates to the config will be synced
self.encoder.config = self.config.encoder
self.decoder.config = self.config.decoder
# encoder outputs might need to be projected to different dimension for decoder
if (
self.encoder.config.hidden_size != self.decoder.config.hidden_size
and self.decoder.config.cross_attention_hidden_size is None
):
self.enc_to_dec_proj = nn.Linear(self.encoder.config.hidden_size, self.decoder.config.hidden_size)
if self.encoder.get_output_embeddings() is not None:
raise ValueError(
f"The encoder {self.encoder} should not have a LM Head. Please use a model without LM Head"
)
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
def get_output_embeddings(self):
return self.decoder.get_output_embeddings()
def set_output_embeddings(self, new_embeddings):
return self.decoder.set_output_embeddings(new_embeddings)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""
Example:
```python
>>> from transformers import VisionEncoderDecoderModel, AutoImageProcessor, AutoTokenizer
>>> from PIL import Image
>>> import requests
>>> image_processor = AutoImageProcessor.from_pretrained("ydshieh/vit-gpt2-coco-en")
>>> decoder_tokenizer = AutoTokenizer.from_pretrained("ydshieh/vit-gpt2-coco-en")
>>> model = VisionEncoderDecoderModel.from_pretrained("ydshieh/vit-gpt2-coco-en")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> img = Image.open(requests.get(url, stream=True).raw)
>>> pixel_values = image_processor(images=img, return_tensors="pt").pixel_values # Batch size 1
>>> output_ids = model.generate(
... pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True
... ).sequences
>>> preds = decoder_tokenizer.batch_decode(output_ids, skip_special_tokens=True)
>>> preds = [pred.strip() for pred in preds]
>>> assert preds == ["a cat laying on top of a couch next to another cat"]
```"""
from_tf = kwargs.pop("from_tf", False)
if from_tf:
from transformers import TFVisionEncoderDecoderModel
# a workaround to load from tensorflow checkpoint
# Using `_tf_model` won't work, because the weight names in the encoder/decoder of `_tf_model` get
# extended before saving those components. For example, The name of `_tf_model.encoder.vit` is
# `[top model name]/encoder/vit`, but the name of `tf_model.encoder.vit` is `[top model name]/vit`. The
# [top model name] is handled (stripped) by the conversion method, and the former case gets extra `encoder`,
# which should not occur when we want to save the components alone.
# There was a (very) ugly potential fix, which wasn't integrated to `transformers`: see
# https://github.com/huggingface/transformers/pull/13222/commits/dbb3c9de76eee235791d2064094654637c99f36d#r697304245
# (the change in `src/transformers/modeling_tf_utils.py`)
_tf_model = TFVisionEncoderDecoderModel.from_pretrained(
pretrained_model_name_or_path, *model_args, **kwargs
)
config = _tf_model.config
# Using `tf_model` instead
encoder = _tf_model.encoder.__class__(_tf_model.config.encoder)
decoder = _tf_model.decoder.__class__(_tf_model.config.decoder)
# Make sure models are built
encoder(encoder.dummy_inputs)
decoder(decoder.dummy_inputs)
# Get the variable correspondence between `_tf_model` and `encoder` and `decoder`
encoder_variables = {}
for v in encoder.trainable_variables + encoder.non_trainable_variables:
encoder_variables["/".join(v.name.split("/")[1:])] = v
decoder_variables = {}
for v in decoder.trainable_variables + decoder.non_trainable_variables:
decoder_variables["/".join(v.name.split("/")[1:])] = v
_encoder_variables = {}
for v in _tf_model.encoder.trainable_variables + _tf_model.encoder.non_trainable_variables:
_encoder_variables["/".join(v.name.split("/")[2:])] = v
_decoder_variables = {}
for v in _tf_model.decoder.trainable_variables + _tf_model.decoder.non_trainable_variables:
_decoder_variables["/".join(v.name.split("/")[2:])] = v
# assign weight values to `encoder` and `decoder` from `_tf_model`
for name, v in encoder_variables.items():
v.assign(_encoder_variables[name])
for name, v in decoder_variables.items():
v.assign(_decoder_variables[name])
tf_model = TFVisionEncoderDecoderModel(encoder=encoder, decoder=decoder)
# Deal with `enc_to_dec_proj`
if hasattr(_tf_model, "enc_to_dec_proj"):
tf_model(tf_model.dummy_inputs)
tf_model.enc_to_dec_proj.kernel.assign(_tf_model.enc_to_dec_proj.kernel)
tf_model.enc_to_dec_proj.bias.assign(_tf_model.enc_to_dec_proj.bias)
with tempfile.TemporaryDirectory() as tmpdirname:
encoder_dir = os.path.join(tmpdirname, "encoder")
decoder_dir = os.path.join(tmpdirname, "decoder")
tf_model.encoder.save_pretrained(encoder_dir)
tf_model.decoder.save_pretrained(decoder_dir)
if hasattr(tf_model, "enc_to_dec_proj"):
enc_to_dec_proj_weight = torch.transpose(
torch.from_numpy(tf_model.enc_to_dec_proj.kernel.numpy()), 1, 0
)
enc_to_dec_proj_bias = torch.from_numpy(tf_model.enc_to_dec_proj.bias.numpy())
del _tf_model
del tf_model
gc.collect()
model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_dir, decoder_dir, encoder_from_tf=True, decoder_from_tf=True
)
# This is only for copying some specific attributes of this particular model.
model.config = config
if hasattr(model, "enc_to_dec_proj"):
model.enc_to_dec_proj.weight.data = enc_to_dec_proj_weight.contiguous()
model.enc_to_dec_proj.bias.data = enc_to_dec_proj_bias.contiguous()
return model
# At the moment fast initialization is not supported for composite models
if kwargs.get("_fast_init", False):
logger.warning(
"Fast initialization is currently not supported for VisionEncoderDecoderModel. "
"Falling back to slow initialization..."
)
kwargs["_fast_init"] = False
return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
@classmethod
def from_encoder_decoder_pretrained(
cls,
encoder_pretrained_model_name_or_path: str = None,
decoder_pretrained_model_name_or_path: str = None,
*model_args,
**kwargs,
) -> PreTrainedModel:
r"""
Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model
checkpoints.
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
the model, you need to first set it back in training mode with `model.train()`.
Params:
encoder_pretrained_model_name_or_path (`str`, *optional*):
Information necessary to initiate the image encoder. Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. An
example is `google/vit-base-patch16-224-in21k`.
- A path to a *directory* containing model weights saved using
[`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In
this case, `from_tf` should be set to `True` and a configuration object should be provided as
`config` argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
decoder_pretrained_model_name_or_path (`str`, *optional*, defaults to `None`):
Information necessary to initiate the text decoder. Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
user or organization name, like `dbmdz/bert-base-german-cased`.
- A path to a *directory* containing model weights saved using
[`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In
this case, `from_tf` should be set to `True` and a configuration object should be provided as
`config` argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
model_args (remaining positional arguments, *optional*):
All remaning positional arguments will be passed to the underlying model's `__init__` method.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
`output_attentions=True`).
- To update the encoder configuration, use the prefix *encoder_* for each configuration parameter.
- To update the decoder configuration, use the prefix *decoder_* for each configuration parameter.
- To update the parent model configuration, do not use a prefix for each configuration parameter.
Behaves differently depending on whether a `config` is provided or automatically loaded.
Example:
```python
>>> from transformers import VisionEncoderDecoderModel
>>> # initialize a vit-bert from a pretrained ViT and a pretrained BERT model. Note that the cross-attention layers will be randomly initialized
>>> model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
... "google/vit-base-patch16-224-in21k", "bert-base-uncased"
... )
>>> # saving model after fine-tuning
>>> model.save_pretrained("./vit-bert")
>>> # load fine-tuned model
>>> model = VisionEncoderDecoderModel.from_pretrained("./vit-bert")
```"""
kwargs_encoder = {
argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_")
}
kwargs_decoder = {
argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
}
# remove encoder, decoder kwargs from kwargs
for key in kwargs_encoder.keys():
del kwargs["encoder_" + key]
for key in kwargs_decoder.keys():
del kwargs["decoder_" + key]
# Load and initialize the encoder and decoder
# The distinction between encoder and decoder at the model level is made
# by the value of the flag `is_decoder` that we need to set correctly.
encoder = kwargs_encoder.pop("model", None)
if encoder is None:
if encoder_pretrained_model_name_or_path is None:
raise ValueError(
"If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has "
"to be defined."
)
if "config" not in kwargs_encoder:
encoder_config, kwargs_encoder = AutoConfig.from_pretrained(
encoder_pretrained_model_name_or_path, **kwargs_encoder, return_unused_kwargs=True
)
if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
logger.info(
f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model "
"from a decoder model. Cross-attention and casual mask are disabled."
)
encoder_config.is_decoder = False
encoder_config.add_cross_attention = False
kwargs_encoder["config"] = encoder_config
encoder = AutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder)
decoder = kwargs_decoder.pop("model", None)
if decoder is None:
if decoder_pretrained_model_name_or_path is None:
raise ValueError(
"If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has "
"to be defined."
)
if "config" not in kwargs_decoder:
decoder_config, kwargs_decoder = AutoConfig.from_pretrained(
decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True
)
if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
logger.info(
f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention"
f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if"
f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers."
)
decoder_config.is_decoder = True
decoder_config.add_cross_attention = True
kwargs_decoder["config"] = decoder_config
if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False:
logger.warning(
f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. "
f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, "
"make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` "
"passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a "
"`decoder_config` to `.from_encoder_decoder_pretrained(...)`"
)
decoder = AutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
# instantiate config with corresponding kwargs
config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs)
# make sure input & output embeddings is not tied
config.tie_word_embeddings = False
return cls(encoder=encoder, decoder=decoder, config=config)
@add_start_docstrings_to_model_forward(VISION_ENCODER_DECODER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
r"""
Returns:
Examples:
```python
>>> from transformers import AutoProcessor, VisionEncoderDecoderModel
>>> import requests
>>> from PIL import Image
>>> import torch
>>> processor = AutoProcessor.from_pretrained("microsoft/trocr-base-handwritten")
>>> model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
>>> # load image from the IAM dataset
>>> url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
>>> # training
>>> model.config.decoder_start_token_id = processor.tokenizer.cls_token_id
>>> model.config.pad_token_id = processor.tokenizer.pad_token_id
>>> model.config.vocab_size = model.config.decoder.vocab_size
>>> pixel_values = processor(image, return_tensors="pt").pixel_values
>>> text = "hello world"
>>> labels = processor.tokenizer(text, return_tensors="pt").input_ids
>>> outputs = model(pixel_values=pixel_values, labels=labels)
>>> loss = outputs.loss
>>> # inference (generation)
>>> generated_ids = model.generate(pixel_values)
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}
kwargs_decoder = {
argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
}
if encoder_outputs is None:
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
encoder_outputs = self.encoder(
pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs_encoder,
)
elif isinstance(encoder_outputs, tuple):
encoder_outputs = BaseModelOutput(*encoder_outputs)
encoder_hidden_states = encoder_outputs[0]
# optionally project encoder_hidden_states
if (
self.encoder.config.hidden_size != self.decoder.config.hidden_size
and self.decoder.config.cross_attention_hidden_size is None
):
encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)
# else:
encoder_attention_mask = None
if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
# Decode
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
inputs_embeds=decoder_inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
use_cache=use_cache,
past_key_values=past_key_values,
return_dict=return_dict,
**kwargs_decoder,
)
# Compute loss independent from decoder (as some shift the logits inside them)
loss = None
if labels is not None:
logits = decoder_outputs.logits if return_dict else decoder_outputs[0]
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.reshape(-1))
if not return_dict:
if loss is not None:
return (loss,) + decoder_outputs + encoder_outputs
else:
return decoder_outputs + encoder_outputs
return Seq2SeqLMOutput(
loss=loss,
logits=decoder_outputs.logits,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
):
decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, past_key_values=past_key_values)
decoder_attention_mask = decoder_inputs["attention_mask"] if "attention_mask" in decoder_inputs else None
input_dict = {
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"decoder_input_ids": decoder_inputs["input_ids"],
"encoder_outputs": encoder_outputs,
"past_key_values": decoder_inputs["past_key_values"],
"use_cache": use_cache,
}
return input_dict
def resize_token_embeddings(self, *args, **kwargs):
raise NotImplementedError(
"Resizing the embedding layers via the VisionEncoderDecoderModel directly is not supported.Please use the"
" respective methods of the wrapped decoder object (model.decoder.resize_token_embeddings(...))"
)
def _reorder_cache(self, past_key_values, beam_idx):
# apply decoder cache reordering here
return self.decoder._reorder_cache(past_key_values, beam_idx)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/gpt_sw3/__init__.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_import_structure = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_gpt_sw3"] = ["GPTSw3Tokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_sw3 import GPTSw3Tokenizer
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/gpt_sw3/convert_megatron_to_pytorch.py | # Copyright 2022 The HuggingFace Inc. team and the AI-Sweden team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Convert GPT-SW3 megatron checkpoints to pytorch"""
import argparse
import os
from os.path import isfile
import torch
from transformers import GPT2Config
def recursive_print(name, val, spaces=0):
# Format the message.
if name is None:
msg = None
else:
fmt = "." * max(0, spaces - 2) + "# {:" + str(50 - spaces) + "s}"
msg = fmt.format(name)
# Print and recurse (if needed).
if isinstance(val, dict):
if msg is not None:
print(msg)
for k in val.keys():
recursive_print(k, val[k], spaces + 2)
elif isinstance(val, torch.Tensor):
print(msg, ":", val.size())
else:
print(msg, ":", val)
def fix_query_key_value_ordering(param, num_splits, num_heads, hidden_size):
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
input_shape = param.size()
# other versions store [num_heads * num_splits * hidden_size, :]
saved_shape = (num_heads, num_splits, hidden_size) + input_shape[1:]
param = param.view(*saved_shape)
param = param.transpose(0, 1).contiguous()
param = param.view(*input_shape)
return param
def convert_megatron_checkpoint(sd_megatron, config):
"""
Converts a Megatron checkpoint to a HuggingFace GPT-SW3 checkpoint.
"""
n_positions = config.n_positions
layers = config.n_layer
vocab_size = config.vocab_size
heads = config.n_head
hidden_size_per_head = config.n_embd // config.n_head
word_embeddings = sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
sd_hf = {
"transformer.wte.weight": word_embeddings,
"transformer.wpe.weight": sd_megatron["model.language_model.embedding.position_embeddings.weight"],
"transformer.ln_f.weight": sd_megatron["model.language_model.encoder.final_layernorm.weight"],
"transformer.ln_f.bias": sd_megatron["model.language_model.encoder.final_layernorm.bias"],
}
pf = "model.language_model.encoder.layers."
for i in range(layers):
causal_mask = torch.tril(torch.ones((n_positions, n_positions), dtype=torch.bool))
causal_mask = causal_mask.view(1, 1, n_positions, n_positions)
sd_hf[f"transformer.h.{i}.attn.bias"] = causal_mask
sd_hf[f"transformer.h.{i}.attn.masked_bias"] = torch.tensor(-1e4, dtype=torch.bfloat16)
sd_hf[f"transformer.h.{i}.ln_1.weight"] = sd_megatron[f"{pf}{i}.input_layernorm.weight"]
sd_hf[f"transformer.h.{i}.ln_1.bias"] = sd_megatron[f"{pf}{i}.input_layernorm.bias"]
val1 = sd_megatron[f"{pf}{i}.self_attention.query_key_value.weight"]
val1 = fix_query_key_value_ordering(val1, 3, heads, hidden_size_per_head)
sd_hf[f"transformer.h.{i}.attn.c_attn.weight"] = val1.transpose(0, 1).contiguous()
val2 = sd_megatron[f"{pf}{i}.self_attention.query_key_value.bias"]
val2 = fix_query_key_value_ordering(val2, 3, heads, hidden_size_per_head)
sd_hf[f"transformer.h.{i}.attn.c_attn.bias"] = val2
sd_hf[f"transformer.h.{i}.attn.c_proj.weight"] = sd_megatron[f"{pf}{i}.self_attention.dense.weight"].transpose(
0, 1
)
sd_hf[f"transformer.h.{i}.attn.c_proj.bias"] = sd_megatron[f"{pf}{i}.self_attention.dense.bias"]
sd_hf[f"transformer.h.{i}.ln_2.weight"] = sd_megatron[f"{pf}{i}.post_attention_layernorm.weight"]
sd_hf[f"transformer.h.{i}.ln_2.bias"] = sd_megatron[f"{pf}{i}.post_attention_layernorm.bias"]
sd_hf[f"transformer.h.{i}.mlp.c_fc.weight"] = sd_megatron[f"{pf}{i}.mlp.dense_h_to_4h.weight"].transpose(0, 1)
sd_hf[f"transformer.h.{i}.mlp.c_fc.bias"] = sd_megatron[f"{pf}{i}.mlp.dense_h_to_4h.bias"]
sd_hf[f"transformer.h.{i}.mlp.c_proj.weight"] = sd_megatron[f"{pf}{i}.mlp.dense_4h_to_h.weight"].transpose(
0, 1
)
sd_hf[f"transformer.h.{i}.mlp.c_proj.bias"] = sd_megatron[f"{pf}{i}.mlp.dense_4h_to_h.bias"]
# For LM head, transformers' wants the matrix to weight embeddings.
sd_hf["lm_head.weight"] = word_embeddings
return sd_hf
def copy_config(config_hf, config_megatron):
"""Copy the config from Megatron to hf."""
config_hf.vocab_size = 64000
config_hf.n_positions = config_megatron["encoder_seq_length"]
config_hf.n_embd = config_megatron["hidden_size"]
config_hf.n_layer = config_megatron["num_layers"]
config_hf.n_head = config_megatron["num_attention_heads"]
config_hf.n_inner = config_megatron["ffn_hidden_size"]
config_hf.activation_function = "gelu"
config_hf.resid_pdrop = 0.1
config_hf.embd_pdrop = 0.1
config_hf.attn_pdrop = 0.1
config_hf.layer_norm_epsilon = config_megatron["layernorm_epsilon"] # 1e-5
config_hf.initializer_range = config_megatron["init_method_std"] # 0.02
config_hf.apply_query_key_layer_scaling = config_megatron["apply_query_key_layer_scaling"] # True
config_hf.normalize_attention_scores = True
config_hf.use_cache = True
# This identifies the 6.7B (7B) model which uses a different tokenizer
if config_megatron["hidden_size"] == 4096:
config_hf.bos_token_id = 1 # <|endoftext|>
config_hf.eos_token_id = 1 # <|endoftext|>
config_hf.pad_token_id = 0 # <unk>
else:
config_hf.bos_token_id = 2 # <s>
config_hf.eos_token_id = 3 # <|endoftext|>
config_hf.pad_token_id = 0 # <pad>
return config_hf
def main(args):
print(args)
checkpoint_path = args.checkpoint_path
save_path = args.save_path
if isfile(checkpoint_path):
raise FileNotFoundError(f"ERROR! could not find file {checkpoint_path}")
# Load the model.
checkpoint = torch.load(checkpoint_path, map_location="cpu")
# Load the config.
config_megatron = checkpoint["hyper_parameters"]["cfg"]
config_hf = GPT2Config()
config_hf = copy_config(config_hf=config_hf, config_megatron=config_megatron)
config_hf.architectures = ["GPT2LMHeadModel"]
sd_megatron = checkpoint["state_dict"]
# Convert.
print("Converting")
sd_hf = convert_megatron_checkpoint(sd_megatron, config_hf)
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(None, sd_hf)
config_hf.tokenizer_class = "GPTSw3Tokenizer"
# Store the config to file.
print("Saving config")
config_hf.save_pretrained(save_path)
# Store the state_dict to file.
output_checkpoint_file = os.path.join(save_path, "pytorch_model.bin")
print(f'Saving checkpoint to "{output_checkpoint_file}"')
torch.save(sd_hf, output_checkpoint_file)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path",
type=str,
required=True,
help="e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000",
)
parser.add_argument("--save_path", type=str, required=True, help="e.g. /home/user/gpt-sw3/hf")
parser.add_argument("--print-checkpoint-structure", action="store_true")
_args = parser.parse_args()
main(_args)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py | """The tokenizer used by the GPT-SW3 models."""
import os
import re
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"AI-Sweden-Models/gpt-sw3-126m": "https://huggingface.co/AI-Sweden-Models/gpt-sw3-126m/resolve/main/spiece.model",
"AI-Sweden-Models/gpt-sw3-356m": "https://huggingface.co/AI-Sweden-Models/gpt-sw3-356m/resolve/main/spiece.model",
"AI-Sweden-Models/gpt-sw3-1.3b": "https://huggingface.co/AI-Sweden-Models/gpt-sw3-1.3b/resolve/main/spiece.model",
"AI-Sweden-Models/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b/resolve/main/spiece.model",
"AI-Sweden-Models/gpt-sw3-6.7b-v2": "https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2/resolve/main/spiece.model",
"AI-Sweden-Models/gpt-sw3-20b": "https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b/resolve/main/spiece.model",
"AI-Sweden-Models/gpt-sw3-40b": "https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b/resolve/main/spiece.model",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"AI-Sweden-Models/gpt-sw3-126m": 2048,
"AI-Sweden-Models/gpt-sw3-356m": 2048,
"AI-Sweden-Models/gpt-sw3-1.3b": 2048,
"AI-Sweden-Models/gpt-sw3-6.7b": 2048,
"AI-Sweden-Models/gpt-sw3-6.7b-v2": 2048,
"AI-Sweden-Models/gpt-sw3-20b": 2048,
"AI-Sweden-Models/gpt-sw3-40b": 2048,
}
class GPTSw3Tokenizer(PreTrainedTokenizer):
"""
Construct an GPTSw3 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Example usage:
```python
>>> from transformers import GPTSw3Tokenizer
>>> tokenizer = GPTSw3Tokenizer.from_pretrained("AI-Sweden-Models/gpt-sw3-126m")
>>> tokenizer("Svenska är kul!")["input_ids"]
[1814, 377, 3617, 63504]
```
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
do_lower_case (`bool`, *optional*, defaults to `False`):
Whether or not to lowercase the input when tokenizing.
remove_space (`bool`, *optional*, defaults to `False`):
Whether or not to strip the text when tokenizing (removing excess spaces before and after the string).
keep_accents (`bool`, *optional*, defaults to `False`):
Whether or not to keep accents when tokenizing.
pad_token (`str`, *optional*):
The token used for padding, for example when batching sequences of different lengths. If not provided, will
default to '<pad>' or '<unk>' depending on model size.
unk_token (`str`, *optional*):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead. If not provided, will default to '<unk>'.
eos_token (`str`, *optional*):
The end of sequence token seen during pretraining. If not provided, will default to '<|endoftext|>'
bos_token (`str`, *optional*):
The beginning of sequence token that can be used for downstream task, was not seen during pretraining. If
not provided, will default to '<s>' or '<|endoftext|>', depending on model size.
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
Attributes:
sp_model (`SentencePieceProcessor`):
The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
whitespaces (`set`):
The whitespaces that are replaced in the whitespace normalization in preprocessing.
non_printing_characters_re (`Pattern`):
The compiled regular expression to remove non-printing characters in preprocessing.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
do_lower_case=False,
remove_space=False,
keep_accents=False,
pad_token=None,
unk_token=None,
eos_token=None,
bos_token=None,
sp_model_kwargs: Optional[Dict[str, Any]] = None,
**kwargs,
) -> None:
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
name_or_path = kwargs.get("name_or_path")
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored"
)
name_or_path = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
eos_token = "<|endoftext|>" if eos_token is None else eos_token
unk_token = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
pad_token = unk_token if pad_token is None else pad_token
bos_token = eos_token if bos_token is None else bos_token
else:
pad_token = "<pad>" if pad_token is None else pad_token
bos_token = "<s>" if bos_token is None else bos_token
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.keep_accents = keep_accents
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(vocab_file)
# Used for whitespace normalization in input texts
# fmt : off
self.whitespaces = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
self.non_printing_characters_re = re.compile(
f"[{''.join(map(chr, list(range(0, 9)) + list(range(11, 32)) + list(range(127, 160)) + [160, 173, 8203]))}]"
)
super().__init__(
do_lower_case=do_lower_case,
remove_space=remove_space,
keep_accents=keep_accents,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
pad_token=pad_token,
sp_model_kwargs=self.sp_model_kwargs,
**kwargs,
)
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.__getstate__
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.__setstate__
def __setstate__(self, d):
self.__dict__ = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def vocab_size(self) -> int:
return len(self.sp_model)
def preprocess_text(self, text: str) -> str:
"""
Returns the preprocessed text. This procedure is identical to what was used when training the tokenizer.
"""
# Remove non-printing characters
text = self.non_printing_characters_re.sub("", text)
# Normalize whitespaces
text = "".join([char if char not in self.whitespaces else " " for char in text])
# NFC Unicode normalization
text = unicodedata.normalize("NFC", text)
return text
def _tokenize(self, text: str, **kwargs) -> List[str]:
text = self.preprocess_text(text)
return self.sp_model.encode(text, out_type=str)
def _convert_token_to_id(self, token: str) -> int:
"""Converts a token (str) to an id (int) using the vocab."""
return self.sp_model.PieceToId(token)
def _convert_id_to_token(self, index: int) -> str:
"""Converts an index (int) to a token (str) using the vocab."""
return self.sp_model.IdToPiece(index)
@staticmethod
def clean_up_tokenization(out_string: str) -> str:
"""Returns the input string, this function is overridden to remove the default clean up."""
return out_string
def convert_tokens_to_string(self, tokens: List[str]) -> str:
"""Converts a sequence of tokens (strings) to a single string. Special tokens remain intact."""
current_sub_tokens = []
out_string = ""
prev_is_special = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(current_sub_tokens) + token
prev_is_special = True
current_sub_tokens = []
else:
current_sub_tokens.append(token)
prev_is_special = False
out_string += self.sp_model.decode(current_sub_tokens)
return out_string
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.get_vocab
def get_vocab(self) -> Dict[str, int]:
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.save_vocabulary
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, out_vocab_file)
elif not os.path.isfile(self.vocab_file):
with open(out_vocab_file, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
return (out_vocab_file,)
def encode_fast(
self, text: Union[str, List[str]], return_tensors: Union[str, bool] = False
) -> Union[List[int], List[List[int]], "torch.Tensor"]:
"""
Encodes a text or batch of texts to token ids using preprocessing and the raw SP tokenizer. This has reduced
functionality but is often much faster.
Does NOT handle special tokens correctly, these can manually be added as ids afterwards.
Does NOT support padding, these can manually be added as ids afterwards.
Use default HuggingFace tokenization methods for full functionality.
Args:
text (`str` or `List[str]`): One or several text(s) to convert to token ids.
return_tensors (`str` or `bool`): Returns PyTorch tensors if set to True or "pt"
Returns:
`List[int]`, `List[List[int]]`, or `torch.Tensor`: The encoded text(s) as token ids.
"""
if isinstance(text, str):
text = self.preprocess_text(text)
token_ids = self.sp_model.encode(text)
else:
text = [self.preprocess_text(t) for t in text]
token_ids = self.sp_model.encode(text)
if return_tensors is True or return_tensors == "pt":
token_ids = torch.tensor(token_ids)
return token_ids
def decode_fast(self, token_ids: Union[int, List[int]]) -> str:
"""
Encodes a text or batch of texts to token ids using preprocessing and the raw SP tokenizer. This has reduced
functionality but is often much faster.
Args:
token_ids (`int` or `List[int]`): Encoded token or text as token id(s).
Returns:
`str`: Decoded text
"""
return self.sp_model.decode(token_ids)
@property
def default_chat_template(self):
"""
This chat template formats messages like an instant messenger chat log, with "User:" and "Bot:" strings
preceding messages. BOS tokens are added between all messages.
"""
logger.warning_once(
"\nNo chat template is defined for this tokenizer - using the default template "
f"for the {self.__class__.__name__} class. If the default is not appropriate for "
"your model, please set `tokenizer.chat_template` to an appropriate template. "
"See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n"
)
return (
"{{ eos_token }}{{ bos_token }}"
"{% for message in messages %}"
"{% if message['role'] == 'user' %}{{ 'User: ' + message['content']}}"
"{% else %}{{ 'Bot: ' + message['content']}}{% endif %}"
"{{ message['text'] }}{{ bos_token }}"
"{% endfor %}"
"Bot:"
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/tvp/processing_tvp.py | # coding=utf-8
# Copyright 2023 The Intel AIA Team Authors, and HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License=, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing=, software
# distributed under the License is distributed on an "AS IS" BASIS=,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND=, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for TVP.
"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class TvpProcessor(ProcessorMixin):
r"""
Constructs an TVP processor which wraps a TVP image processor and a Bert tokenizer into a single processor.
[`TvpProcessor`] offers all the functionalities of [`TvpImageProcessor`] and [`BertTokenizerFast`]. See the
[`~TvpProcessor.__call__`] and [`~TvpProcessor.decode`] for more information.
Args:
image_processor ([`TvpImageProcessor`], *optional*):
The image processor is a required input.
tokenizer ([`BertTokenizerFast`], *optional*):
The tokenizer is a required input.
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "TvpImageProcessor"
tokenizer_class = ("BertTokenizer", "BertTokenizerFast")
def __init__(self, image_processor=None, tokenizer=None, **kwargs):
if image_processor is None:
raise ValueError("You need to specify an `image_processor`.")
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`.")
super().__init__(image_processor, tokenizer)
def __call__(self, text=None, videos=None, return_tensors=None, **kwargs):
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to BertTokenizerFast's [`~BertTokenizerFast.__call__`] if `text` is not `None` to encode
the text. To prepare the image(s), this method forwards the `videos` and `kwargs` arguments to
TvpImageProcessor's [`~TvpImageProcessor.__call__`] if `videos` is not `None`. Please refer to the doctsring of
the above two methods for more information.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
videos (`List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`, `List[List[PIL.Image.Image]]`, `List[List[np.ndarrray]]`,:
`List[List[torch.Tensor]]`): The video or batch of videos to be prepared. Each video should be a list
of frames, which can be either PIL images or NumPy arrays. In case of NumPy arrays/PyTorch tensors,
each frame should be of shape (H, W, C), where H and W are frame height and width, and C is a number of
channels.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
`None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `videos` is not `None`.
"""
max_text_length = kwargs.pop("max_text_length", None)
if text is None and videos is None:
raise ValueError("You have to specify either text or videos. Both cannot be none.")
encoding = {}
if text is not None:
textual_input = self.tokenizer.batch_encode_plus(
text,
truncation=True,
padding="max_length",
max_length=max_text_length,
pad_to_max_length=True,
return_tensors=return_tensors,
return_token_type_ids=False,
**kwargs,
)
encoding.update(textual_input)
if videos is not None:
image_features = self.image_processor(videos, return_tensors=return_tensors, **kwargs)
encoding.update(image_features)
return BatchEncoding(data=encoding, tensor_type=return_tensors)
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
def post_process_video_grounding(self, logits, video_durations):
"""
Compute the time of the video.
Args:
logits (`torch.Tensor`):
The logits output of TvpForVideoGrounding.
video_durations (`float`):
The video's duration.
Returns:
start (`float`):
The start time of the video.
end (`float`):
The end time of the video.
"""
start, end = (
round(logits.tolist()[0][0] * video_durations, 1),
round(logits.tolist()[0][1] * video_durations, 1),
)
return start, end
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/tvp/image_processing_tvp.py | # coding=utf-8
# Copyright 2023 The Intel AIA Team Authors, and HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License=, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing=, software
# distributed under the License is distributed on an "AS IS" BASIS=,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND=, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Image processor class for TVP."""
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
PaddingMode,
flip_channel_order,
pad,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
logger = logging.get_logger(__name__)
# Copied from transformers.models.vivit.image_processing_vivit.make_batched
def make_batched(videos) -> List[List[ImageInput]]:
if isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)) and is_valid_image(videos[0][0]):
return videos
elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]):
return [videos]
elif is_valid_image(videos):
return [[videos]]
raise ValueError(f"Could not make batched video from {videos}")
def get_resize_output_image_size(
input_image: np.ndarray,
max_size: int = 448,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> Tuple[int, int]:
height, width = get_image_size(input_image, input_data_format)
if height >= width:
ratio = width * 1.0 / height
new_height = max_size
new_width = new_height * ratio
else:
ratio = height * 1.0 / width
new_width = max_size
new_height = new_width * ratio
size = (int(new_height), int(new_width))
return size
class TvpImageProcessor(BaseImageProcessor):
r"""
Constructs a Tvp image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
`do_resize` parameter in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"longest_edge": 448}`):
Size of the output image after resizing. The longest edge of the image will be resized to
`size["longest_edge"]` while maintaining the aspect ratio of the original image. Can be overriden by
`size` in the `preprocess` method.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
`preprocess` method.
do_center_crop (`bool`, *optional*, defaults to `True`):
Whether to center crop the image to the specified `crop_size`. Can be overridden by the `do_center_crop`
parameter in the `preprocess` method.
crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 448, "width": 448}`):
Size of the image after applying the center crop. Can be overridden by the `crop_size` parameter in the
`preprocess` method.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
parameter in the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Defines the scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter
in the `preprocess` method.
do_pad (`bool`, *optional*, defaults to `True`):
Whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess` method.
pad_size (`Dict[str, int]`, *optional*, defaults to `{"height": 448, "width": 448}`):
Size of the image after applying the padding. Can be overridden by the `pad_size` parameter in the
`preprocess` method.
constant_values (`Union[float, Iterable[float]]`, *optional*, defaults to 0):
The fill value to use when padding the image.
pad_mode (`PaddingMode`, *optional*, defaults to `PaddingMode.CONSTANT`):
Use what kind of mode in padding.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
method.
do_flip_channel_order (`bool`, *optional*, defaults to `True`):
Whether to flip the color channels from RGB to BGR. Can be overridden by the `do_flip_channel_order`
parameter in the `preprocess` method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_pad: bool = True,
pad_size: Dict[str, int] = None,
constant_values: Union[float, Iterable[float]] = 0,
pad_mode: PaddingMode = PaddingMode.CONSTANT,
do_normalize: bool = True,
do_flip_channel_order: bool = True,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
size = size if size is not None else {"longest_edge": 448}
crop_size = crop_size if crop_size is not None else {"height": 448, "width": 448}
pad_size = pad_size if pad_size is not None else {"height": 448, "width": 448}
self.do_resize = do_resize
self.size = size
self.do_center_crop = do_center_crop
self.crop_size = crop_size
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_pad = do_pad
self.pad_size = pad_size
self.constant_values = constant_values
self.pad_mode = pad_mode
self.do_normalize = do_normalize
self.do_flip_channel_order = do_flip_channel_order
self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BILINEAR,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Resize an image.
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Size of the output image. If `size` is of the form `{"height": h, "width": w}`, the output image will
have the size `(h, w)`. If `size` is of the form `{"longest_edge": s}`, the output image will have its
longest edge of length `s` while keeping the aspect ratio of the original image.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
Resampling filter to use when resiizing the image.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
size = get_size_dict(size, default_to_square=False)
if "height" in size and "width" in size:
output_size = (size["height"], size["width"])
elif "longest_edge" in size:
output_size = get_resize_output_image_size(image, size["longest_edge"], input_data_format)
else:
raise ValueError(f"Size must have 'height' and 'width' or 'longest_edge' as keys. Got {size.keys()}")
return resize(
image,
size=output_size,
resample=resample,
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
def pad_image(
self,
image: np.ndarray,
pad_size: Dict[str, int] = None,
constant_values: Union[float, Iterable[float]] = 0,
pad_mode: PaddingMode = PaddingMode.CONSTANT,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
):
"""
Pad an image with zeros to the given size.
Args:
image (`np.ndarray`):
Image to pad.
pad_size (`Dict[str, int]`)
Size of the output image with pad.
constant_values (`Union[float, Iterable[float]]`)
The fill value to use when padding the image.
pad_mode (`PaddingMode`)
The pad mode, default to PaddingMode.CONSTANT
data_format (`ChannelDimension` or `str`, *optional*)
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
height, width = get_image_size(image, channel_dim=input_data_format)
max_height = pad_size.get("height", height)
max_width = pad_size.get("width", width)
pad_right, pad_bottom = max_width - width, max_height - height
if pad_right < 0 or pad_bottom < 0:
raise ValueError("The padding size must be greater than image size")
padding = ((0, pad_bottom), (0, pad_right))
padded_image = pad(
image,
padding,
mode=pad_mode,
constant_values=constant_values,
data_format=data_format,
input_data_format=input_data_format,
)
return padded_image
def _preprocess_image(
self,
image: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_center_crop: bool = None,
crop_size: Dict[str, int] = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_pad: bool = True,
pad_size: Dict[str, int] = None,
constant_values: Union[float, Iterable[float]] = None,
pad_mode: PaddingMode = None,
do_normalize: bool = None,
do_flip_channel_order: bool = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""Preprocesses a single image."""
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True.")
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True.")
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True.")
if do_pad and pad_size is None:
raise ValueError("Padding size must be specified if do_pad is True.")
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True.")
# All transformations expect numpy arrays.
image = to_numpy_array(image)
if do_resize:
image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
if do_center_crop:
image = self.center_crop(image, size=crop_size, input_data_format=input_data_format)
if do_rescale:
image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
if do_normalize:
image = self.normalize(
image=image.astype(np.float32), mean=image_mean, std=image_std, input_data_format=input_data_format
)
if do_pad:
image = self.pad_image(
image=image,
pad_size=pad_size,
constant_values=constant_values,
pad_mode=pad_mode,
input_data_format=input_data_format,
)
# the pretrained checkpoints assume images are BGR, not RGB
if do_flip_channel_order:
image = flip_channel_order(image=image, input_data_format=input_data_format)
image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
return image
def preprocess(
self,
videos: Union[ImageInput, List[ImageInput], List[List[ImageInput]]],
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_center_crop: bool = None,
crop_size: Dict[str, int] = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_pad: bool = None,
pad_size: Dict[str, int] = None,
constant_values: Union[float, Iterable[float]] = None,
pad_mode: PaddingMode = None,
do_normalize: bool = None,
do_flip_channel_order: bool = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: ChannelDimension = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> PIL.Image.Image:
"""
Preprocess an image or batch of images.
Args:
videos (`ImageInput` or `List[ImageInput]` or `List[List[ImageInput]]`):
Frames to preprocess.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after applying resize.
resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only
has an effect if `do_resize` is set to `True`.
do_center_crop (`bool`, *optional*, defaults to `self.do_centre_crop`):
Whether to centre crop the image.
crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
Size of the image after applying the centre crop.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image values between [0 - 1].
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_pad (`bool`, *optional*, defaults to `True`):
Whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess` method.
pad_size (`Dict[str, int]`, *optional*, defaults to `{"height": 448, "width": 448}`):
Size of the image after applying the padding. Can be overridden by the `pad_size` parameter in the
`preprocess` method.
constant_values (`Union[float, Iterable[float]]`, *optional*, defaults to 0):
The fill value to use when padding the image.
pad_mode (`PaddingMode`, *optional*, defaults to "PaddingMode.CONSTANT"):
Use what kind of mode in padding.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image.
do_flip_channel_order (`bool`, *optional*, defaults to `self.do_flip_channel_order`):
Whether to flip the channel order of the image.
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
Image mean.
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
Image standard deviation.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the inferred channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
do_resize = do_resize if do_resize is not None else self.do_resize
resample = resample if resample is not None else self.resample
do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
do_pad = do_pad if do_pad is not None else self.do_pad
pad_size = pad_size if pad_size is not None else self.pad_size
constant_values = constant_values if constant_values is not None else self.constant_values
pad_mode = pad_mode if pad_mode else self.pad_mode
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
do_flip_channel_order = (
do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
)
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.image_std
size = size if size is not None else self.size
size = get_size_dict(size, default_to_square=False)
crop_size = crop_size if crop_size is not None else self.crop_size
crop_size = get_size_dict(crop_size, param_name="crop_size")
if not valid_images(videos):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
videos = make_batched(videos)
videos = [
np.array(
[
self._preprocess_image(
image=img,
do_resize=do_resize,
size=size,
resample=resample,
do_center_crop=do_center_crop,
crop_size=crop_size,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_pad=do_pad,
pad_size=pad_size,
constant_values=constant_values,
pad_mode=pad_mode,
do_normalize=do_normalize,
do_flip_channel_order=do_flip_channel_order,
image_mean=image_mean,
image_std=image_std,
data_format=data_format,
input_data_format=input_data_format,
)
for img in video
]
)
for video in videos
]
data = {"pixel_values": videos}
return BatchFeature(data=data, tensor_type=return_tensors)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/tvp/__init__.py | # coding=utf-8
# Copyright 2023 The Intel AIA Team Authors, and HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License=, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing=, software
# distributed under the License is distributed on an "AS IS" BASIS=,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND=, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {
"configuration_tvp": [
"TVP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TvpConfig",
],
"processing_tvp": ["TvpProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["image_processing_tvp"] = ["TvpImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tvp"] = [
"TVP_PRETRAINED_MODEL_ARCHIVE_LIST",
"TvpModel",
"TvpPreTrainedModel",
"TvpForVideoGrounding",
]
if TYPE_CHECKING:
from .configuration_tvp import (
TVP_PRETRAINED_CONFIG_ARCHIVE_MAP,
TvpConfig,
)
from .processing_tvp import TvpProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_tvp import TvpImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tvp import (
TVP_PRETRAINED_MODEL_ARCHIVE_LIST,
TvpForVideoGrounding,
TvpModel,
TvpPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/tvp/modeling_tvp.py | # coding=utf-8
# Copyright 2023 The Intel AIA Team Authors, and HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License=, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing=, software
# distributed under the License is distributed on an "AS IS" BASIS=,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND=, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch TVP Model"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ModelOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import prune_linear_layer
from ...utils import logging
from ..auto import AutoBackbone
from .configuration_tvp import TvpConfig
logger = logging.get_logger(__name__)
TVP_PRETRAINED_MODEL_ARCHIVE_LIST = [
"Intel/tvp-base",
"Intel/tvp-base-ANet",
# See all Tvp models at https://huggingface.co/models?filter=tvp
]
@dataclass
class TvpVideoGroundingOutput(ModelOutput):
"""
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
Temporal-Distance IoU loss for video grounding.
logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
Contains start_time/duration and end_time/duration. It is the time slot of the videos corresponding to the
input texts.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of
the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
class TvpLoss(nn.Module):
"""
This class computes the losses for `TvpForVideoGrounding`. The process happens in two steps: 1) we compute
hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair of matched
ground-truth / prediction (supervise class and box).
Args:
losses (`List[str]`):
List of all the losses to be applied.
"""
def __init__(self, losses):
super().__init__()
self.loss_map = {
"iou": self.loss_iou,
"distance": self.loss_distance,
"duration": self.loss_duration,
}
for loss in losses:
if loss not in self.loss_map:
raise ValueError(f"Loss {loss} not supported")
self.losses = losses
def loss_iou(self, start_time, end_time, candidates_start_time, candidates_end_time, duration):
"""
Measure the intersection over union.
"""
inter = torch.min(candidates_end_time, end_time) - torch.max(candidates_start_time, start_time)
union = torch.max(candidates_end_time, end_time) - torch.min(candidates_start_time, start_time)
iou = 1 - inter.clamp(min=0) / union
return iou
def loss_distance(self, start_time, end_time, candidates_start_time, candidates_end_time, duration):
"""
Measure the distance of mid points.
"""
mid_candidates = torch.div(torch.add(candidates_start_time, candidates_end_time), 2.0)
mid_groundtruth = torch.div(torch.add(start_time, end_time), 2.0)
distance_diff = torch.div(
torch.max(mid_candidates, mid_groundtruth) - torch.min(mid_candidates, mid_groundtruth), duration
).clamp(min=0.2)
return distance_diff
def loss_duration(self, start_time, end_time, candidates_start_time, candidates_end_time, duration):
"""
Measure the difference of duration.
"""
duration_candidates = torch.sub(candidates_end_time, candidates_start_time)
duration_groundtruth = torch.sub(end_time, start_time)
duration_diff = torch.square(torch.div(torch.sub(duration_candidates, duration_groundtruth), duration))
duration_diff = duration_diff.clamp(min=0.4)
return duration_diff
def forward(self, logits, labels):
"""
This performs the loss computation.
Args:
logits (`torch.FloatTensor`):
The output logits of head module.
labels (`List[torch.FloatTensor]`):
List of tensors ([start, end, duration]), which contains start time, end time of the video corresponding to the text, and also the duration.
"""
duration, start_time, end_time = labels
candidates = torch.mul(logits, duration)
candidates_start_time, candidates_end_time = candidates[:, 0].float(), candidates[:, 1].float()
losses_dict = {}
for loss in self.losses:
losses_dict.update(
{loss: self.loss_map[loss](start_time, end_time, candidates_start_time, candidates_end_time, duration)}
)
return losses_dict
class TvpVisionModel(nn.Module):
def __init__(self, config):
super().__init__()
self.backbone = AutoBackbone.from_config(config.backbone_config)
self.grid_encoder_conv = nn.Conv2d(
config.backbone_config.hidden_sizes[-1],
config.hidden_size,
kernel_size=3,
stride=1,
padding=1,
groups=1,
bias=False,
)
def forward(self, pixel_values):
batch_size, num_frames, num_channels, height, width = pixel_values.shape
# (batch_size * num_frames, num_channels, height, width)
pixel_values = pixel_values.view(batch_size * num_frames, num_channels, height, width)
grid_feat_outputs = self.backbone(pixel_values)["feature_maps"][0]
grid = self.grid_encoder_conv(grid_feat_outputs)
grid = nn.functional.max_pool2d(grid, kernel_size=2, stride=2)
grid = nn.functional.relu(grid, inplace=True)
new_channel, new_height, new_width = grid.shape[-3:]
# (batch_size, num_frames, num_channels, height, width)
grid = grid.view(batch_size, num_frames, new_channel, new_height, new_width)
# (batch_size, num_frames, height, width, num_channels)
grid = grid.permute(0, 1, 3, 4, 2)
return grid
class TvpVisualInputEmbedding(nn.Module):
"""
Takes input of both image and video (multi-frame)
"""
def __init__(self, config):
super().__init__()
# sequence embedding
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.row_position_embeddings = nn.Embedding(config.max_grid_row_position_embeddings, config.hidden_size)
self.col_position_embeddings = nn.Embedding(config.max_grid_col_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(1, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def add_2d_positional_embeddings(self, grid):
"""
Args:
grid: (batch_size, height, width, hidden_dim)
Returns:
grid + col_position_embeddings.view(*col_shape): (batch_size, *, height, width, hidden_dim)
"""
batch_size, height, width, hidden_dim = grid.shape
# add row-wise position embeddings
row_position_ids = torch.arange(height, dtype=torch.long, device=grid.device) # (height, )
row_position_embeddings = self.row_position_embeddings(row_position_ids) # (height, hidden_dim)
row_shape = (1,) * (len(grid.shape) - 3) + (height, 1, hidden_dim) # (1, height, 1, hidden_dim)
grid = grid + row_position_embeddings.view(*row_shape) # broadcast automatically
# add column-wise position embeddings
col_position_ids = torch.arange(width, dtype=torch.long, device=grid.device) # (width, )
col_position_embeddings = self.col_position_embeddings(col_position_ids) # (width, hidden_dim)
col_shape = (batch_size, 1, width, hidden_dim) # (1, 1, width, hidden_dim)
return grid + col_position_embeddings.view(*col_shape) # broadcast automatically
def forward(self, grid):
"""
Args:
grid: Array of shape (batch_size, num_frames, height, width, num_channels).
It contains processed frames extracted from videos, and is generated by Tvp image preprocessor. Note,
num_frames can be 1
Returns:
embeddings: The embedding of grid with size (batch_size, height*width, num_channels)
"""
batch_size, num_frames, height, width, num_channels = grid.shape
# temporal mean pooling, (batch_size, height, width, hidden_size)
grid = grid.mean(1)
grid = self.add_2d_positional_embeddings(grid)
# image token sequence, (batch_size, height*width, num_channels)
visual_tokens = grid.view(batch_size, -1, num_channels)
visual_tokens_shape = visual_tokens.shape[:-1]
device = visual_tokens.device
# image token type embeddings.
token_type_ids = torch.zeros(visual_tokens_shape, dtype=torch.long, device=device)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = visual_tokens + token_type_embeddings
embeddings = self.layer_norm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class TvpTextInputEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
device = input_ids.device if input_ids is not None else inputs_embeds.device
if position_ids is None:
position_ids = torch.arange(seq_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).expand(input_shape)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = self.layer_norm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class TvpAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size {config.hidden_size} is not a multiple of the number of attention heads {config.num_attention_heads}"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.attn_dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
mask = torch.ones(self.num_attention_heads, self.attention_head_size)
heads = set(heads) - self.pruned_heads # Convert to set and remove already pruned heads
for head in heads:
# Compute how many pruned heads are before the head and move the index accordingly
head = head - sum(1 if h < head else 0 for h in self.pruned_heads)
mask[head] = 0
mask = mask.view(-1).contiguous().eq(1)
index = torch.arange(len(mask))[mask].long()
# Prune linear layers
self.query = prune_linear_layer(self.query, index)
self.key = prune_linear_layer(self.key, index)
self.value = prune_linear_layer(self.value, index)
self.dense = prune_linear_layer(self.dense, index, dim=1)
# Update hyper params and store pruned heads
self.num_attention_heads = self.num_attention_heads - len(heads)
self.all_head_size = self.attention_head_size * self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def _reshape(self, tensor: torch.Tensor, sequence_length: int, batch_size: int):
return (
tensor.view(batch_size, sequence_length, self.num_attention_heads, self.attention_head_size)
.transpose(1, 2)
.contiguous()
)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions: Optional[bool] = None,
):
batch_size, sequence_length = hidden_states.shape[:2]
mixed_query_layer = self.query(hidden_states)
mixed_key_layer = self.key(hidden_states)
mixed_value_layer = self.value(hidden_states)
query_layer = self._reshape(mixed_query_layer, sequence_length, batch_size)
key_layer = self._reshape(mixed_key_layer, sequence_length, batch_size)
value_layer = self._reshape(mixed_value_layer, sequence_length, batch_size)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.attn_dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
attn_output = torch.matmul(attention_probs, value_layer)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(batch_size, sequence_length, self.all_head_size)
attn_output = self.dense(attn_output)
attn_output = self.dropout(attn_output)
attn_output = self.layer_norm(attn_output + hidden_states)
# add attentions if we output them
outputs = (attn_output, attention_probs) if output_attentions else (attn_output,)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Tvp
class TvpIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class TvpOutputLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.layer_norm(hidden_states + input_tensor)
return hidden_states
class TvpEncodeLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = TvpAttention(config)
self.intermediate = TvpIntermediate(config)
self.output = TvpOutputLayer(config)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions: Optional[bool] = None,
):
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
outputs = (layer_output,) + outputs
return outputs
class TvpEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([TvpEncodeLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
attention_mask=None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
return_dict = return_dict if return_dict is not None else self.config.return_dict
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
all_hidden_states = ()
all_attentions = ()
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
(head_mask[i] if head_mask is not None else None),
output_attentions,
)
else:
layer_outputs = layer_module(hidden_states, attention_mask, head_mask[i], output_attentions)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
outputs = (hidden_states,)
if output_hidden_states:
outputs = outputs + (all_hidden_states,)
if output_attentions:
outputs = outputs + (all_attentions,)
return outputs # last-layer hidden state, (all hidden states), (all attentions)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states if output_hidden_states else None,
attentions=all_attentions if output_attentions else None,
)
# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->Tvp
class TvpPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
class TvpPreTrainedModel(PreTrainedModel):
"""An abstract class to handle weights initialization and
a simple interface for downloading and loading pretrained models.
"""
config_class = TvpConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Embedding)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
if module.bias is not None:
nn.init.constant_(module.bias, 0)
TVP_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`TvpConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
TVP_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input
IDs?](../glossary#input-ids)
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`TvpImageProcessor`]. See [`TvpImageProcessor.__call__`]
for details.
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class TvpFrameDownPadPrompter(nn.Module):
"""
Pad frames extracted from videos only at the bottom.
"""
def __init__(self, config):
if config.visual_prompter_apply not in ("add", "replace", "remove"):
raise ValueError("`visual_prompter_apply` must be in (add, replace, remove)")
super().__init__()
self.visual_prompt_size = config.visual_prompt_size
self.frame_num = config.frame_num
self.max_img_size = config.max_img_size
self.visual_prompter_apply = config.visual_prompter_apply
self.pad_down = nn.Parameter(
torch.randn([1, config.frame_num, 3, config.visual_prompt_size, config.max_img_size])
)
def forward(self, pixel_values):
if self.visual_prompter_apply != "add":
visual_prompt_mask = torch.ones(
[self.max_img_size, self.max_img_size], dtype=pixel_values.dtype, device=pixel_values.device
)
visual_prompt_mask[self.max_img_size - self.visual_prompt_size : self.max_img_size, :] = 0.0
pixel_values *= visual_prompt_mask
if self.visual_prompter_apply != "remove":
prompt = torch.zeros(
[pixel_values.shape[0], pixel_values.shape[1], 3, self.max_img_size, self.max_img_size],
device=pixel_values.device,
)
start_point = self.max_img_size - self.visual_prompt_size
prompt[:, :, :, start_point : self.max_img_size, :] = self.pad_down
pixel_values += prompt.to(pixel_values.dtype)
return pixel_values
class TvpFramePadPrompter(nn.Module):
"""
Pad frames extracted from videos in the surroundings.
"""
def __init__(self, config):
if config.visual_prompter_apply not in ("add", "replace", "remove"):
raise ValueError("`visual_prompter_apply` must be in (add, replace, remove)")
super().__init__()
self.num_frames = config.num_frames
self.max_img_size = config.max_img_size
self.visual_prompter_apply = config.visual_prompter_apply
self.base_size = config.max_img_size - config.visual_prompt_size * 2
self.pad_up = nn.Parameter(
torch.randn([1, config.num_frames, 3, config.visual_prompt_size, config.max_img_size])
)
self.pad_down = nn.Parameter(
torch.randn([1, config.num_frames, 3, config.visual_prompt_size, config.max_img_size])
)
self.pad_left = nn.Parameter(
torch.randn(
[
1,
config.num_frames,
3,
config.max_img_size - config.visual_prompt_size * 2,
config.visual_prompt_size,
]
)
)
self.pad_right = nn.Parameter(
torch.randn(
[
1,
config.num_frames,
3,
config.max_img_size - config.visual_prompt_size * 2,
config.visual_prompt_size,
]
)
)
def forward(self, pixel_values):
if self.visual_prompter_apply not in ("add", "remove", "replace"):
raise ValueError(f"Invalid visual_prompter_apply value {self.visual_prompter_apply}")
if self.visual_prompter_apply in ("replace", "remove"):
visual_prompt_mask = torch.ones(
[self.max_img_size, self.max_img_size], dtype=pixel_values.dtype, device=pixel_values.device
)
pixel_values *= visual_prompt_mask
if self.visual_prompter_apply in ("replace", "add"):
base = torch.zeros(1, self.num_frames, 3, self.base_size, self.base_size, device=pixel_values.device)
prompt = torch.cat([self.pad_left, base, self.pad_right], dim=4)
prompt = torch.cat([self.pad_up, prompt, self.pad_down], dim=3)
prompt = torch.cat(pixel_values.size(0) * [prompt])
pixel_values += prompt.to(pixel_values.dtype)
return pixel_values
TVP_PROMPTER_CLASSES_MAPPING = {
"framedownpad": TvpFrameDownPadPrompter,
"framepad": TvpFramePadPrompter,
}
@add_start_docstrings(
"The bare Tvp Model transformer outputting BaseModelOutputWithPooling object without any specific head on" " top.",
TVP_START_DOCSTRING,
)
class TvpModel(TvpPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.vision_model = TvpVisionModel(config)
self.embeddings = TvpTextInputEmbeddings(config)
self.visual_embeddings = TvpVisualInputEmbedding(config)
self.encoder = TvpEncoder(config)
self.pooler = TvpPooler(config)
self.text_prompt = nn.Parameter(torch.randn([1, 10, config.hidden_size]))
self.dropout = nn.Dropout(config.hidden_dropout_prob)
if config.visual_prompter_type not in TVP_PROMPTER_CLASSES_MAPPING:
raise ValueError("`visual_prompter_type` must be in (framedownpad, framepad)")
self.visual_prompter = TVP_PROMPTER_CLASSES_MAPPING[config.visual_prompter_type](config)
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(TVP_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=TvpConfig)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Returns:
Examples:
```python
>>> import torch
>>> from transformers import AutoConfig, AutoTokenizer, TvpModel
>>> model = TvpModel.from_pretrained("Jiqing/tiny-random-tvp")
>>> tokenizer = AutoTokenizer.from_pretrained("Jiqing/tiny-random-tvp")
>>> pixel_values = torch.rand(1, 1, 3, 448, 448)
>>> text_inputs = tokenizer("This is an example input", return_tensors="pt")
>>> output = model(text_inputs.input_ids, pixel_values, text_inputs.attention_mask)
```"""
return_dict = return_dict if return_dict is not None else self.config.return_dict
# Add visual prompt, it compensates for the spatiotemporal information loss in 2D visual features.
pixel_values = self.vision_model(self.visual_prompter(pixel_values))
# (batch_size, sequence_length, hidden_size)
text_embedding_output = self.embeddings(input_ids=input_ids)
# (batch_size, visual_sequence_length, hidden_size)
visual_embedding_output = self.visual_embeddings(pixel_values)
if attention_mask is not None:
# (batch_size, visual_sequence_length)
visual_attention_mask = attention_mask.new_ones(visual_embedding_output.shape[:2])
pt_mask = torch.ones(attention_mask.shape[0], 10).to(
device=attention_mask.device, dtype=attention_mask.dtype
)
attention_mask = torch.cat([pt_mask, attention_mask, visual_attention_mask], dim=-1)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
attention_mask = self.get_extended_attention_mask(attention_mask, input_ids.size()).to(input_ids.device)
text_prompt = self.text_prompt.expand(text_embedding_output.shape[0], -1, -1)
# (batch_size, sequence_length + visual_sequence_length, hidden_size)
embedding_output = torch.cat([text_prompt, text_embedding_output, visual_embedding_output], dim=1)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=attention_mask,
head_mask=self.get_head_mask(head_mask, self.config.num_hidden_layers),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs.last_hidden_state if return_dict else encoder_outputs[0]
pooled_output = self.pooler(last_hidden_state)
last_hidden_state = self.dropout(last_hidden_state)
pooled_output = self.dropout(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class TvpVideoGroundingHead(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_0 = nn.Linear(config.hidden_size, config.hidden_size * 2)
self.layer_1 = nn.Linear(config.hidden_size * 2, 2)
self.activation_0 = nn.ReLU()
self.activation_1 = nn.Sigmoid()
def forward(self, pooler_output):
logits = self.activation_0(self.layer_0(pooler_output))
logits = self.activation_1(self.layer_1(logits))
return logits
@add_start_docstrings(
"""
Tvp Model with a video grounding head on top computing IoU, distance, and duration loss.
""",
TVP_START_DOCSTRING,
)
class TvpForVideoGrounding(TvpPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.model = TvpModel(config)
self.video_grounding_head = TvpVideoGroundingHead(config)
self.post_init()
@add_start_docstrings_to_model_forward(TVP_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TvpVideoGroundingOutput, config_class=TvpConfig)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
labels: Tuple[torch.Tensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
labels (`torch.FloatTensor` of shape `(batch_size, 3)`, *optional*):
The labels contains duration, start time, and end time of the video corresponding to the text.
Returns:
Examples:
```python
>>> import torch
>>> from transformers import AutoConfig, AutoTokenizer, TvpForVideoGrounding
>>> model = TvpForVideoGrounding.from_pretrained("Jiqing/tiny-random-tvp")
>>> tokenizer = AutoTokenizer.from_pretrained("Jiqing/tiny-random-tvp")
>>> pixel_values = torch.rand(1, 1, 3, 448, 448)
>>> text_inputs = tokenizer("This is an example input", return_tensors="pt")
>>> output = model(text_inputs.input_ids, pixel_values, text_inputs.attention_mask)
```"""
return_dict = return_dict if return_dict is not None else self.config.return_dict
outputs = self.model(
input_ids,
pixel_values,
attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooler_output = outputs[1]
logits = self.video_grounding_head(pooler_output)
loss = None
if labels is not None:
criterion = TvpLoss(["iou", "distance", "duration"])
criterion.to(self.device)
loss_dict = criterion(logits, labels)
loss = (
loss_dict["iou"]
+ self.config.distance_loss_weight * loss_dict["distance"]
+ self.config.duration_loss_weight * loss_dict["duration"]
)
if not return_dict:
outputs = (logits,) + outputs[2:]
if loss is not None:
outputs = (loss,) + outputs
return outputs
return TvpVideoGroundingOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/tvp/configuration_tvp.py | # coding=utf-8
# Copyright 2023 The Intel AIA Team Authors, and HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License=, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing=, software
# distributed under the License is distributed on an "AS IS" BASIS=,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND=, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TVP model configuration"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
logger = logging.get_logger(__name__)
TVP_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"Intel/tvp-base": "https://huggingface.co/Intel/tvp-base/resolve/main/config.json",
}
class TvpConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`TvpModel`]. It is used to instantiate an Tvp
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Tvp
[Intel/tvp-base](https://huggingface.co/Intel/tvp-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
backbone_config (`PretrainedConfig` or `dict`, *optional*):
The configuration of the backbone model.
distance_loss_weight (`float`, *optional*, defaults to 1.0):
The weight of distance loss.
duration_loss_weight (`float`, *optional*, defaults to 0.1):
The weight of duration loss.
visual_prompter_type (`str`, *optional*, defaults to `"framepad"`):
Visual prompt type. The type of padding. Framepad means padding on each frame. Should be one of "framepad"
or "framedownpad"
visual_prompter_apply (`str`, *optional*, defaults to `"replace"`):
The way of applying visual prompt. Replace means use the value of prompt to change the original value in
visual inputs. Should be one of "replace", or "add", or "remove".
visual_prompt_size (`int`, *optional*, defaults to 96):
The size of visual prompt.
max_img_size (`int`, *optional*, defaults to 448):
The maximum size of frame.
num_frames (`int`, *optional*, defaults to 48):
The number of frames extracted from a video.
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the Tvp text model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`TvpModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
max_grid_col_position_embeddings (`int`, *optional*, defaults to 100):
The largest number of horizontal patches from a video frame.
max_grid_row_position_embeddings (`int`, *optional*, defaults to 100):
The largest number of vertical patches from a video frame.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability of hidden layers.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability of attention layers.
"""
model_type = "tvp"
def __init__(
self,
backbone_config=None,
distance_loss_weight=1.0,
duration_loss_weight=0.1,
visual_prompter_type="framepad",
visual_prompter_apply="replace",
visual_prompt_size=96,
max_img_size=448,
num_frames=48,
vocab_size=30522,
hidden_size=768,
intermediate_size=3072,
num_hidden_layers=12,
num_attention_heads=12,
max_position_embeddings=512,
max_grid_col_position_embeddings=100,
max_grid_row_position_embeddings=100,
hidden_dropout_prob=0.1,
hidden_act="gelu",
layer_norm_eps=1e-12,
initializer_range=0.02,
attention_probs_dropout_prob=0.1,
**kwargs,
):
super().__init__(**kwargs)
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage4"])
elif isinstance(backbone_config, dict):
backbone_model_type = backbone_config.get("model_type")
config_class = CONFIG_MAPPING[backbone_model_type]
backbone_config = config_class.from_dict(backbone_config)
self.backbone_config = backbone_config
self.distance_loss_weight = distance_loss_weight
self.duration_loss_weight = duration_loss_weight
self.visual_prompter_type = visual_prompter_type
self.visual_prompter_apply = visual_prompter_apply
self.visual_prompt_size = visual_prompt_size
self.max_img_size = max_img_size
self.num_frames = num_frames
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.max_position_embeddings = max_position_embeddings
self.max_grid_col_position_embeddings = max_grid_col_position_embeddings
self.max_grid_row_position_embeddings = max_grid_row_position_embeddings
self.layer_norm_eps = layer_norm_eps
self.hidden_dropout_prob = hidden_dropout_prob
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.attention_probs_dropout_prob = attention_probs_dropout_prob
@classmethod
def from_backbone_config(cls, backbone_config: PretrainedConfig, **kwargs):
"""Instantiate a [`TvpConfig`] (or a derived class) from a pre-trained backbone model configuration.
Args:
backbone_config ([`PretrainedConfig`]):
The backbone configuration.
Returns:
[`TvpConfig`]: An instance of a configuration object
"""
return cls(backbone_config=backbone_config, **kwargs)
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = copy.deepcopy(self.__dict__)
if output["backbone_config"] is not None:
output["backbone_config"] = self.backbone_config.to_dict()
output["model_type"] = self.__class__.model_type
return output
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/ibert/modeling_ibert.py | # coding=utf-8
# Copyright 2021 The I-BERT Authors (Sehoon Kim, Amir Gholami, Zhewei Yao,
# Michael Mahoney, Kurt Keutzer - UC Berkeley) and The HuggingFace Inc. team.
# Copyright (c) 20121, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch I-BERT model."""
import math
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import gelu
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
MaskedLMOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_ibert import IBertConfig
from .quant_modules import IntGELU, IntLayerNorm, IntSoftmax, QuantAct, QuantEmbedding, QuantLinear
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "kssteven/ibert-roberta-base"
_CONFIG_FOR_DOC = "IBertConfig"
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"kssteven/ibert-roberta-base",
"kssteven/ibert-roberta-large",
"kssteven/ibert-roberta-large-mnli",
]
class IBertEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.embedding_bit = 8
self.embedding_act_bit = 16
self.act_bit = 8
self.ln_input_bit = 22
self.ln_output_bit = 32
self.word_embeddings = QuantEmbedding(
config.vocab_size,
config.hidden_size,
padding_idx=config.pad_token_id,
weight_bit=self.embedding_bit,
quant_mode=self.quant_mode,
)
self.token_type_embeddings = QuantEmbedding(
config.type_vocab_size, config.hidden_size, weight_bit=self.embedding_bit, quant_mode=self.quant_mode
)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
# End copy
self.padding_idx = config.pad_token_id
self.position_embeddings = QuantEmbedding(
config.max_position_embeddings,
config.hidden_size,
padding_idx=self.padding_idx,
weight_bit=self.embedding_bit,
quant_mode=self.quant_mode,
)
# Integer-only addition between embeddings
self.embeddings_act1 = QuantAct(self.embedding_act_bit, quant_mode=self.quant_mode)
self.embeddings_act2 = QuantAct(self.embedding_act_bit, quant_mode=self.quant_mode)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = IntLayerNorm(
config.hidden_size,
eps=config.layer_norm_eps,
output_bit=self.ln_output_bit,
quant_mode=self.quant_mode,
force_dequant=config.force_dequant,
)
self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(
self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
):
if position_ids is None:
if input_ids is not None:
# Create the position ids from the input token ids. Any padded tokens remain padded.
position_ids = create_position_ids_from_input_ids(
input_ids, self.padding_idx, past_key_values_length
).to(input_ids.device)
else:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds, inputs_embeds_scaling_factor = self.word_embeddings(input_ids)
else:
inputs_embeds_scaling_factor = None
token_type_embeddings, token_type_embeddings_scaling_factor = self.token_type_embeddings(token_type_ids)
embeddings, embeddings_scaling_factor = self.embeddings_act1(
inputs_embeds,
inputs_embeds_scaling_factor,
identity=token_type_embeddings,
identity_scaling_factor=token_type_embeddings_scaling_factor,
)
if self.position_embedding_type == "absolute":
position_embeddings, position_embeddings_scaling_factor = self.position_embeddings(position_ids)
embeddings, embeddings_scaling_factor = self.embeddings_act1(
embeddings,
embeddings_scaling_factor,
identity=position_embeddings,
identity_scaling_factor=position_embeddings_scaling_factor,
)
embeddings, embeddings_scaling_factor = self.LayerNorm(embeddings, embeddings_scaling_factor)
embeddings = self.dropout(embeddings)
embeddings, embeddings_scaling_factor = self.output_activation(embeddings, embeddings_scaling_factor)
return embeddings, embeddings_scaling_factor
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
"""
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
"""
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
)
return position_ids.unsqueeze(0).expand(input_shape)
class IBertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.quant_mode = config.quant_mode
self.weight_bit = 8
self.bias_bit = 32
self.act_bit = 8
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
# Q, K, V Linear layers
self.query = QuantLinear(
config.hidden_size,
self.all_head_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
self.key = QuantLinear(
config.hidden_size,
self.all_head_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
self.value = QuantLinear(
config.hidden_size,
self.all_head_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
# Requantization (32bit -> 8bit) for Q, K, V activations
self.query_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.key_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.value_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
if self.position_embedding_type != "absolute":
raise ValueError("I-BERT only supports 'absolute' for `config.position_embedding_type`")
self.softmax = IntSoftmax(self.act_bit, quant_mode=self.quant_mode, force_dequant=config.force_dequant)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
hidden_states_scaling_factor,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
# Projection
mixed_query_layer, mixed_query_layer_scaling_factor = self.query(hidden_states, hidden_states_scaling_factor)
mixed_key_layer, mixed_key_layer_scaling_factor = self.key(hidden_states, hidden_states_scaling_factor)
mixed_value_layer, mixed_value_layer_scaling_factor = self.value(hidden_states, hidden_states_scaling_factor)
# Requantization
query_layer, query_layer_scaling_factor = self.query_activation(
mixed_query_layer, mixed_query_layer_scaling_factor
)
key_layer, key_layer_scaling_factor = self.key_activation(mixed_key_layer, mixed_key_layer_scaling_factor)
value_layer, value_layer_scaling_factor = self.value_activation(
mixed_value_layer, mixed_value_layer_scaling_factor
)
# Transpose
query_layer = self.transpose_for_scores(query_layer)
key_layer = self.transpose_for_scores(key_layer)
value_layer = self.transpose_for_scores(value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
scale = math.sqrt(self.attention_head_size)
attention_scores = attention_scores / scale
if self.quant_mode:
attention_scores_scaling_factor = query_layer_scaling_factor * key_layer_scaling_factor / scale
else:
attention_scores_scaling_factor = None
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in IBertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs, attention_probs_scaling_factor = self.softmax(
attention_scores, attention_scores_scaling_factor
)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
if attention_probs_scaling_factor is not None:
context_layer_scaling_factor = attention_probs_scaling_factor * value_layer_scaling_factor
else:
context_layer_scaling_factor = None
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
# requantization: 32-bit -> 8-bit
context_layer, context_layer_scaling_factor = self.output_activation(
context_layer, context_layer_scaling_factor
)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
output_scaling_factor = (
(context_layer_scaling_factor, attention_probs_scaling_factor)
if output_attentions
else (context_layer_scaling_factor,)
)
return outputs, output_scaling_factor
class IBertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.weight_bit = 8
self.bias_bit = 32
self.ln_input_bit = 22
self.ln_output_bit = 32
self.dense = QuantLinear(
config.hidden_size,
config.hidden_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
self.ln_input_act = QuantAct(self.ln_input_bit, quant_mode=self.quant_mode)
self.LayerNorm = IntLayerNorm(
config.hidden_size,
eps=config.layer_norm_eps,
output_bit=self.ln_output_bit,
quant_mode=self.quant_mode,
force_dequant=config.force_dequant,
)
self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor):
hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor)
hidden_states = self.dropout(hidden_states)
hidden_states, hidden_states_scaling_factor = self.ln_input_act(
hidden_states,
hidden_states_scaling_factor,
identity=input_tensor,
identity_scaling_factor=input_tensor_scaling_factor,
)
hidden_states, hidden_states_scaling_factor = self.LayerNorm(hidden_states, hidden_states_scaling_factor)
hidden_states, hidden_states_scaling_factor = self.output_activation(
hidden_states, hidden_states_scaling_factor
)
return hidden_states, hidden_states_scaling_factor
class IBertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.self = IBertSelfAttention(config)
self.output = IBertSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states,
hidden_states_scaling_factor,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
self_outputs, self_outputs_scaling_factor = self.self(
hidden_states,
hidden_states_scaling_factor,
attention_mask,
head_mask,
output_attentions,
)
attention_output, attention_output_scaling_factor = self.output(
self_outputs[0], self_outputs_scaling_factor[0], hidden_states, hidden_states_scaling_factor
)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
outputs_scaling_factor = (attention_output_scaling_factor,) + self_outputs_scaling_factor[1:]
return outputs, outputs_scaling_factor
class IBertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.weight_bit = 8
self.bias_bit = 32
self.dense = QuantLinear(
config.hidden_size,
config.intermediate_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
if config.hidden_act != "gelu":
raise ValueError("I-BERT only supports 'gelu' for `config.hidden_act`")
self.intermediate_act_fn = IntGELU(quant_mode=self.quant_mode, force_dequant=config.force_dequant)
self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
def forward(self, hidden_states, hidden_states_scaling_factor):
hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor)
hidden_states, hidden_states_scaling_factor = self.intermediate_act_fn(
hidden_states, hidden_states_scaling_factor
)
# Requantization: 32bit -> 8-bit
hidden_states, hidden_states_scaling_factor = self.output_activation(
hidden_states, hidden_states_scaling_factor
)
return hidden_states, hidden_states_scaling_factor
class IBertOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.weight_bit = 8
self.bias_bit = 32
self.ln_input_bit = 22
self.ln_output_bit = 32
self.dense = QuantLinear(
config.intermediate_size,
config.hidden_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
self.ln_input_act = QuantAct(self.ln_input_bit, quant_mode=self.quant_mode)
self.LayerNorm = IntLayerNorm(
config.hidden_size,
eps=config.layer_norm_eps,
output_bit=self.ln_output_bit,
quant_mode=self.quant_mode,
force_dequant=config.force_dequant,
)
self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor):
hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor)
hidden_states = self.dropout(hidden_states)
hidden_states, hidden_states_scaling_factor = self.ln_input_act(
hidden_states,
hidden_states_scaling_factor,
identity=input_tensor,
identity_scaling_factor=input_tensor_scaling_factor,
)
hidden_states, hidden_states_scaling_factor = self.LayerNorm(hidden_states, hidden_states_scaling_factor)
hidden_states, hidden_states_scaling_factor = self.output_activation(
hidden_states, hidden_states_scaling_factor
)
return hidden_states, hidden_states_scaling_factor
class IBertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.seq_len_dim = 1
self.attention = IBertAttention(config)
self.intermediate = IBertIntermediate(config)
self.output = IBertOutput(config)
self.pre_intermediate_act = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.pre_output_act = QuantAct(self.act_bit, quant_mode=self.quant_mode)
def forward(
self,
hidden_states,
hidden_states_scaling_factor,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
self_attention_outputs, self_attention_outputs_scaling_factor = self.attention(
hidden_states,
hidden_states_scaling_factor,
attention_mask,
head_mask,
output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0]
attention_output_scaling_factor = self_attention_outputs_scaling_factor[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
layer_output, layer_output_scaling_factor = self.feed_forward_chunk(
attention_output, attention_output_scaling_factor
)
outputs = (layer_output,) + outputs
return outputs
def feed_forward_chunk(self, attention_output, attention_output_scaling_factor):
attention_output, attention_output_scaling_factor = self.pre_intermediate_act(
attention_output, attention_output_scaling_factor
)
intermediate_output, intermediate_output_scaling_factor = self.intermediate(
attention_output, attention_output_scaling_factor
)
intermediate_output, intermediate_output_scaling_factor = self.pre_output_act(
intermediate_output, intermediate_output_scaling_factor
)
layer_output, layer_output_scaling_factor = self.output(
intermediate_output, intermediate_output_scaling_factor, attention_output, attention_output_scaling_factor
)
return layer_output, layer_output_scaling_factor
class IBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.quant_mode = config.quant_mode
self.layer = nn.ModuleList([IBertLayer(config) for _ in range(config.num_hidden_layers)])
def forward(
self,
hidden_states,
hidden_states_scaling_factor,
attention_mask=None,
head_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = None # `config.add_cross_attention` is not supported
next_decoder_cache = None # `config.use_cache` is not supported
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
layer_outputs = layer_module(
hidden_states,
hidden_states_scaling_factor,
attention_mask,
layer_head_mask,
output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
class IBertPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
class IBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = IBertConfig
base_model_prefix = "ibert"
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (QuantLinear, nn.Linear)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, (QuantEmbedding, nn.Embedding)):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, (IntLayerNorm, nn.LayerNorm)):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def resize_token_embeddings(self, new_num_tokens=None):
raise NotImplementedError("`resize_token_embeddings` is not supported for I-BERT.")
IBERT_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`IBertConfig`]): Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
IBERT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare I-BERT Model transformer outputting raw hidden-states without any specific head on top.",
IBERT_START_DOCSTRING,
)
class IBertModel(IBertPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
"""
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.quant_mode = config.quant_mode
self.embeddings = IBertEmbeddings(config)
self.encoder = IBertEncoder(config)
self.pooler = IBertPooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[BaseModelOutputWithPoolingAndCrossAttentions, Tuple[torch.FloatTensor]]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length)), device=device)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output, embedding_output_scaling_factor = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
encoder_outputs = self.encoder(
embedding_output,
embedding_output_scaling_factor,
attention_mask=extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
@add_start_docstrings("""I-BERT Model with a `language modeling` head on top.""", IBERT_START_DOCSTRING)
class IBertForMaskedLM(IBertPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.bias", "lm_head.decoder.weight"]
def __init__(self, config):
super().__init__(config)
self.ibert = IBertModel(config, add_pooling_layer=False)
self.lm_head = IBertLMHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.lm_head.decoder
def set_output_embeddings(self, new_embeddings):
self.lm_head.decoder = new_embeddings
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
mask="<mask>",
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[MaskedLMOutput, Tuple[torch.FloatTensor]]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
kwargs (`Dict[str, any]`, optional, defaults to *{}*):
Used to hide legacy arguments that have been deprecated.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.ibert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class IBertLMHead(nn.Module):
"""I-BERT Head for masked language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
self.decoder.bias = self.bias
def forward(self, features, **kwargs):
x = self.dense(features)
x = gelu(x)
x = self.layer_norm(x)
# project back to size of vocabulary with bias
x = self.decoder(x)
return x
def _tie_weights(self):
# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
self.bias = self.decoder.bias
@add_start_docstrings(
"""
I-BERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
""",
IBERT_START_DOCSTRING,
)
class IBertForSequenceClassification(IBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ibert = IBertModel(config, add_pooling_layer=False)
self.classifier = IBertClassificationHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[SequenceClassifierOutput, Tuple[torch.FloatTensor]]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.ibert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
I-BERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
IBERT_START_DOCSTRING,
)
class IBertForMultipleChoice(IBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.ibert = IBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[MultipleChoiceModelOutput, Tuple[torch.FloatTensor]]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
flat_inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.ibert(
flat_input_ids,
position_ids=flat_position_ids,
token_type_ids=flat_token_type_ids,
attention_mask=flat_attention_mask,
head_mask=head_mask,
inputs_embeds=flat_inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
I-BERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
IBERT_START_DOCSTRING,
)
class IBertForTokenClassification(IBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ibert = IBertModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[TokenClassifierOutput, Tuple[torch.FloatTensor]]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.ibert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class IBertClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, features, **kwargs):
hidden_states = features[:, 0, :] # take <s> token (equiv. to [CLS])
hidden_states = self.dropout(hidden_states)
hidden_states = self.dense(hidden_states)
hidden_states = torch.tanh(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.out_proj(hidden_states)
return hidden_states
@add_start_docstrings(
"""
I-BERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
IBERT_START_DOCSTRING,
)
class IBertForQuestionAnswering(IBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ibert = IBertModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[QuestionAnsweringModelOutput, Tuple[torch.FloatTensor]]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.ibert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's *utils.make_positions*.
Args:
input_ids (`torch.LongTensor`):
Indices of input sequence tokens in the vocabulary.
Returns: torch.Tensor
"""
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
mask = input_ids.ne(padding_idx).int()
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
return incremental_indices.long() + padding_idx
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/ibert/quant_modules.py | # coding=utf-8
# Copyright 2021 The I-BERT Authors (Sehoon Kim, Amir Gholami, Zhewei Yao,
# Michael Mahoney, Kurt Keutzer - UC Berkeley) and The HuggingFace Inc. team.
# Copyright (c) 20121, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import decimal
import numpy as np
import torch
from torch import nn
from torch.autograd import Function
from ...utils import logging
logger = logging.get_logger(__name__)
class QuantEmbedding(nn.Module):
"""
Quantized version of `torch.nn.Embedding`. Adds quantization-specific arguments on top of `torch.nn.Embedding`.
Args:
weight_bit (`int`, *optional*, defaults to `8`):
Bitwidth for the quantized weight.
momentum (`float`, *optional*, defaults to `0.95`):
Momentum for updating the activation quantization range.
quant_mode (`bool`, *optional*, defaults to `False`):
Whether or not the layer is quantized.
"""
def __init__(
self,
num_embeddings,
embedding_dim,
padding_idx=None,
max_norm=None,
norm_type=2.0,
scale_grad_by_freq=False,
sparse=False,
_weight=None,
weight_bit=8,
momentum=0.95,
quant_mode=False,
):
super().__init__()
self.num_ = num_embeddings
self.dim = embedding_dim
self.padding_idx = padding_idx
self.max_norm = max_norm
self.norm_type = norm_type
self.scale_grad_by_freq = scale_grad_by_freq
self.sparse = sparse
self.weight = nn.Parameter(torch.zeros([num_embeddings, embedding_dim]))
self.register_buffer("weight_scaling_factor", torch.zeros(1))
self.register_buffer("weight_integer", torch.zeros_like(self.weight))
self.weight_bit = weight_bit
self.momentum = momentum
self.quant_mode = quant_mode
self.percentile_mode = False
self.weight_function = SymmetricQuantFunction.apply
def forward(self, x, positions=None, incremental_state=None):
if not self.quant_mode:
return (
nn.functional.embedding(
x,
self.weight,
self.padding_idx,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.sparse,
),
None,
)
w = self.weight
w_transform = w.data.detach()
w_min = w_transform.min().expand(1)
w_max = w_transform.max().expand(1)
self.weight_scaling_factor = symmetric_linear_quantization_params(self.weight_bit, w_min, w_max, False)
self.weight_integer = self.weight_function(
self.weight, self.weight_bit, self.percentile_mode, self.weight_scaling_factor
)
emb_int = nn.functional.embedding(
x,
self.weight_integer,
self.padding_idx,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.sparse,
)
return emb_int * self.weight_scaling_factor, self.weight_scaling_factor
class QuantAct(nn.Module):
"""
Quantizes the given activation.
Args:
activation_bit (`int`):
Bitwidth for the quantized activation.
act_range_momentum (`float`, *optional*, defaults to `0.95`):
Momentum for updating the activation quantization range.
per_channel (`bool`, *optional*, defaults to `False`):
Whether to or not use channel-wise quantization.
channel_len (`int`, *optional*):
Specify the channel length when set the *per_channel* True.
quant_mode (`bool`, *optional*, defaults to `False`):
Whether or not the layer is quantized.
"""
def __init__(self, activation_bit, act_range_momentum=0.95, per_channel=False, channel_len=None, quant_mode=False):
super().__init__()
self.activation_bit = activation_bit
self.act_range_momentum = act_range_momentum
self.quant_mode = quant_mode
self.per_channel = per_channel
self.percentile = False
self.act_function = SymmetricQuantFunction.apply
if not self.per_channel:
self.register_buffer("x_min", torch.zeros(1))
self.register_buffer("x_max", torch.zeros(1))
self.register_buffer("act_scaling_factor", torch.zeros(1))
self.x_min -= 1e-5
self.x_max += 1e-5
else:
raise NotImplementedError("per-channel mode is not currently supported for activation.")
def __repr__(self):
return (
f"{self.__class__.__name__}(activation_bit={self.activation_bit}, "
f"quant_mode: {self.quant_mode}, Act_min: {self.x_min.item():.2f}, "
f"Act_max: {self.x_max.item():.2f})"
)
def forward(
self,
x,
pre_act_scaling_factor=None,
identity=None,
identity_scaling_factor=None,
specified_min=None,
specified_max=None,
):
x_act = x if identity is None else identity + x
# collect running stats if training
if self.training:
assert not self.percentile, "percentile mode is not currently supported for activation."
assert not self.per_channel, "per-channel mode is not currently supported for activation."
x_min = x_act.data.min()
x_max = x_act.data.max()
assert (
x_max.isnan().sum() == 0 and x_min.isnan().sum() == 0
), "NaN detected when computing min/max of the activation"
# Initialization
if self.x_min.min() > -1.1e-5 and self.x_max.max() < 1.1e-5:
self.x_min = self.x_min + x_min
self.x_max = self.x_max + x_max
# exponential moving average (EMA)
# use momentum to prevent the quantized values change greatly every iteration
elif self.act_range_momentum == -1:
self.x_min = torch.min(self.x_min, x_min)
self.x_max = torch.max(self.x_max, x_max)
else:
self.x_min = self.x_min * self.act_range_momentum + x_min * (1 - self.act_range_momentum)
self.x_max = self.x_max * self.act_range_momentum + x_max * (1 - self.act_range_momentum)
if not self.quant_mode:
return x_act, None
x_min = self.x_min if specified_min is None else specified_min
x_max = self.x_max if specified_max is None else specified_max
self.act_scaling_factor = symmetric_linear_quantization_params(
self.activation_bit, x_min, x_max, per_channel=self.per_channel
)
if pre_act_scaling_factor is None:
# this is for the input quantization
quant_act_int = self.act_function(x, self.activation_bit, self.percentile, self.act_scaling_factor)
else:
quant_act_int = FixedPointMul.apply(
x,
pre_act_scaling_factor,
self.activation_bit,
self.act_scaling_factor,
identity,
identity_scaling_factor,
)
correct_output_scale = self.act_scaling_factor.view(-1)
return quant_act_int * correct_output_scale, self.act_scaling_factor
class QuantLinear(nn.Module):
"""
Quantized version of `torch.nn.Linear`. Adds quantization-specific arguments on top of `torch.nn.Linear`.
Args:
weight_bit (`int`, *optional*, defaults to `8`):
Bitwidth for the quantized weight.
bias_bit (`int`, *optional*, defaults to `32`):
Bitwidth for the quantized bias.
per_channel (`bool`, *optional*, defaults to `False`):
Whether or not to use channel-wise quantization.
quant_mode (`bool`, *optional*, defaults to `False`):
Whether or not the layer is quantized.
"""
def __init__(
self, in_features, out_features, bias=True, weight_bit=8, bias_bit=32, per_channel=False, quant_mode=False
):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = nn.Parameter(torch.zeros([out_features, in_features]))
self.register_buffer("weight_integer", torch.zeros_like(self.weight))
self.register_buffer("fc_scaling_factor", torch.zeros(self.out_features))
if bias:
self.bias = nn.Parameter(torch.zeros(out_features))
self.register_buffer("bias_integer", torch.zeros_like(self.bias))
self.weight_bit = weight_bit
self.quant_mode = quant_mode
self.per_channel = per_channel
self.bias_bit = bias_bit
self.quant_mode = quant_mode
self.percentile_mode = False
self.weight_function = SymmetricQuantFunction.apply
def __repr__(self):
s = super().__repr__()
s = f"({s} weight_bit={self.weight_bit}, quant_mode={self.quant_mode})"
return s
def forward(self, x, prev_act_scaling_factor=None):
if not self.quant_mode:
return nn.functional.linear(x, weight=self.weight, bias=self.bias), None
# assert that prev_act_scaling_factor is a scalar tensor
assert prev_act_scaling_factor is not None and prev_act_scaling_factor.shape == (1,), (
"Input activation to the QuantLinear layer should be globally (non-channel-wise) quantized. "
"Please add a QuantAct layer with `per_channel = True` before this QuantAct layer"
)
w = self.weight
w_transform = w.data.detach()
if self.per_channel:
w_min, _ = torch.min(w_transform, dim=1, out=None)
w_max, _ = torch.max(w_transform, dim=1, out=None)
else:
w_min = w_transform.min().expand(1)
w_max = w_transform.max().expand(1)
self.fc_scaling_factor = symmetric_linear_quantization_params(self.weight_bit, w_min, w_max, self.per_channel)
self.weight_integer = self.weight_function(
self.weight, self.weight_bit, self.percentile_mode, self.fc_scaling_factor
)
bias_scaling_factor = self.fc_scaling_factor * prev_act_scaling_factor
if self.bias is not None:
self.bias_integer = self.weight_function(self.bias, self.bias_bit, False, bias_scaling_factor)
prev_act_scaling_factor = prev_act_scaling_factor.view(1, -1)
x_int = x / prev_act_scaling_factor
return (
nn.functional.linear(x_int, weight=self.weight_integer, bias=self.bias_integer) * bias_scaling_factor,
bias_scaling_factor,
)
class IntGELU(nn.Module):
"""
Quantized version of `torch.nn.GELU`. Adds quantization-specific arguments on top of `torch.nn.GELU`.
Args:
quant_mode (`bool`, *optional*, defaults to `False`):
Whether or not the layer is quantized.
force_dequant (`str`, *optional*, defaults to `"none"`):
Force dequantize the layer if either "gelu" or "nonlinear" is given.
"""
def __init__(self, quant_mode=True, force_dequant="none"):
super().__init__()
self.quant_mode = quant_mode
if force_dequant in ["nonlinear", "gelu"]:
logger.info("Force dequantize gelu")
self.quant_mode = False
if not self.quant_mode:
self.activation_fn = nn.GELU()
self.k = 1.4142
self.const = 14 # dummy integer constant
self.coeff = [-0.2888, -1.769, 1] # a(x+b)**2 + c
self.coeff[2] /= self.coeff[0]
def int_erf(self, x_int, scaling_factor):
b_int = torch.floor(self.coeff[1] / scaling_factor)
c_int = torch.floor(self.coeff[2] / scaling_factor**2)
sign = torch.sign(x_int)
abs_int = torch.min(torch.abs(x_int), -b_int)
y_int = sign * ((abs_int + b_int) ** 2 + c_int)
scaling_factor = scaling_factor**2 * self.coeff[0]
# avoid overflow
y_int = floor_ste.apply(y_int / 2**self.const)
scaling_factor = scaling_factor * 2**self.const
return y_int, scaling_factor
def forward(self, x, scaling_factor=None):
if not self.quant_mode:
return self.activation_fn(x), None
x_int = x / scaling_factor
sigmoid_int, sigmoid_scaling_factor = self.int_erf(x_int, scaling_factor / self.k)
shift_int = 1.0 // sigmoid_scaling_factor
x_int = x_int * (sigmoid_int + shift_int)
scaling_factor = scaling_factor * sigmoid_scaling_factor / 2
return x_int * scaling_factor, scaling_factor
class IntSoftmax(nn.Module):
"""
Quantized version of `torch.nn.Softmax`. Adds quantization-specific arguments on top of `torch.nn.Softmax`.
Args:
output_bit (`int`):
Bitwidth for the layer output activation.
quant_mode (`bool`, *optional*, defaults to `False`):
Whether or not the layer is quantized.
force_dequant (`str`, *optional*, defaults to `"none"`):
Force dequantize the layer if either "softmax" or "nonlinear" is given.
"""
def __init__(self, output_bit, quant_mode=False, force_dequant="none"):
super().__init__()
self.output_bit = output_bit
self.max_bit = 32
self.quant_mode = quant_mode
if force_dequant in ["nonlinear", "softmax"]:
logger.info("Force dequantize softmax")
self.quant_mode = False
self.act = QuantAct(16, quant_mode=self.quant_mode)
self.x0 = -0.6931 # -ln2
self.const = 30 # dummy integer constant
self.coef = [0.35815147, 0.96963238, 1.0] # ax**2 + bx + c
self.coef[1] /= self.coef[0]
self.coef[2] /= self.coef[0]
def int_polynomial(self, x_int, scaling_factor):
with torch.no_grad():
b_int = torch.floor(self.coef[1] / scaling_factor)
c_int = torch.floor(self.coef[2] / scaling_factor**2)
z = (x_int + b_int) * x_int + c_int
scaling_factor = self.coef[0] * scaling_factor**2
return z, scaling_factor
def int_exp(self, x_int, scaling_factor):
with torch.no_grad():
x0_int = torch.floor(self.x0 / scaling_factor)
x_int = torch.max(x_int, self.const * x0_int)
q = floor_ste.apply(x_int / x0_int)
r = x_int - x0_int * q
exp_int, exp_scaling_factor = self.int_polynomial(r, scaling_factor)
exp_int = torch.clamp(floor_ste.apply(exp_int * 2 ** (self.const - q)), min=0)
scaling_factor = exp_scaling_factor / 2**self.const
return exp_int, scaling_factor
def forward(self, x, scaling_factor):
if not self.quant_mode:
return nn.functional.softmax(x, dim=-1), None
x_int = x / scaling_factor
x_int_max, _ = x_int.max(dim=-1, keepdim=True)
x_int = x_int - x_int_max
exp_int, exp_scaling_factor = self.int_exp(x_int, scaling_factor)
# Avoid overflow
exp, exp_scaling_factor = self.act(exp_int, exp_scaling_factor)
exp_int = exp / exp_scaling_factor
exp_int_sum = exp_int.sum(dim=-1, keepdim=True)
factor = floor_ste.apply(2**self.max_bit / exp_int_sum)
exp_int = floor_ste.apply(exp_int * factor / 2 ** (self.max_bit - self.output_bit))
scaling_factor = 1 / 2**self.output_bit
return exp_int * scaling_factor, scaling_factor
class IntLayerNorm(nn.Module):
"""
Quantized version of `torch.nn.LayerNorm`. Adds quantization-specific arguments on top of `torch.nn.LayerNorm`.
Args:
output_bit (`int`, *optional*, defaults to `8`):
Bitwidth for the layer output activation.
quant_mode (`bool`, *optional*, defaults to `False`):
Whether or not the layer is quantized.
force_dequant (`str`, *optional*, defaults to `"none"`):
Force dequantize the layer if either "layernorm" or "nonlinear" is given.
"""
def __init__(self, normalized_shape, eps, output_bit=8, quant_mode=False, force_dequant="none"):
super().__init__()
self.normalized_shape = normalized_shape
self.eps = eps
self.weight = nn.Parameter(torch.zeros(normalized_shape))
self.bias = nn.Parameter(torch.zeros(normalized_shape))
self.quant_mode = quant_mode
if force_dequant in ["nonlinear", "layernorm"]:
logger.info("Force dequantize layernorm")
self.quant_mode = False
self.register_buffer("shift", torch.zeros(1))
self.output_bit = output_bit
self.max_bit = 32
self.dim_sqrt = None
self.activation = QuantAct(self.output_bit, quant_mode=self.quant_mode)
def set_shift(self, y_int):
with torch.no_grad():
y_sq_int = y_int**2
var_int = torch.sum(y_sq_int, axis=2, keepdim=True)
shift = (torch.log2(torch.sqrt(var_int / 2**self.max_bit)).ceil()).max()
shift_old = self.shift
self.shift = torch.max(self.shift, shift)
logger.info(f"Dynamic shift adjustment: {int(shift_old)} -> {int(self.shift)}")
def overflow_fallback(self, y_int):
"""
This fallback function is called when overflow is detected during training time, and adjusts the `self.shift`
to avoid overflow in the subsequent runs.
"""
self.set_shift(y_int) # adjusts `self.shift`
y_int_shifted = floor_ste.apply(y_int / 2**self.shift)
y_sq_int = y_int_shifted**2
var_int = torch.sum(y_sq_int, axis=2, keepdim=True)
return var_int
def forward(self, x, scaling_factor=None):
if not self.quant_mode:
mean = x.mean(axis=2, keepdim=True)
y = x - mean
var = torch.mean(y**2, axis=2, keepdim=True)
x = y / torch.sqrt(self.eps + var)
x = x * self.weight + self.bias
return x, None
# compute sqrt of the feature dimension if it is the first run
if self.dim_sqrt is None:
n = torch.tensor(x.shape[2], dtype=torch.float)
self.dim_sqrt = torch.sqrt(n).to(x.device)
# Normalization: computes mean and variance(std)
x_int = x / scaling_factor
mean_int = round_ste.apply(x_int.mean(axis=2, keepdim=True))
y_int = x_int - mean_int
y_int_shifted = floor_ste.apply(y_int / 2**self.shift)
y_sq_int = y_int_shifted**2
var_int = torch.sum(y_sq_int, axis=2, keepdim=True)
# overflow handling in training time
if self.training:
# if overflow is detected
if var_int.max() >= 2**self.max_bit:
var_int = self.overflow_fallback(y_int)
assert var_int.max() < 2**self.max_bit + 0.1, (
"Error detected in overflow handling: "
"`var_int` exceeds `self.max_bit` (the maximum possible bit width)"
)
# To be replaced with integer-sqrt kernel that produces the same output
std_int = floor_ste.apply(torch.sqrt(var_int)) * 2**self.shift
factor = floor_ste.apply(2**31 / std_int)
y_int = floor_ste.apply(y_int * factor / 2)
scaling_factor = self.dim_sqrt / 2**30
# scaling and shifting
bias = self.bias.data.detach() / (self.weight.data.detach())
bias_int = floor_ste.apply(bias / scaling_factor)
y_int = y_int + bias_int
scaling_factor = scaling_factor * self.weight
x = y_int * scaling_factor
return x, scaling_factor
def get_percentile_min_max(input, lower_percentile, upper_percentile, output_tensor=False):
"""
Calculate the percentile max and min values in a given tensor
Args:
input (`torch.Tensor`):
The target tensor to calculate percentile max and min.
lower_percentile (`float`):
If 0.1, means we return the value of the smallest 0.1% value in the tensor as percentile min.
upper_percentile (`float`):
If 99.9, means we return the value of the largest 0.1% value in the tensor as percentile max.
output_tensor (`bool`, *optional*, defaults to `False`):
If True, this function returns tensors, otherwise it returns values.
Returns:
`Tuple(torch.Tensor, torch.Tensor)`: Percentile min and max value of *input*
"""
input_length = input.shape[0]
lower_index = round(input_length * (1 - lower_percentile * 0.01))
upper_index = round(input_length * upper_percentile * 0.01)
upper_bound = torch.kthvalue(input, k=upper_index).values
if lower_percentile == 0:
lower_bound = upper_bound * 0
# lower_index += 1
else:
lower_bound = -torch.kthvalue(-input, k=lower_index).values
if not output_tensor:
lower_bound = lower_bound.item()
upper_bound = upper_bound.item()
return lower_bound, upper_bound
def linear_quantize(input, scale, zero_point, inplace=False):
"""
Quantize single-precision input tensor to integers with the given scaling factor and zeropoint.
Args:
input (`torch.Tensor`):
Single-precision input tensor to be quantized.
scale (`torch.Tensor`):
Scaling factor for quantization.
zero_pint (`torch.Tensor`):
Shift for quantization.
inplace (`bool`, *optional*, defaults to `False`):
Whether to compute inplace or not.
Returns:
`torch.Tensor`: Linearly quantized value of *input* according to *scale* and *zero_point*.
"""
# reshape scale and zeropoint for convolutional weights and activation
if len(input.shape) == 4:
scale = scale.view(-1, 1, 1, 1)
zero_point = zero_point.view(-1, 1, 1, 1)
# reshape scale and zeropoint for linear weights
elif len(input.shape) == 2:
scale = scale.view(-1, 1)
zero_point = zero_point.view(-1, 1)
else:
scale = scale.view(-1)
zero_point = zero_point.view(-1)
# quantized = float / scale + zero_point
if inplace:
input.mul_(1.0 / scale).add_(zero_point).round_()
return input
return torch.round(1.0 / scale * input + zero_point)
def symmetric_linear_quantization_params(num_bits, saturation_min, saturation_max, per_channel=False):
"""
Compute the scaling factor with the given quantization range for symmetric quantization.
Args:
saturation_min (`torch.Tensor`):
Lower bound for quantization range.
saturation_max (`torch.Tensor`):
Upper bound for quantization range.
per_channel (`bool`, *optional*, defaults to `False`):
Whether to or not use channel-wise quantization.
Returns:
`torch.Tensor`: Scaling factor that linearly quantizes the given range between *saturation_min* and
*saturation_max*.
"""
# in this part, we do not need any gradient computation,
# in order to enforce this, we put torch.no_grad()
with torch.no_grad():
n = 2 ** (num_bits - 1) - 1
if per_channel:
scale, _ = torch.max(torch.stack([saturation_min.abs(), saturation_max.abs()], dim=1), dim=1)
scale = torch.clamp(scale, min=1e-8) / n
else:
scale = max(saturation_min.abs(), saturation_max.abs())
scale = torch.clamp(scale, min=1e-8) / n
return scale
class SymmetricQuantFunction(Function):
"""
Class to quantize the given floating-point values using symmetric quantization with given range and bitwidth.
"""
@staticmethod
def forward(ctx, x, k, percentile_mode, scale):
"""
Args:
x (`torch.Tensor`):
Floating point tensor to be quantized.
k (`int`):
Quantization bitwidth.
percentile_mode (`bool`):
Whether or not to use percentile calibration.
scale (`torch.Tensor`):
Pre-calculated scaling factor for *x*. Note that the current implementation of SymmetricQuantFunction
requires pre-calculated scaling factor.
Returns:
`torch.Tensor`: Symmetric-quantized value of *input*.
"""
zero_point = torch.tensor(0.0).to(scale.device)
n = 2 ** (k - 1) - 1
new_quant_x = linear_quantize(x, scale, zero_point, inplace=False)
new_quant_x = torch.clamp(new_quant_x, -n, n - 1)
ctx.scale = scale
return new_quant_x
@staticmethod
def backward(ctx, grad_output):
scale = ctx.scale
if len(grad_output.shape) == 4:
scale = scale.view(-1, 1, 1, 1)
# reshape scale and zeropoint for linear weights
elif len(grad_output.shape) == 2:
scale = scale.view(-1, 1)
else:
scale = scale.view(-1)
return grad_output.clone() / scale, None, None, None, None
class floor_ste(Function):
"""
Straight-through Estimator(STE) for torch.floor()
"""
@staticmethod
def forward(ctx, x):
return torch.floor(x)
@staticmethod
def backward(ctx, grad_output):
return grad_output.clone()
class round_ste(Function):
"""
Straight-through Estimator(STE) for torch.round()
"""
@staticmethod
def forward(ctx, x):
return torch.round(x)
@staticmethod
def backward(ctx, grad_output):
return grad_output.clone()
def batch_frexp(inputs, max_bit=31):
"""
Decompose the scaling factor into mantissa and twos exponent.
Args:
scaling_factor (`torch.Tensor`):
Target scaling factor to decompose.
Returns:
``Tuple(torch.Tensor, torch.Tensor)`: mantisa and exponent
"""
shape_of_input = inputs.size()
# trans the input to be a 1-d tensor
inputs = inputs.view(-1)
output_m, output_e = np.frexp(inputs.cpu().numpy())
tmp_m = []
for m in output_m:
int_m_shifted = int(
decimal.Decimal(m * (2**max_bit)).quantize(decimal.Decimal("1"), rounding=decimal.ROUND_HALF_UP)
)
tmp_m.append(int_m_shifted)
output_m = np.array(tmp_m)
output_e = float(max_bit) - output_e
return (
torch.from_numpy(output_m).to(inputs.device).view(shape_of_input),
torch.from_numpy(output_e).to(inputs.device).view(shape_of_input),
)
class FixedPointMul(Function):
"""
Function to perform fixed-point arithmetic that can match integer arithmetic on hardware.
Args:
pre_act (`torch.Tensor`):
Input tensor.
pre_act_scaling_factor (`torch.Tensor`):
Scaling factor of the input tensor *pre_act*.
bit_num (`int`):
Quantization bitwidth.
z_scaling_factor (`torch.Tensor`):
Scaling factor of the output tensor.
identity (`torch.Tensor`, *optional*):
Identity tensor, if exists.
identity_scaling_factor (`torch.Tensor`, *optional*):
Scaling factor of the identity tensor *identity*, if exists.
Returns:
`torch.Tensor`: Output tensor(*pre_act* if *identity* is not given, otherwise the addition of *pre_act* and
*identity*), whose scale is rescaled to *z_scaling_factor*.
"""
@staticmethod
def forward(
ctx,
pre_act,
pre_act_scaling_factor,
bit_num,
z_scaling_factor,
identity=None,
identity_scaling_factor=None,
):
if len(pre_act_scaling_factor.shape) == 3:
reshape = lambda x: x # noqa: E731
else:
reshape = lambda x: x.view(1, 1, -1) # noqa: E731
ctx.identity = identity
n = 2 ** (bit_num - 1) - 1
with torch.no_grad():
pre_act_scaling_factor = reshape(pre_act_scaling_factor)
if identity is not None:
identity_scaling_factor = reshape(identity_scaling_factor)
ctx.z_scaling_factor = z_scaling_factor
z_int = torch.round(pre_act / pre_act_scaling_factor)
_A = pre_act_scaling_factor.type(torch.double)
_B = (z_scaling_factor.type(torch.float)).type(torch.double)
new_scale = _A / _B
new_scale = reshape(new_scale)
m, e = batch_frexp(new_scale)
output = z_int.type(torch.double) * m.type(torch.double)
output = torch.round(output / (2.0**e))
if identity is not None:
# needs addition of identity activation
wx_int = torch.round(identity / identity_scaling_factor)
_A = identity_scaling_factor.type(torch.double)
_B = (z_scaling_factor.type(torch.float)).type(torch.double)
new_scale = _A / _B
new_scale = reshape(new_scale)
m1, e1 = batch_frexp(new_scale)
output1 = wx_int.type(torch.double) * m1.type(torch.double)
output1 = torch.round(output1 / (2.0**e1))
output = output1 + output
return torch.clamp(output.type(torch.float), -n - 1, n)
@staticmethod
def backward(ctx, grad_output):
identity_grad = None
if ctx.identity is not None:
identity_grad = grad_output.clone() / ctx.z_scaling_factor
return grad_output.clone() / ctx.z_scaling_factor, None, None, None, None, identity_grad, None
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/ibert/__init__.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_ibert"] = [
"IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"IBertForMaskedLM",
"IBertForMultipleChoice",
"IBertForQuestionAnswering",
"IBertForSequenceClassification",
"IBertForTokenClassification",
"IBertModel",
"IBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/ibert/configuration_ibert.py | # coding=utf-8
# Copyright 2021 The I-BERT Authors (Sehoon Kim, Amir Gholami, Zhewei Yao,
# Michael Mahoney, Kurt Keutzer - UC Berkeley) and The HuggingFace Inc. team.
# Copyright (c) 20121, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" I-BERT configuration"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
"kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
"kssteven/ibert-roberta-large-mnli": (
"https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
),
}
class IBertConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`IBertModel`]. It is used to instantiate a I-BERT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the IBERT
[kssteven/ibert-roberta-base](https://huggingface.co/kssteven/ibert-roberta-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the I-BERT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`IBertModel`]
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`IBertModel`]
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
quant_mode (`bool`, *optional*, defaults to `False`):
Whether to quantize the model or not.
force_dequant (`str`, *optional*, defaults to `"none"`):
Force dequantize specific nonlinear layer. Dequatized layers are then executed with full precision.
`"none"`, `"gelu"`, `"softmax"`, `"layernorm"` and `"nonlinear"` are supported. As deafult, it is set as
`"none"`, which does not dequantize any layers. Please specify `"gelu"`, `"softmax"`, or `"layernorm"` to
dequantize GELU, Softmax, or LayerNorm, respectively. `"nonlinear"` will dequantize all nonlinear layers,
i.e., GELU, Softmax, and LayerNorm.
"""
model_type = "ibert"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
position_embedding_type="absolute",
quant_mode=False,
force_dequant="none",
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.quant_mode = quant_mode
self.force_dequant = force_dequant
class IBertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
]
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/codegen/modeling_codegen.py | # coding=utf-8
# Copyright 2022 Salesforce authors, The EleutherAI, and HuggingFace Teams. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch CodeGen model."""
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_codegen import CodeGenConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "Salesforce/codegen-2B-mono"
_CONFIG_FOR_DOC = "CodeGenConfig"
CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST = [
"Salesforce/codegen-350M-nl",
"Salesforce/codegen-350M-multi",
"Salesforce/codegen-350M-mono",
"Salesforce/codegen-2B-nl",
"Salesforce/codegen-2B-multi",
"Salesforce/codegen-2B-mono",
"Salesforce/codegen-6B-nl",
"Salesforce/codegen-6B-multi",
"Salesforce/codegen-6B-mono",
"Salesforce/codegen-16B-nl",
"Salesforce/codegen-16B-multi",
"Salesforce/codegen-16B-mono",
# See all CodeGen models at https://huggingface.co/models?filter=codegen
]
# Copied from transformers.models.gptj.modeling_gptj.create_sinusoidal_positions
def create_sinusoidal_positions(num_pos: int, dim: int) -> torch.Tensor:
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2) / dim))
sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(num_pos, dtype=torch.float), inv_freq).float()
return torch.cat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1)
# Copied from transformers.models.gptj.modeling_gptj.rotate_every_two
def rotate_every_two(x: torch.Tensor) -> torch.Tensor:
x1 = x[:, :, :, ::2]
x2 = x[:, :, :, 1::2]
x = torch.stack((-x2, x1), dim=-1)
return x.flatten(-2) # in einsum notation: rearrange(x, '... d j -> ... (d j)')
# Copied from transformers.models.gptj.modeling_gptj.apply_rotary_pos_emb
def apply_rotary_pos_emb(tensor: torch.Tensor, sin: torch.Tensor, cos: torch.Tensor) -> torch.Tensor:
sin = torch.repeat_interleave(sin[:, :, None, :], 2, 3)
cos = torch.repeat_interleave(cos[:, :, None, :], 2, 3)
return (tensor * cos) + (rotate_every_two(tensor) * sin)
class CodeGenAttention(nn.Module):
def __init__(self, config):
super().__init__()
max_positions = config.max_position_embeddings
self.register_buffer(
"causal_mask",
torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
1, 1, max_positions, max_positions
),
persistent=False,
)
self.attn_dropout = nn.Dropout(config.attn_pdrop)
self.resid_dropout = nn.Dropout(config.resid_pdrop)
self.embed_dim = config.hidden_size
self.num_attention_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_attention_heads
if self.head_dim * self.num_attention_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and"
f" `num_attention_heads`: {self.num_attention_heads})."
)
self.scale_attn = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype())
self.qkv_proj = nn.Linear(self.embed_dim, self.embed_dim * 3, bias=False)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
self.rotary_dim = config.rotary_dim
pos_embd_dim = self.rotary_dim or self.embed_dim
self.embed_positions = create_sinusoidal_positions(max_positions, pos_embd_dim)
def _split_heads(self, x, n_head, dim_head, mp_num):
reshaped = x.reshape(x.shape[:-1] + (n_head // mp_num, dim_head))
reshaped = reshaped.reshape(x.shape[:-2] + (-1,) + reshaped.shape[-1:])
return reshaped
def _merge_heads(self, tensor, num_attention_heads, attn_head_size):
"""
Merges attn_head_size dim and num_attn_heads dim into n_ctx
"""
if len(tensor.shape) == 5:
tensor = tensor.permute(0, 1, 3, 2, 4).contiguous()
elif len(tensor.shape) == 4:
tensor = tensor.permute(0, 2, 1, 3).contiguous()
else:
raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}")
new_shape = tensor.size()[:-2] + (num_attention_heads * attn_head_size,)
return tensor.view(new_shape)
def _attn(
self,
query,
key,
value,
attention_mask=None,
head_mask=None,
):
# compute causal mask from causal mask buffer
query_length, key_length = query.size(-2), key.size(-2)
causal_mask = self.causal_mask[:, :, key_length - query_length : key_length, :key_length]
# Keep the attention weights computation in fp32 to avoid overflow issues
query = query.to(torch.float32)
key = key.to(torch.float32)
attn_weights = torch.matmul(query, key.transpose(-1, -2))
attn_weights = attn_weights / self.scale_attn
mask_value = torch.finfo(attn_weights.dtype).min
# Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
# Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
attn_weights = torch.where(causal_mask, attn_weights, mask_value)
if attention_mask is not None:
# Apply the attention mask
attn_weights = attn_weights + attention_mask
attn_weights = nn.Softmax(dim=-1)(attn_weights)
attn_weights = attn_weights.to(value.dtype)
attn_weights = self.attn_dropout(attn_weights)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
def forward(
self,
hidden_states: Optional[torch.FloatTensor],
layer_past: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
) -> Union[
Tuple[torch.Tensor, Tuple[torch.Tensor]],
Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]],
]:
qkv = self.qkv_proj(hidden_states)
# TODO(enijkamp): factor out number of logical TPU-v4 cores or make forward pass agnostic
mp_num = 4
qkv_split = qkv.reshape(qkv.shape[:-1] + (mp_num, -1))
local_dim = self.head_dim * self.num_attention_heads // mp_num
query, value, key = torch.split(qkv_split, local_dim, dim=-1)
query = self._split_heads(query, self.num_attention_heads, self.head_dim, mp_num=mp_num)
key = self._split_heads(key, self.num_attention_heads, self.head_dim, mp_num=mp_num)
value = self._split_heads(value, self.num_attention_heads, self.head_dim, mp_num=mp_num)
value = value.permute(0, 2, 1, 3)
embed_positions = self.embed_positions
if embed_positions.device != position_ids.device:
embed_positions = embed_positions.to(position_ids.device)
self.embed_positions = embed_positions
sincos = embed_positions[position_ids]
sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1)
if self.rotary_dim is not None:
k_rot = key[:, :, :, : self.rotary_dim]
k_pass = key[:, :, :, self.rotary_dim :]
q_rot = query[:, :, :, : self.rotary_dim]
q_pass = query[:, :, :, self.rotary_dim :]
k_rot = apply_rotary_pos_emb(k_rot, sin, cos)
q_rot = apply_rotary_pos_emb(q_rot, sin, cos)
key = torch.cat([k_rot, k_pass], dim=-1)
query = torch.cat([q_rot, q_pass], dim=-1)
else:
key = apply_rotary_pos_emb(key, sin, cos)
query = apply_rotary_pos_emb(query, sin, cos)
key = key.permute(0, 2, 1, 3)
query = query.permute(0, 2, 1, 3)
if layer_past is not None:
past_key = layer_past[0]
past_value = layer_past[1]
key = torch.cat((past_key, key), dim=-2)
value = torch.cat((past_value, value), dim=-2)
if use_cache is True:
# Note that this cast is quite ugly, but is not implemented before ROPE as k_rot in the original codebase is always in fp32.
# Reference: https://github.com/salesforce/CodeGen/blob/f210c3bb1216c975ad858cd4132c0fdeabf4bfc2/codegen1/jaxformer/hf/codegen/modeling_codegen.py#L38
present = (key.to(hidden_states.dtype), value)
else:
present = None
# compute self-attention: V x Softmax(QK^T)
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim)
attn_output = self.out_proj(attn_output)
attn_output = self.resid_dropout(attn_output)
outputs = (attn_output, present)
if output_attentions:
outputs += (attn_weights,)
return outputs # a, present, (attentions)
# Copied from transformers.models.gptj.modeling_gptj.GPTJMLP with GPTJ->CodeGen
class CodeGenMLP(nn.Module):
def __init__(self, intermediate_size, config): # in MLP: intermediate_size= 4 * embed_dim
super().__init__()
embed_dim = config.n_embd
self.fc_in = nn.Linear(embed_dim, intermediate_size)
self.fc_out = nn.Linear(intermediate_size, embed_dim)
self.act = ACT2FN[config.activation_function]
self.dropout = nn.Dropout(config.resid_pdrop)
def forward(self, hidden_states: Optional[torch.FloatTensor]) -> torch.FloatTensor:
hidden_states = self.fc_in(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.fc_out(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
# Copied from transformers.models.gptj.modeling_gptj.GPTJBlock with GPTJ->CodeGen
class CodeGenBlock(nn.Module):
def __init__(self, config):
super().__init__()
inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.attn = CodeGenAttention(config)
self.mlp = CodeGenMLP(inner_dim, config)
def forward(
self,
hidden_states: Optional[torch.FloatTensor],
layer_past: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_outputs = self.attn(
hidden_states=hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
outputs = attn_outputs[1:]
feed_forward_hidden_states = self.mlp(hidden_states)
hidden_states = attn_output + feed_forward_hidden_states + residual
if use_cache:
outputs = (hidden_states,) + outputs
else:
outputs = (hidden_states,) + outputs[1:]
return outputs # hidden_states, present, (attentions)
class CodeGenPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = CodeGenConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = True
_no_split_modules = ["CodeGenBlock"]
_skip_keys_device_placement = "past_key_values"
def __init__(self, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, (nn.Linear,)):
# Slightly different from Mesh Transformer JAX which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
CODEGEN_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`CodeGenConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
CODEGEN_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoProcenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_attention_heads,)` or `(n_layer, num_attention_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_dim)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare CodeGen Model transformer outputting raw hidden-states without any specific head on top.",
CODEGEN_START_DOCSTRING,
)
class CodeGenModel(CodeGenPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embed_dim = config.n_embd
self.vocab_size = config.vocab_size
self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
self.drop = nn.Dropout(config.embd_pdrop)
self.h = nn.ModuleList([CodeGenBlock(config) for _ in range(config.n_layer)])
self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
self.rotary_dim = min(config.rotary_dim, config.n_ctx // config.num_attention_heads)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.wte
def set_input_embeddings(self, new_embeddings):
self.wte = new_embeddings
@add_start_docstrings_to_model_forward(CODEGEN_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size = inputs_embeds.shape[0]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * len(self.h))
else:
past_length = past_key_values[0][0].size(-2)
if position_ids is None:
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0)
# Attention mask.
if attention_mask is not None:
if batch_size <= 0:
raise ValueError("batch_size has to be defined and > 0")
attention_mask = attention_mask.view(batch_size, -1)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask = attention_mask[:, None, None, :]
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and the dtype's smallest value for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x num_attention_heads x N x N
# head_mask has shape n_layer x batch x num_attention_heads x N x N
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
hidden_states = inputs_embeds
if token_type_ids is not None:
token_type_embeds = self.wte(token_type_ids)
hidden_states = hidden_states + token_type_embeds
hidden_states = self.drop(hidden_states)
output_shape = input_shape + (hidden_states.size(-1),)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
"`use_cache=False`..."
)
use_cache = False
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
outputs = self._gradient_checkpointing_func(
block.__call__,
hidden_states,
None,
attention_mask,
position_ids,
head_mask[i],
use_cache,
output_attentions,
)
else:
outputs = block(
hidden_states=hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask[i],
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
@add_start_docstrings(
"""
The CodeGen Model transformer with a language modeling head on top.
""",
CODEGEN_START_DOCSTRING,
)
class CodeGenForCausalLM(CodeGenPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.transformer = CodeGenModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
token_type_ids = kwargs.get("token_type_ids", None)
# Omit tokens covered by past_key_values
if past_key_values:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_length:]
if token_type_ids is not None:
token_type_ids = token_type_ids[:, -input_ids.shape[1] :]
attention_mask = kwargs.get("attention_mask", None)
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_ids[:, -input_ids.shape[1] :]
return {
"input_ids": input_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"position_ids": position_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
@add_start_docstrings_to_model_forward(CODEGEN_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
# make sure sampling in fp16 works correctly and
# compute loss in fp32 to match with mesh-tf version
# https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179
lm_logits = self.lm_head(hidden_states).to(torch.float32)
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(lm_logits.device)
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
loss = loss.to(hidden_states.dtype)
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@staticmethod
def _reorder_cache(
past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
) -> Tuple[Tuple[torch.Tensor]]:
"""
This function is used to re-order the `past_key_values` cache if [`~PretrainedModel.beam_search`] or
[`~PretrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
beam_idx at every generation step.
"""
return tuple(
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
for layer_past in past_key_values
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/codegen/configuration_codegen.py | # coding=utf-8
# Copyright 2022 Salesforce authors, The EleutherAI, and HuggingFace Teams. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" CodeGen model configuration"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
logger = logging.get_logger(__name__)
CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
"Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
"Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
"Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
"Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
"Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
"Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
"Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
"Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
"Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
"Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}
class CodeGenConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`CodeGenModel`]. It is used to instantiate a
CodeGen model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the CodeGen
[Salesforce/codegen-2B-mono](https://huggingface.co/Salesforce/codegen-2B-mono) architecture. Configuration objects
inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from
[`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50400):
Vocabulary size of the CodeGen model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`CodeGenModel`].
n_positions (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
n_ctx (`int`, *optional*, defaults to 2048):
This attribute is used in `CodeGenModel.__init__` without any real effect.
n_embd (`int`, *optional*, defaults to 4096):
Dimensionality of the embeddings and hidden states.
n_layer (`int`, *optional*, defaults to 28):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
rotary_dim (`int`, *optional*, defaults to 64):
Number of dimensions in the embedding that Rotary Position Embedding is applied to.
n_inner (`int`, *optional*):
Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
activation_function (`str`, *optional*, defaults to `"gelu_new"`):
Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
resid_pdrop (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`int`, *optional*, defaults to 0.0):
The dropout ratio for the embeddings.
attn_pdrop (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
The epsilon to use in the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
bos_token_id (`int`, *optional*, defaults to 50256):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 50256):
End of stream token id.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the
model has a output word embedding layer.
Example:
```python
>>> from transformers import CodeGenConfig, CodeGenModel
>>> # Initializing a CodeGen 6B configuration
>>> configuration = CodeGenConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = CodeGenModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "codegen"
attribute_map = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
vocab_size=50400,
n_positions=2048,
n_ctx=2048,
n_embd=4096,
n_layer=28,
n_head=16,
rotary_dim=64,
n_inner=None,
activation_function="gelu_new",
resid_pdrop=0.0,
embd_pdrop=0.0,
attn_pdrop=0.0,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
use_cache=True,
bos_token_id=50256,
eos_token_id=50256,
tie_word_embeddings=False,
**kwargs,
):
self.vocab_size = vocab_size
self.n_ctx = n_ctx
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.n_inner = n_inner
self.rotary_dim = rotary_dim
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.use_cache = use_cache
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
super().__init__(
bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
)
# Copied from transformers.models.gpt2.configuration_gpt2.GPT2OnnxConfig
class CodeGenOnnxConfig(OnnxConfigWithPast):
def __init__(
self,
config: PretrainedConfig,
task: str = "default",
patching_specs: List[PatchingSpec] = None,
use_past: bool = False,
):
super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)
if not getattr(self._config, "pad_token_id", None):
# TODO: how to do that better?
self._config.pad_token_id = 0
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
if self.use_past:
self.fill_with_past_key_values_(common_inputs, direction="inputs")
common_inputs["attention_mask"] = {0: "batch", 1: "past_sequence + sequence"}
else:
common_inputs["attention_mask"] = {0: "batch", 1: "sequence"}
return common_inputs
@property
def num_layers(self) -> int:
return self._config.n_layer
@property
def num_attention_heads(self) -> int:
return self._config.n_head
def generate_dummy_inputs(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
common_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
)
# We need to order the input in the way they appears in the forward()
ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]})
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
batch, seqlen = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
past_key_values_length = seqlen + 2
past_shape = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
ordered_inputs["past_key_values"] = [
(torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(self.num_layers)
]
ordered_inputs["attention_mask"] = common_inputs["attention_mask"]
if self.use_past:
mask_dtype = ordered_inputs["attention_mask"].dtype
ordered_inputs["attention_mask"] = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
)
return ordered_inputs
@property
def default_onnx_opset(self) -> int:
return 13
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/codegen/tokenization_codegen.py | # coding=utf-8
# Copyright 2022 The Salesforce authors, The Open AI Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for CodeGen"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
import regex as re
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
},
"merges_file": {
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"Salesforce/codegen-350M-mono": 2048,
}
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
characters the bpe code barfs on.
The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
tables between utf-8 bytes and unicode strings.
"""
bs = (
list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
)
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8 + n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
def get_pairs(word):
"""
Return set of symbol pairs in a word.
Word is represented as tuple of symbols (symbols being variable-length strings).
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
class CodeGenTokenizer(PreTrainedTokenizer):
"""
Construct a CodeGen tokenizer. Based on byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
```python
>>> from transformers import CodeGenTokenizer
>>> tokenizer = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
>>> tokenizer("Hello world")["input_ids"]
[15496, 995]
>>> tokenizer(" Hello world")["input_ids"]
[18435, 995]
```
You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
<Tip>
When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one).
</Tip>
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
bos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The beginning of sequence token.
eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The end of sequence token.
pad_token (`str`, *optional*):
The token used for padding, for example when batching sequences of different lengths.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (CodeGen tokenizer detect beginning of words by the preceding space).
add_bos_token (`bool`, *optional*, defaults to `False`):
Whether to add a beginning of sequence token at the start of sequences.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
merges_file,
errors="replace",
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
pad_token=None,
add_prefix_space=False,
add_bos_token=False,
**kwargs,
):
bos_token = AddedToken(bos_token, special=True) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, special=True) if isinstance(eos_token, str) else eos_token
unk_token = AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token
pad_token = AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token
self.add_bos_token = add_bos_token
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)
self.decoder = {v: k for k, v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
with open(merges_file, encoding="utf-8") as merges_handle:
bpe_merges = merges_handle.read().split("\n")[1:-1]
bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.cache = {}
self.add_prefix_space = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
super().__init__(
errors=errors,
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
pad_token=pad_token,
add_prefix_space=add_prefix_space,
add_bos_token=add_bos_token,
**kwargs,
)
@property
def vocab_size(self):
return len(self.encoder)
def get_vocab(self):
return dict(self.encoder, **self.added_tokens_encoder)
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token)
pairs = get_pairs(word)
if not pairs:
return token
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
except ValueError:
new_word.extend(word[i:])
break
else:
new_word.extend(word[i:j])
i = j
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = " ".join(word)
self.cache[token] = word
return word
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
if self.add_bos_token:
bos_token_ids = [self.bos_token_id]
else:
bos_token_ids = []
output = bos_token_ids + token_ids_0
if token_ids_1 is None:
return output
return output + bos_token_ids + token_ids_1
def _tokenize(self, text):
"""Tokenize a string."""
bpe_tokens = []
for token in re.findall(self.pat, text):
token = "".join(
self.byte_encoder[b] for b in token.encode("utf-8")
) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
return bpe_tokens
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decoder.get(index)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
text = "".join(tokens)
text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
return text
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
merge_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
)
with open(vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write("#version: 0.2\n")
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!"
)
index = token_index
writer.write(" ".join(bpe_tokens) + "\n")
index += 1
return vocab_file, merge_file
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
if is_split_into_words or add_prefix_space:
text = " " + text
return (text, kwargs)
def decode(
self,
token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = None,
truncate_before_pattern: Optional[List[str]] = None,
**kwargs,
) -> str:
"""
Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces.
Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.
Args:
token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (`bool`, *optional*):
Whether or not to clean up the tokenization spaces. If `None`, will default to
`self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
truncate_before_pattern (`List[str]`, *optional*, defaults to `None`):
A list of regular expression strings that will be used to truncate the returned string. This can be
used to remove extra pieces of code (e.g. truncate if observing a comment symbol "#" at the beginning
of a new line). An example pattern could be `["^#", re.escape("<|endoftext|>"), "^'''", "\n\n\n"]`.
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific decode method.
Returns:
`str`: The decoded sentence.
"""
decoded_text = super()._decode(
token_ids=token_ids,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
**kwargs,
)
if truncate_before_pattern is not None and len(truncate_before_pattern) > 0:
decoded_text = self.truncate(decoded_text, truncate_before_pattern)
return decoded_text
def truncate(self, completion, truncate_before_pattern):
def find_re(string, pattern, start_pos):
m = pattern.search(string, start_pos)
return m.start() if m else -1
terminals = [re.compile(pattern, re.MULTILINE) for pattern in truncate_before_pattern]
prints = list(re.finditer("^print", completion, re.MULTILINE))
if len(prints) > 1:
completion = completion[: prints[1].start()]
defs = list(re.finditer("^def", completion, re.MULTILINE))
if len(defs) > 1:
completion = completion[: defs[1].start()]
start_pos = 0
terminals_pos = [
pos for pos in [find_re(completion, terminal, start_pos) for terminal in terminals] if pos != -1
]
if len(terminals_pos) > 0:
return completion[: min(terminals_pos)]
else:
return completion
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/codegen/__init__.py | # Copyright 2022 Salesforce authors, The EleutherAI, and HuggingFace Teams. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_import_structure = {
"configuration_codegen": ["CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP", "CodeGenConfig", "CodeGenOnnxConfig"],
"tokenization_codegen": ["CodeGenTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_codegen_fast"] = ["CodeGenTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_codegen"] = [
"CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST",
"CodeGenForCausalLM",
"CodeGenModel",
"CodeGenPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
from .tokenization_codegen import CodeGenTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_codegen_fast import CodeGenTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_codegen import (
CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
CodeGenForCausalLM,
CodeGenModel,
CodeGenPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/codegen/tokenization_codegen_fast.py | # coding=utf-8
# Copyright 2022 The Salesforce authors, The Open AI Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for OpenAI GPT."""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
},
"merges_file": {
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
},
"tokenizer_file": {
"Salesforce/codegen-350M-mono": (
"https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
),
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"Salesforce/codegen-350M-mono": 2048,
}
class CodeGenTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" CodeGen tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
```python
>>> from transformers import CodeGenTokenizerFast
>>> tokenizer = CodeGenTokenizerFast.from_pretrained("Salesforce/codegen-350M-mono")
>>> tokenizer("Hello world")["input_ids"]
[15496, 995]
>>> tokenizer(" Hello world")["input_ids"]
[18435, 995]
```
You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since
the model was not pretrained this way, it might yield a decrease in performance.
<Tip>
When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.
</Tip>
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`, *optional*):
Path to the vocabulary file.
merges_file (`str`, *optional*):
Path to the merges file.
tokenizer_file (`str`, *optional*):
Path to [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
contains everything needed to load the tokenizer.
unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
bos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The beginning of sequence token.
eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The end of sequence token.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (CodeGen tokenizer detect beginning of words by the preceding space).
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
slow_tokenizer_class = CodeGenTokenizer
def __init__(
self,
vocab_file=None,
merges_file=None,
tokenizer_file=None,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
add_prefix_space=False,
**kwargs,
):
super().__init__(
vocab_file,
merges_file,
tokenizer_file=tokenizer_file,
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
add_prefix_space=add_prefix_space,
**kwargs,
)
if kwargs.pop("add_bos_token", False):
model_id = kwargs.pop("name_or_path", "")
raise ValueError(
"Currenty GPT2's fast tokenizer does NOT support adding a BOS token. "
"Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"
f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
"This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."
" so that the fast tokenizer works correctly."
)
pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type"))
pre_tok_state["add_prefix_space"] = add_prefix_space
self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state)
self.add_prefix_space = add_prefix_space
def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
is_split_into_words = kwargs.get("is_split_into_words", False)
assert self.add_prefix_space or not is_split_into_words, (
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*args, **kwargs)
def _encode_plus(self, *args, **kwargs) -> BatchEncoding:
is_split_into_words = kwargs.get("is_split_into_words", False)
assert self.add_prefix_space or not is_split_into_words, (
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._encode_plus(*args, **kwargs)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
files = self._tokenizer.model.save(save_directory, name=filename_prefix)
return tuple(files)
def decode(
self,
token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = None,
truncate_before_pattern: Optional[List[str]] = None,
**kwargs,
) -> str:
"""
Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces.
Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.
Args:
token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (`bool`, *optional*):
Whether or not to clean up the tokenization spaces. If `None`, will default to
`self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
truncate_before_pattern (`List[str]`, *optional*, defaults to `None`):
A list of regular expression strings that will be used to truncate the returned string. This can be
used to remove extra pieces of code (e.g. truncate if observing a comment symbol "#" at the beginning
of a new line). An example pattern could be `["^#", re.escape("<|endoftext|>"), "^'''", "\n\n\n"]`.
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific decode method.
Returns:
`str`: The decoded sentence.
"""
decoded_text = super().decode(
token_ids=token_ids,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
**kwargs,
)
if truncate_before_pattern is not None and len(truncate_before_pattern) > 0:
decoded_text = self.truncate(decoded_text, truncate_before_pattern)
return decoded_text
def truncate(self, completion, truncate_before_pattern):
def find_re(string, pattern, start_pos):
m = pattern.search(string, start_pos)
return m.start() if m else -1
terminals = [re.compile(pattern, re.MULTILINE) for pattern in truncate_before_pattern]
prints = list(re.finditer("^print", completion, re.MULTILINE))
if len(prints) > 1:
completion = completion[: prints[1].start()]
defs = list(re.finditer("^def", completion, re.MULTILINE))
if len(defs) > 1:
completion = completion[: defs[1].start()]
start_pos = 0
terminals_pos = [
pos for pos in [find_re(completion, terminal, start_pos) for terminal in terminals] if pos != -1
]
if len(terminals_pos) > 0:
return completion[: min(terminals_pos)]
else:
return completion
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/tapas/convert_tapas_original_tf_checkpoint_to_pytorch.py | # coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert TAPAS checkpoint."""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def convert_tf_checkpoint_to_pytorch(
task, reset_position_index_per_cell, tf_checkpoint_path, tapas_config_file, pytorch_dump_path
):
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
config = TapasConfig.from_json_file(tapas_config_file)
# set absolute/relative position embeddings parameter
config.reset_position_index_per_cell = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
model = TapasForQuestionAnswering(config=config)
elif task == "WTQ":
# run_task_main.py hparams
config.num_aggregation_labels = 4
config.use_answer_as_supervision = True
# hparam_utils.py hparams
config.answer_loss_cutoff = 0.664694
config.cell_selection_preference = 0.207951
config.huber_loss_delta = 0.121194
config.init_cell_selection_weights_to_zero = True
config.select_one_column = True
config.allow_empty_column_selection = False
config.temperature = 0.0352513
model = TapasForQuestionAnswering(config=config)
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
config.num_aggregation_labels = 4
config.use_answer_as_supervision = False
# hparam_utils.py hparams
config.answer_loss_cutoff = 36.4519
config.cell_selection_preference = 0.903421
config.huber_loss_delta = 222.088
config.init_cell_selection_weights_to_zero = True
config.select_one_column = True
config.allow_empty_column_selection = True
config.temperature = 0.763141
model = TapasForQuestionAnswering(config=config)
elif task == "TABFACT":
model = TapasForSequenceClassification(config=config)
elif task == "MLM":
model = TapasForMaskedLM(config=config)
elif task == "INTERMEDIATE_PRETRAINING":
model = TapasModel(config=config)
else:
raise ValueError(f"Task {task} not supported.")
print(f"Building PyTorch model from configuration: {config}")
# Load weights from tf checkpoint
load_tf_weights_in_tapas(model, config, tf_checkpoint_path)
# Save pytorch-model (weights and configuration)
print(f"Save PyTorch model to {pytorch_dump_path}")
model.save_pretrained(pytorch_dump_path)
# Save tokenizer files
print(f"Save tokenizer files to {pytorch_dump_path}")
tokenizer = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt", model_max_length=512)
tokenizer.save_pretrained(pytorch_dump_path)
print("Used relative position embeddings:", model.config.reset_position_index_per_cell)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
)
parser.add_argument(
"--reset_position_index_per_cell",
default=False,
action="store_true",
help="Whether to use relative position embeddings or not. Defaults to True.",
)
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--tapas_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained TAPAS model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
args = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/tapas/modeling_tapas.py | # coding=utf-8
# Copyright 2020 Google Research and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch TAPAS model."""
import enum
import math
import os
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, SequenceClassifierOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import (
apply_chunking_to_forward,
find_pruneable_heads_and_indices,
is_torch_greater_or_equal_than_1_12,
prune_linear_layer,
)
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_tapas import TapasConfig
logger = logging.get_logger(__name__)
if not is_torch_greater_or_equal_than_1_12:
logger.warning(
f"You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use "
"TapasModel. Please upgrade torch."
)
_CONFIG_FOR_DOC = "TapasConfig"
_CHECKPOINT_FOR_DOC = "google/tapas-base"
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST = [
# large models
"google/tapas-large",
"google/tapas-large-finetuned-sqa",
"google/tapas-large-finetuned-wtq",
"google/tapas-large-finetuned-wikisql-supervised",
"google/tapas-large-finetuned-tabfact",
# base models
"google/tapas-base",
"google/tapas-base-finetuned-sqa",
"google/tapas-base-finetuned-wtq",
"google/tapas-base-finetuned-wikisql-supervised",
"google/tapas-base-finetuned-tabfact",
# small models
"google/tapas-small",
"google/tapas-small-finetuned-sqa",
"google/tapas-small-finetuned-wtq",
"google/tapas-small-finetuned-wikisql-supervised",
"google/tapas-small-finetuned-tabfact",
# mini models
"google/tapas-mini",
"google/tapas-mini-finetuned-sqa",
"google/tapas-mini-finetuned-wtq",
"google/tapas-mini-finetuned-wikisql-supervised",
"google/tapas-mini-finetuned-tabfact",
# tiny models
"google/tapas-tiny",
"google/tapas-tiny-finetuned-sqa",
"google/tapas-tiny-finetuned-wtq",
"google/tapas-tiny-finetuned-wikisql-supervised",
"google/tapas-tiny-finetuned-tabfact",
# See all TAPAS models at https://huggingface.co/models?filter=tapas
]
EPSILON_ZERO_DIVISION = 1e-10
CLOSE_ENOUGH_TO_LOG_ZERO = -10000.0
@dataclass
class TableQuestionAnsweringOutput(ModelOutput):
"""
Output type of [`TapasForQuestionAnswering`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` (and possibly `answer`, `aggregation_labels`, `numeric_values` and `numeric_values_scale` are provided)):
Total loss as the sum of the hierarchical cell selection log-likelihood loss and (optionally) the
semi-supervised regression loss and (optionally) supervised loss for aggregations.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Prediction scores of the cell selection head, for every token.
logits_aggregation (`torch.FloatTensor`, *optional*, of shape `(batch_size, num_aggregation_labels)`):
Prediction scores of the aggregation head, for every aggregation operator.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
logits_aggregation: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
def load_tf_weights_in_tapas(model, config, tf_checkpoint_path):
"""
Load tf checkpoints in a PyTorch model. This is an adaptation from load_tf_weights_in_bert
- add cell selection and aggregation heads
- take into account additional token type embedding layers
"""
try:
import re
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions."
)
raise
tf_path = os.path.abspath(tf_checkpoint_path)
logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
logger.info(f"Loading TF weight {name} with shape {shape}")
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array)
for name, array in zip(names, arrays):
name = name.split("/")
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculate m and v
# which are not required for using pretrained model
if any(
n
in [
"adam_v",
"adam_m",
"AdamWeightDecayOptimizer",
"AdamWeightDecayOptimizer_1",
"global_step",
"seq_relationship",
]
for n in name
):
logger.info(f"Skipping {'/'.join(name)}")
continue
# in case the model is TapasForSequenceClassification, we skip output_bias and output_weights
# since these are not used for classification
if isinstance(model, TapasForSequenceClassification):
if any(n in ["output_bias", "output_weights"] for n in name):
logger.info(f"Skipping {'/'.join(name)}")
continue
# in case the model is TapasModel, we skip output_bias, output_weights, output_bias_cls and output_weights_cls
# since this model does not have MLM and NSP heads
if isinstance(model, TapasModel):
if any(n in ["output_bias", "output_weights", "output_bias_cls", "output_weights_cls"] for n in name):
logger.info(f"Skipping {'/'.join(name)}")
continue
# in case the model is TapasForMaskedLM, we skip the pooler
if isinstance(model, TapasForMaskedLM):
if any(n in ["pooler"] for n in name):
logger.info(f"Skipping {'/'.join(name)}")
continue
# if first scope name starts with "bert", change it to "tapas"
if name[0] == "bert":
name[0] = "tapas"
pointer = model
for m_name in name:
if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
scope_names = re.split(r"_(\d+)", m_name)
else:
scope_names = [m_name]
if scope_names[0] == "kernel" or scope_names[0] == "gamma":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "beta":
pointer = getattr(pointer, "bias")
# cell selection heads
elif scope_names[0] == "output_bias":
if not isinstance(model, TapasForMaskedLM):
pointer = getattr(pointer, "output_bias")
else:
pointer = getattr(pointer, "bias")
elif scope_names[0] == "output_weights":
pointer = getattr(pointer, "output_weights")
elif scope_names[0] == "column_output_bias":
pointer = getattr(pointer, "column_output_bias")
elif scope_names[0] == "column_output_weights":
pointer = getattr(pointer, "column_output_weights")
# aggregation head
elif scope_names[0] == "output_bias_agg":
pointer = getattr(pointer, "aggregation_classifier")
pointer = getattr(pointer, "bias")
elif scope_names[0] == "output_weights_agg":
pointer = getattr(pointer, "aggregation_classifier")
pointer = getattr(pointer, "weight")
# classification head
elif scope_names[0] == "output_bias_cls":
pointer = getattr(pointer, "classifier")
pointer = getattr(pointer, "bias")
elif scope_names[0] == "output_weights_cls":
pointer = getattr(pointer, "classifier")
pointer = getattr(pointer, "weight")
else:
try:
pointer = getattr(pointer, scope_names[0])
except AttributeError:
logger.info(f"Skipping {'/'.join(name)}")
continue
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
if m_name[-11:] == "_embeddings":
pointer = getattr(pointer, "weight")
elif m_name[-13:] in [f"_embeddings_{i}" for i in range(7)]:
pointer = getattr(pointer, "weight")
elif m_name == "kernel":
array = np.transpose(array)
try:
if pointer.shape != array.shape:
raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
logger.info(f"Initialize PyTorch weight {name}")
# Added a check to see whether the array is a scalar (because bias terms in Tapas checkpoints can be
# scalar => should first be converted to numpy arrays)
if np.isscalar(array):
array = np.array(array)
pointer.data = torch.from_numpy(array)
return model
class TapasEmbeddings(nn.Module):
"""
Construct the embeddings from word, position and token_type embeddings. Same as BertEmbeddings but with a number of
additional token type embeddings to encode tabular structure.
"""
def __init__(self, config):
super().__init__()
# we do not include config.disabled_features and config.disable_position_embeddings from the original implementation
# word embeddings
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
# position embeddings
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
# token type embeddings
for i, type_vocab_sizes in enumerate(config.type_vocab_sizes):
name = f"token_type_embeddings_{i}"
setattr(self, name, nn.Embedding(type_vocab_sizes, config.hidden_size))
self.number_of_token_type_embeddings = len(config.type_vocab_sizes)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.config = config
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
device = input_ids.device if input_ids is not None else inputs_embeds.device
if position_ids is None:
# create absolute position embeddings
position_ids = torch.arange(seq_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).expand(input_shape)
# when self.config.reset_position_index_per_cell is set to True, create relative position embeddings
if self.config.reset_position_index_per_cell:
# shape (batch_size, seq_len)
col_index = IndexMap(token_type_ids[:, :, 1], self.config.type_vocab_sizes[1], batch_dims=1)
# shape (batch_size, seq_len)
row_index = IndexMap(token_type_ids[:, :, 2], self.config.type_vocab_sizes[2], batch_dims=1)
# shape (batch_size, seq_len)
full_index = ProductIndexMap(col_index, row_index)
# shape (max_rows * max_columns,). First absolute position for every cell
first_position_per_segment = reduce_min(position_ids, full_index)[0]
# ? shape (batch_size, seq_len). First absolute position of the cell for every token
first_position = gather(first_position_per_segment, full_index)
# shape (1, seq_len)
position = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0)
position_ids = torch.min(
torch.as_tensor(self.config.max_position_embeddings - 1, device=device), position - first_position
)
if token_type_ids is None:
token_type_ids = torch.zeros(
(input_shape + self.number_of_token_type_embeddings), dtype=torch.long, device=device
)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
position_embeddings = self.position_embeddings(position_ids)
embeddings = inputs_embeds + position_embeddings
for i in range(self.number_of_token_type_embeddings):
name = f"token_type_embeddings_{i}"
embeddings += getattr(self, name)(token_type_ids[:, :, i])
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class TapasSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size {config.hidden_size} is not a multiple of the number of attention "
f"heads {config.num_attention_heads}"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
if self.is_decoder:
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in TapasModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
class TapasSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class TapasAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = TapasSelfAttention(config)
self.output = TapasSelfOutput(config)
self.pruned_heads = set()
# Copied from transformers.models.bert.modeling_bert.BertAttention.prune_heads
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
# Copied from transformers.models.bert.modeling_bert.BertAttention.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
class TapasIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOutput
class TapasOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class TapasLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = TapasAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
self.crossattention = TapasAttention(config)
self.intermediate = TapasIntermediate(config)
self.output = TapasOutput(config)
# Copied from transformers.models.bert.modeling_bert.BertLayer.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and encoder_hidden_states is not None:
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
" by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
cross_attn_past_key_value,
output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertLayer.feed_forward_chunk
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class TapasEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([TapasLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_values,
output_attentions,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_values,
output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
# Copied from transformers.models.bert.modeling_bert.BertPooler
class TapasPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->Tapas
class TapasPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->Tapas
class TapasLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = TapasPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->Tapas
class TapasOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = TapasLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores
class TapasPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = TapasConfig
base_model_prefix = "tapas"
supports_gradient_checkpointing = True
# Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
TAPAS_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its models (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`TapasConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
TAPAS_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0}, 7)`, *optional*):
Token indices that encode tabular structure. Indices can be obtained using [`AutoTokenizer`]. See this
class for more info.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. If
`reset_position_index_per_cell` of [`TapasConfig`] is set to `True`, relative position embeddings will be
used. Selected in the range `[0, config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1
indicates the head is **not masked**, - 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare Tapas Model transformer outputting raw hidden-states without any specific head on top.",
TAPAS_START_DOCSTRING,
)
class TapasModel(TapasPreTrainedModel):
"""
This class is a small change compared to [`BertModel`], taking into account the additional token type ids.
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
"""
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = TapasEmbeddings(config)
self.encoder = TapasEncoder(config)
self.pooler = TapasPooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, TapasModel
>>> import pandas as pd
>>> tokenizer = AutoTokenizer.from_pretrained("google/tapas-base")
>>> model = TapasModel.from_pretrained("google/tapas-base")
>>> data = {
... "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
... "Age": ["56", "45", "59"],
... "Number of movies": ["87", "53", "69"],
... }
>>> table = pd.DataFrame.from_dict(data)
>>> queries = ["How many movies has George Clooney played in?", "How old is Brad Pitt?"]
>>> inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
if token_type_ids is None:
token_type_ids = torch.zeros(
(*input_shape, len(self.config.type_vocab_sizes)), dtype=torch.long, device=device
)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@add_start_docstrings("""Tapas Model with a `language modeling` head on top.""", TAPAS_START_DOCSTRING)
class TapasForMaskedLM(TapasPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
config_class = TapasConfig
base_model_prefix = "tapas"
def __init__(self, config):
super().__init__(config)
self.tapas = TapasModel(config, add_pooling_layer=False)
self.cls = TapasOnlyMLMHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[Tuple, MaskedLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, TapasForMaskedLM
>>> import pandas as pd
>>> tokenizer = AutoTokenizer.from_pretrained("google/tapas-base")
>>> model = TapasForMaskedLM.from_pretrained("google/tapas-base")
>>> data = {
... "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
... "Age": ["56", "45", "59"],
... "Number of movies": ["87", "53", "69"],
... }
>>> table = pd.DataFrame.from_dict(data)
>>> inputs = tokenizer(
... table=table, queries="How many [MASK] has George [MASK] played in?", return_tensors="pt"
... )
>>> labels = tokenizer(
... table=table, queries="How many movies has George Clooney played in?", return_tensors="pt"
... )["input_ids"]
>>> outputs = model(**inputs, labels=labels)
>>> logits = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.tapas(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss() # -100 index = padding token
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
Tapas Model with a cell selection head and optional aggregation head on top for question-answering tasks on tables
(linear layers on top of the hidden-states output to compute `logits` and optional `logits_aggregation`), e.g. for
SQA, WTQ or WikiSQL-supervised tasks.
""",
TAPAS_START_DOCSTRING,
)
class TapasForQuestionAnswering(TapasPreTrainedModel):
def __init__(self, config: TapasConfig):
super().__init__(config)
# base model
self.tapas = TapasModel(config)
# dropout (only used when training)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# cell selection heads
if config.init_cell_selection_weights_to_zero:
# init_cell_selection_weights_to_zero: Whether the initial weights should be
# set to 0. This ensures that all tokens have the same prior probability.
self.output_weights = nn.Parameter(torch.zeros(config.hidden_size))
self.column_output_weights = nn.Parameter(torch.zeros(config.hidden_size))
else:
self.output_weights = nn.Parameter(torch.empty(config.hidden_size))
nn.init.normal_(
self.output_weights, std=config.initializer_range
) # here, a truncated normal is used in the original implementation
self.column_output_weights = nn.Parameter(torch.empty(config.hidden_size))
nn.init.normal_(
self.column_output_weights, std=config.initializer_range
) # here, a truncated normal is used in the original implementation
self.output_bias = nn.Parameter(torch.zeros([]))
self.column_output_bias = nn.Parameter(torch.zeros([]))
# aggregation head
if config.num_aggregation_labels > 0:
self.aggregation_classifier = nn.Linear(config.hidden_size, config.num_aggregation_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TableQuestionAnsweringOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
table_mask: Optional[torch.LongTensor] = None,
labels: Optional[torch.LongTensor] = None,
aggregation_labels: Optional[torch.LongTensor] = None,
float_answer: Optional[torch.FloatTensor] = None,
numeric_values: Optional[torch.FloatTensor] = None,
numeric_values_scale: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, TableQuestionAnsweringOutput]:
r"""
table_mask (`torch.LongTensor` of shape `(batch_size, seq_length)`, *optional*):
Mask for the table. Indicates which tokens belong to the table (1). Question tokens, table headers and
padding are 0.
labels (`torch.LongTensor` of shape `(batch_size, seq_length)`, *optional*):
Labels per token for computing the hierarchical cell selection loss. This encodes the positions of the
answer appearing in the table. Can be obtained using [`AutoTokenizer`].
- 1 for tokens that are **part of the answer**,
- 0 for tokens that are **not part of the answer**.
aggregation_labels (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
Aggregation function index for every example in the batch for computing the aggregation loss. Indices
should be in `[0, ..., config.num_aggregation_labels - 1]`. Only required in case of strong supervision for
aggregation (WikiSQL-supervised).
float_answer (`torch.FloatTensor` of shape `(batch_size, )`, *optional*):
Float answer for every example in the batch. Set to *float('nan')* for cell selection questions. Only
required in case of weak supervision (WTQ) to calculate the aggregate mask and regression loss.
numeric_values (`torch.FloatTensor` of shape `(batch_size, seq_length)`, *optional*):
Numeric values of every token, NaN for tokens which are not numeric values. Can be obtained using
[`AutoTokenizer`]. Only required in case of weak supervision for aggregation (WTQ) to calculate the
regression loss.
numeric_values_scale (`torch.FloatTensor` of shape `(batch_size, seq_length)`, *optional*):
Scale of the numeric values of every token. Can be obtained using [`AutoTokenizer`]. Only required in case
of weak supervision for aggregation (WTQ) to calculate the regression loss.
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, TapasForQuestionAnswering
>>> import pandas as pd
>>> tokenizer = AutoTokenizer.from_pretrained("google/tapas-base-finetuned-wtq")
>>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq")
>>> data = {
... "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
... "Age": ["56", "45", "59"],
... "Number of movies": ["87", "53", "69"],
... }
>>> table = pd.DataFrame.from_dict(data)
>>> queries = ["How many movies has George Clooney played in?", "How old is Brad Pitt?"]
>>> inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> logits_aggregation = outputs.logits_aggregation
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.tapas(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
pooled_output = outputs[1]
sequence_output = self.dropout(sequence_output)
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
device = input_ids.device if input_ids is not None else inputs_embeds.device
# Construct indices for the table.
if token_type_ids is None:
token_type_ids = torch.zeros(
(*input_shape, len(self.config.type_vocab_sizes)), dtype=torch.long, device=device
)
token_types = [
"segment_ids",
"column_ids",
"row_ids",
"prev_labels",
"column_ranks",
"inv_column_ranks",
"numeric_relations",
]
row_ids = token_type_ids[:, :, token_types.index("row_ids")]
column_ids = token_type_ids[:, :, token_types.index("column_ids")]
row_index = IndexMap(
indices=torch.min(row_ids, torch.as_tensor(self.config.max_num_rows - 1, device=row_ids.device)),
num_segments=self.config.max_num_rows,
batch_dims=1,
)
col_index = IndexMap(
indices=torch.min(column_ids, torch.as_tensor(self.config.max_num_columns - 1, device=column_ids.device)),
num_segments=self.config.max_num_columns,
batch_dims=1,
)
cell_index = ProductIndexMap(row_index, col_index)
# Masks.
input_shape = input_ids.size() if input_ids is not None else inputs_embeds.size()[:-1]
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
# Table cells only, without question tokens and table headers.
if table_mask is None:
table_mask = torch.where(row_ids > 0, torch.ones_like(row_ids), torch.zeros_like(row_ids))
# torch.FloatTensor[batch_size, seq_length]
input_mask_float = attention_mask.float().to(device)
table_mask_float = table_mask.float().to(device)
# Mask for cells that exist in the table (i.e. that are not padding).
cell_mask, _ = reduce_mean(input_mask_float, cell_index)
# Compute logits per token. These are used to select individual cells.
logits = compute_token_logits(sequence_output, self.config.temperature, self.output_weights, self.output_bias)
# Compute logits per column. These are used to select a column.
column_logits = None
if self.config.select_one_column:
column_logits = compute_column_logits(
sequence_output,
self.column_output_weights,
self.column_output_bias,
cell_index,
cell_mask,
self.config.allow_empty_column_selection,
)
# Aggregation logits
logits_aggregation = None
if self.config.num_aggregation_labels > 0:
logits_aggregation = self.aggregation_classifier(pooled_output)
# Total loss calculation
total_loss = 0.0
calculate_loss = False
if labels is not None:
calculate_loss = True
is_supervised = not self.config.num_aggregation_labels > 0 or not self.config.use_answer_as_supervision
# Semi-supervised cell selection in case of no aggregation:
# If the answer (the denotation) appears directly in the table we might
# select the answer without applying any aggregation function. There are
# some ambiguous cases, see utils._calculate_aggregate_mask for more info.
# `aggregate_mask` is 1 for examples where we chose to aggregate and 0
# for examples where we chose to select the answer directly.
# `labels` encodes the positions of the answer appearing in the table.
if is_supervised:
aggregate_mask = None
else:
if float_answer is not None:
assert (
labels.shape[0] == float_answer.shape[0]
), "Make sure the answers are a FloatTensor of shape (batch_size,)"
# <float32>[batch_size]
aggregate_mask = _calculate_aggregate_mask(
float_answer,
pooled_output,
self.config.cell_selection_preference,
labels,
self.aggregation_classifier,
)
else:
raise ValueError("You have to specify float answers in order to calculate the aggregate mask")
# Cell selection log-likelihood
if self.config.average_logits_per_cell:
logits_per_cell, _ = reduce_mean(logits, cell_index)
logits = gather(logits_per_cell, cell_index)
dist_per_token = torch.distributions.Bernoulli(logits=logits)
# Compute cell selection loss per example.
selection_loss_per_example = None
if not self.config.select_one_column:
weight = torch.where(
labels == 0,
torch.ones_like(labels, dtype=torch.float32),
self.config.positive_label_weight * torch.ones_like(labels, dtype=torch.float32),
)
selection_loss_per_token = -dist_per_token.log_prob(labels) * weight
selection_loss_per_example = torch.sum(selection_loss_per_token * input_mask_float, dim=1) / (
torch.sum(input_mask_float, dim=1) + EPSILON_ZERO_DIVISION
)
else:
selection_loss_per_example, logits = _single_column_cell_selection_loss(
logits, column_logits, labels, cell_index, col_index, cell_mask
)
dist_per_token = torch.distributions.Bernoulli(logits=logits)
# Supervised cell selection
if self.config.disable_per_token_loss:
pass
elif is_supervised:
total_loss += torch.mean(selection_loss_per_example)
else:
# For the not supervised case, do not assign loss for cell selection
total_loss += torch.mean(selection_loss_per_example * (1.0 - aggregate_mask))
# Semi-supervised regression loss and supervised loss for aggregations
if self.config.num_aggregation_labels > 0:
if is_supervised:
# Note that `aggregate_mask` is None if the setting is supervised.
if aggregation_labels is not None:
assert (
labels.shape[0] == aggregation_labels.shape[0]
), "Make sure the aggregation labels are a LongTensor of shape (batch_size,)"
per_example_additional_loss = _calculate_aggregation_loss(
logits_aggregation,
aggregate_mask,
aggregation_labels,
self.config.use_answer_as_supervision,
self.config.num_aggregation_labels,
self.config.aggregation_loss_weight,
)
else:
raise ValueError(
"You have to specify aggregation labels in order to calculate the aggregation loss"
)
else:
# Set aggregation labels to zeros
aggregation_labels = torch.zeros(labels.shape[0], dtype=torch.long, device=labels.device)
per_example_additional_loss = _calculate_aggregation_loss(
logits_aggregation,
aggregate_mask,
aggregation_labels,
self.config.use_answer_as_supervision,
self.config.num_aggregation_labels,
self.config.aggregation_loss_weight,
)
if self.config.use_answer_as_supervision:
if numeric_values is not None and numeric_values_scale is not None:
assert numeric_values.shape == numeric_values_scale.shape
# Add regression loss for numeric answers which require aggregation.
answer_loss, large_answer_loss_mask = _calculate_regression_loss(
float_answer,
aggregate_mask,
dist_per_token,
numeric_values,
numeric_values_scale,
table_mask_float,
logits_aggregation,
self.config,
)
per_example_additional_loss += answer_loss
# Zero loss for examples with answer_loss > cutoff.
per_example_additional_loss *= large_answer_loss_mask
else:
raise ValueError(
"You have to specify numeric values and numeric values scale in order to calculate the"
" regression loss"
)
total_loss += torch.mean(per_example_additional_loss)
else:
# if no label ids are provided, set them to zeros in order to properly compute logits
labels = torch.zeros_like(logits)
_, logits = _single_column_cell_selection_loss(
logits, column_logits, labels, cell_index, col_index, cell_mask
)
if not return_dict:
output = (logits, logits_aggregation) + outputs[2:]
return ((total_loss,) + output) if calculate_loss else output
return TableQuestionAnsweringOutput(
loss=total_loss if calculate_loss else None,
logits=logits,
logits_aggregation=logits_aggregation,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
Tapas Model with a sequence classification head on top (a linear layer on top of the pooled output), e.g. for table
entailment tasks, such as TabFact (Chen et al., 2020).
""",
TAPAS_START_DOCSTRING,
)
class TapasForSequenceClassification(TapasPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.tapas = TapasModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy). Note: this is called
"classification_class_index" in the original implementation.
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, TapasForSequenceClassification
>>> import torch
>>> import pandas as pd
>>> tokenizer = AutoTokenizer.from_pretrained("google/tapas-base-finetuned-tabfact")
>>> model = TapasForSequenceClassification.from_pretrained("google/tapas-base-finetuned-tabfact")
>>> data = {
... "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
... "Age": ["56", "45", "59"],
... "Number of movies": ["87", "53", "69"],
... }
>>> table = pd.DataFrame.from_dict(data)
>>> queries = [
... "There is only one actor who is 45 years old",
... "There are 3 actors which played in more than 60 movies",
... ]
>>> inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="pt")
>>> labels = torch.tensor([1, 0]) # 1 means entailed, 0 means refuted
>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.tapas(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
""" TAPAS utilities."""
class AverageApproximationFunction(str, enum.Enum):
RATIO = "ratio"
FIRST_ORDER = "first_order"
SECOND_ORDER = "second_order"
# Beginning of everything related to segmented tensors
class IndexMap(object):
"""Index grouping entries within a tensor."""
def __init__(self, indices, num_segments, batch_dims=0):
"""
Creates an index
Args:
indices (`torch.LongTensor`, same shape as a *values* Tensor to which the indices refer):
Tensor containing the indices.
num_segments (`torch.LongTensor`):
Scalar tensor, the number of segments. All elements in a batched segmented tensor must have the same
number of segments (although many segments can be empty).
batch_dims (`int`, *optional*, defaults to 0):
The number of batch dimensions. The first *batch_dims* dimensions of a SegmentedTensor are treated as
batch dimensions. Segments in different batch elements are always distinct even if they have the same
index.
"""
self.indices = torch.as_tensor(indices)
self.num_segments = torch.as_tensor(num_segments, device=indices.device)
self.batch_dims = batch_dims
def batch_shape(self):
return self.indices.size()[: self.batch_dims] # returns a torch.Size object
class ProductIndexMap(IndexMap):
"""The product of two indices."""
def __init__(self, outer_index, inner_index):
"""
Combines indices i and j into pairs (i, j). The result is an index where each segment (i, j) is the
intersection of segments i and j. For example if the inputs represent table cells indexed by respectively rows
and columns the output will be a table indexed by (row, column) pairs, i.e. by cell. The implementation
combines indices {0, .., n - 1} and {0, .., m - 1} into {0, .., nm - 1}. The output has *num_segments* equal to
*outer_index.num_segments* * *inner_index.num_segments*
Args:
outer_index (`IndexMap`):
IndexMap.
inner_index (`IndexMap`):
IndexMap, must have the same shape as *outer_index*.
"""
if outer_index.batch_dims != inner_index.batch_dims:
raise ValueError("outer_index.batch_dims and inner_index.batch_dims must be the same.")
super().__init__(
indices=(inner_index.indices + outer_index.indices * inner_index.num_segments),
num_segments=inner_index.num_segments * outer_index.num_segments,
batch_dims=inner_index.batch_dims,
)
self.outer_index = outer_index
self.inner_index = inner_index
def project_outer(self, index):
"""Projects an index with the same index set onto the outer components."""
indices = torch.div(index.indices, self.inner_index.num_segments, rounding_mode="floor").type(torch.long)
return IndexMap(indices=indices, num_segments=self.outer_index.num_segments, batch_dims=index.batch_dims)
def project_inner(self, index):
"""Projects an index with the same index set onto the inner components."""
return IndexMap(
indices=torch.fmod(index.indices, self.inner_index.num_segments)
.type(torch.float)
.floor()
.type(torch.long),
num_segments=self.inner_index.num_segments,
batch_dims=index.batch_dims,
)
def gather(values, index, name="segmented_gather"):
"""
Gathers from *values* using the index map. For each element in the domain of the index map this operation looks up
a value for that index in *values*. Two elements from the same segment always get assigned the same value.
Args:
values (`torch.Tensor` of shape (B1, ..., Bn, num_segments, V1, ...)):
Tensor with segment values.
index (`IndexMap` of shape (B1, ..., Bn, I1, ..., Ik)):
IndexMap.
name (`str`, *optional*, defaults to 'segmented_gather'):
Name for the operation. Currently not used
Returns:
`tuple(torch.Tensor)`: Tensor of shape (B1, ..., Bn, I1, ..., Ik, V1, ...) with the gathered values.
"""
indices = index.indices
# first, check whether the indices of the index represent scalar values (i.e. not vectorized)
if len(values.shape[index.batch_dims :]) < 2:
return torch.gather(
values,
index.batch_dims,
indices.view(
values.size()[0], -1
), # torch.gather expects index to have the same number of dimensions as values
).view(indices.size())
else:
# this means we have a vectorized version
# we have to adjust the index
indices = indices.unsqueeze(-1).expand(values.shape)
return torch.gather(values, index.batch_dims, indices)
def flatten(index, name="segmented_flatten"):
"""
Flattens a batched index map (which is typically of shape batch_size, seq_length) to a 1d index map. This operation
relabels the segments to keep batch elements distinct. The k-th batch element will have indices shifted by
*num_segments* * (k - 1). The result is a tensor with *num_segments* multiplied by the number of elements in the
batch.
Args:
index (`IndexMap`):
IndexMap to flatten.
name (`str`, *optional*, defaults to 'segmented_flatten'):
Name for the operation. Currently not used
Returns:
(`IndexMap`): The flattened IndexMap.
"""
# first, get batch_size as scalar tensor
batch_size = torch.prod(torch.tensor(list(index.batch_shape())))
# next, create offset as 1-D tensor of length batch_size,
# and multiply element-wise by num segments (to offset different elements in the batch) e.g. if batch size is 2: [0, 64]
offset = torch.arange(start=0, end=batch_size, device=index.num_segments.device) * index.num_segments
offset = offset.view(index.batch_shape())
for _ in range(index.batch_dims, len(index.indices.size())): # typically range(1,2)
offset = offset.unsqueeze(-1)
indices = offset + index.indices
return IndexMap(indices=indices.view(-1), num_segments=index.num_segments * batch_size, batch_dims=0)
def range_index_map(batch_shape, num_segments, name="range_index_map"):
"""
Constructs an index map equal to range(num_segments).
Args:
batch_shape (`torch.Size`):
Batch shape
num_segments (`int`):
Number of segments
name (`str`, *optional*, defaults to 'range_index_map'):
Name for the operation. Currently not used
Returns:
(`IndexMap`): IndexMap of shape batch_shape with elements equal to range(num_segments).
"""
batch_shape = torch.as_tensor(
batch_shape, dtype=torch.long
) # create a rank 1 tensor vector containing batch_shape (e.g. [2])
assert len(batch_shape.size()) == 1
num_segments = torch.as_tensor(num_segments) # create a rank 0 tensor (scalar) containing num_segments (e.g. 64)
assert len(num_segments.size()) == 0
indices = torch.arange(
start=0, end=num_segments, device=num_segments.device
) # create a rank 1 vector with num_segments elements
new_tensor = torch.cat(
[torch.ones_like(batch_shape, dtype=torch.long, device=num_segments.device), num_segments.unsqueeze(dim=0)],
dim=0,
)
# new_tensor is just a vector of [1 64] for example (assuming only 1 batch dimension)
new_shape = [int(x) for x in new_tensor.tolist()]
indices = indices.view(new_shape)
multiples = torch.cat([batch_shape, torch.as_tensor([1])], dim=0)
indices = indices.repeat(multiples.tolist())
# equivalent (in Numpy:)
# indices = torch.as_tensor(np.tile(indices.numpy(), multiples.tolist()))
return IndexMap(indices=indices, num_segments=num_segments, batch_dims=list(batch_shape.size())[0])
def _segment_reduce(values, index, segment_reduce_fn, name):
"""
Applies a segment reduction segment-wise.
Args:
values (`torch.Tensor`):
Tensor with segment values.
index (`IndexMap`):
IndexMap.
segment_reduce_fn (`str`):
Name for the reduce operation. One of "sum", "mean", "max" or "min".
name (`str`):
Name for the operation. Currently not used
Returns:
(`IndexMap`): IndexMap of shape batch_shape with elements equal to range(num_segments).
"""
# Flatten the batch dimensions, as segments ops (scatter) do not support batching.
# However if `values` has extra dimensions to the right keep them
# unflattened. Segmented ops support vector-valued operations.
flat_index = flatten(index)
vector_shape = values.size()[len(index.indices.size()) :] # torch.Size object
flattened_shape = torch.cat(
[torch.as_tensor([-1], dtype=torch.long), torch.as_tensor(vector_shape, dtype=torch.long)], dim=0
)
# changed "view" by "reshape" in the following line
flat_values = values.reshape(flattened_shape.tolist())
out = torch.zeros(int(flat_index.num_segments), dtype=torch.float, device=flat_values.device)
segment_means = out.scatter_reduce(
dim=0, index=flat_index.indices.long(), src=flat_values.float(), reduce=segment_reduce_fn, include_self=False
)
# Unflatten the values.
new_shape = torch.cat(
[
torch.as_tensor(index.batch_shape(), dtype=torch.long),
torch.as_tensor([index.num_segments], dtype=torch.long),
torch.as_tensor(vector_shape, dtype=torch.long),
],
dim=0,
)
output_values = segment_means.clone().view(new_shape.tolist()).to(values.dtype)
output_index = range_index_map(index.batch_shape(), index.num_segments)
return output_values, output_index
def reduce_sum(values, index, name="segmented_reduce_sum"):
"""
Sums a tensor over its segments.
Outputs 0 for empty segments.
This operations computes the sum over segments, with support for:
- Batching using the first dimensions [B1, B2, ..., Bn]. Each element in a batch can have different indices.
- Vectorization using the last dimension [V1, V2, ...]. If they are present, the output will be a sum of
vectors rather than scalars. Only the middle dimensions [I1, ..., Ik] are reduced by the operation.
Args:
values (`torch.Tensor` of shape [B1, B2, ..., Bn, I1, .., Ik, V1, V2, ..]):
Tensor containing the values of which the sum must be taken segment-wise.
index (`IndexMap`, indices are of shape [B1, B2, ..., Bn, I1, .., Ik].):
Index defining the segments.
name (`str`, *optional*, defaults to 'segmented_reduce_sum'):
Name for the operation. Currently not used
Returns:
output_values (`torch.Tensor`of shape [B1, B2, ..., Bn, num_segments, V1, V2, ..]): Tensor containing the
output values. output_index (`IndexMap`): IndexMap with shape [B1, B2, ..., Bn, num_segments]. .
"""
return _segment_reduce(values, index, "sum", name)
def reduce_mean(values, index, name="segmented_reduce_mean"):
"""
Averages a tensor over its segments.
Outputs 0 for empty segments.
This operations computes the mean over segments, with support for:
- Batching using the first dimensions [B1, B2, ..., Bn]. Each element in a batch can have different indices.
- Vectorization using the last dimension [V1, V2, ...]. If they are present, the output will be a mean of
vectors rather than scalars.
Only the middle dimensions [I1, ..., Ik] are reduced by the operation.
Args:
values (`torch.Tensor` of shape [B1, B2, ..., Bn, I1, .., Ik, V1, V2, ..]):
Tensor containing the values of which the mean must be taken segment-wise.
index (`IndexMap`, indices are of shape [B1, B2, ..., Bn, I1, .., Ik].):
Index defining the segments.
name (`str`, *optional*, defaults to 'segmented_reduce_sum'):
Name for the operation. Currently not used
Returns:
output_values (`torch.Tensor`of shape [B1, B2, ..., Bn, num_segments, V1, V2, ..]): Tensor containing the
output values. output_index (`IndexMap`): IndexMap with shape [B1, B2, ..., Bn, num_segments].
"""
return _segment_reduce(values, index, "mean", name)
def reduce_max(values, index, name="segmented_reduce_max"):
"""
Computes the maximum over segments.
This operation computes the maximum over segments, with support for:
- Batching using the first dimensions [B1, B2, ..., Bn]. Each element in a batch can have different indices.
- Vectorization using the last dimension [V1, V2, ...]. If they are present, the output will be an element-wise
maximum of vectors rather than scalars.
Only the middle dimensions [I1, ..., Ik] are reduced by the operation.
Args:
values (`torch.Tensor` of shape [B1, B2, ..., Bn, I1, .., Ik, V1, V2, ..]):
Tensor containing the values of which the max must be taken segment-wise.
index (`IndexMap`, indices are of shape [B1, B2, ..., Bn, I1, .., Ik].):
Index defining the segments.
name (`str`, *optional*, defaults to 'segmented_reduce_sum'):
Name for the operation. Currently not used
Returns:
output_values (`torch.Tensor`of shape [B1, B2, ..., Bn, num_segments, V1, V2, ..]): Tensor containing the
output values. output_index (`IndexMap`): IndexMap with shape [B1, B2, ..., Bn, num_segments].
"""
return _segment_reduce(values, index, "amax", name)
def reduce_min(values, index, name="segmented_reduce_min"):
"""
Computes the minimum over segments.
This operations computes the minimum over segments, with support for:
- Batching using the first dimensions [B1, B2, ..., Bn]. Each element in a batch can have different indices.
- Vectorization using the last dimension [V1, V2, ...]. If they are present, the output will be an element-wise
minimum of vectors rather than scalars.
Only the middle dimensions [I1, ..., Ik] are reduced by the operation.
Args:
values (`torch.Tensor` of shape [B1, B2, ..., Bn, I1, .., Ik, V1, V2, ..]):
Tensor containing the values of which the min must be taken segment-wise.
index (`IndexMap`, indices are of shape [B1, B2, ..., Bn, I1, .., Ik].):
Index defining the segments.
name (`str`, *optional*, defaults to 'segmented_reduce_sum'):
Name for the operation. Currently not used
Returns:
output_values (`torch.Tensor`of shape [B1, B2, ..., Bn, num_segments, V1, V2, ..]): Tensor containing the
output values. output_index (`IndexMap`): IndexMap with shape [B1, B2, ..., Bn, num_segments].
"""
return _segment_reduce(values, index, "amin", name)
# End of everything related to segmented tensors
def compute_column_logits(
sequence_output, column_output_weights, column_output_bias, cell_index, cell_mask, allow_empty_column_selection
):
"""
Computes the column logits.
Args:
sequence_output (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Also known as last_hidden_state. Sequence of hidden-states at the output of the last layer of the model.
column_output_weights (`torch.FloatTensor` of shape `(hidden_size)`):
Weights of the linear layer for column selection.
column_output_bias (`torch.FloatTensor` of shape `()`):
Bias of the linear layer for column selection.
cell_index (`ProductIndexMap`):
Index that groups tokens into cells.
cell_mask (`torch.FloatTensor` of shape `(batch_size, max_num_rows * max_num_cols)`):
Mask for cells that exist in the table (i.e. that are not padding).
allow_empty_column_selection (`bool`):
Whether to allow not to select any column
Returns:
column_logits (`torch.FloatTensor`of shape `(batch_size, max_num_cols)`): Tensor containing the column logits
for every example in the batch.
"""
# First, compute the token logits (batch_size, seq_len) - without temperature
token_logits = torch.einsum("bsj,j->bs", sequence_output, column_output_weights) + column_output_bias
# Next, average the logits per cell (batch_size, max_num_cols*max_num_rows)
cell_logits, cell_logits_index = reduce_mean(token_logits, cell_index)
# Finally, average the logits per column (batch_size, max_num_cols)
column_index = cell_index.project_inner(cell_logits_index)
column_logits, out_index = reduce_sum(cell_logits * cell_mask, column_index)
cell_count, _ = reduce_sum(cell_mask, column_index)
column_logits /= cell_count + EPSILON_ZERO_DIVISION
# Mask columns that do not appear in the example.
is_padding = torch.logical_and(cell_count < 0.5, ~torch.eq(out_index.indices, 0))
column_logits += CLOSE_ENOUGH_TO_LOG_ZERO * torch.as_tensor(
is_padding, dtype=torch.float32, device=is_padding.device
)
if not allow_empty_column_selection:
column_logits += CLOSE_ENOUGH_TO_LOG_ZERO * torch.as_tensor(
torch.eq(out_index.indices, 0), dtype=torch.float32, device=out_index.indices.device
)
return column_logits
def _single_column_cell_selection_loss(token_logits, column_logits, labels, cell_index, col_index, cell_mask):
"""
Computes the loss for cell selection constrained to a single column. The loss is a hierarchical log-likelihood. The
model first predicts a column and then selects cells within that column (conditioned on the column). Cells outside
the selected column are never selected.
Args:
token_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Tensor containing the logits per token.
column_logits (`torch.FloatTensor` of shape `(batch_size, max_num_cols)`):
Tensor containing the logits per column.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Labels per token.
cell_index (`ProductIndexMap`):
Index that groups tokens into cells.
col_index (`IndexMap`):
Index that groups tokens into columns.
cell_mask (`torch.FloatTensor` of shape `(batch_size, max_num_rows * max_num_cols)`):
Mask for cells that exist in the table (i.e. that are not padding).
Returns:
selection_loss_per_example (`torch.FloatTensor` of shape `(batch_size,)`): Loss for each example. logits
(`torch.FloatTensor` of shape `(batch_size, sequence_length)`): New logits which are only allowed to select
cells in a single column. Logits outside of the most likely column according to *column_logits* will be set to
a very low value (such that the probabilities are 0).
"""
# Part 1: column loss
# First find the column we should select. We use the column with maximum number of selected cells.
labels_per_column, _ = reduce_sum(torch.as_tensor(labels, dtype=torch.float32, device=labels.device), col_index)
# shape of labels_per_column is (batch_size, max_num_cols). It contains the number of label ids for every column, for every example
column_label = torch.argmax(labels_per_column, dim=-1) # shape (batch_size,)
# Check if there are no selected cells in the column. In that case the model
# should predict the special column id 0, which means "select nothing".
no_cell_selected = torch.eq(
torch.max(labels_per_column, dim=-1)[0], 0
) # no_cell_selected is of shape (batch_size,) and equals True
# if an example of the batch has no cells selected (i.e. if there are no labels set to 1 for that example)
column_label = torch.where(
no_cell_selected.view(column_label.size()), torch.zeros_like(column_label), column_label
)
column_dist = torch.distributions.Categorical(logits=column_logits) # shape (batch_size, max_num_cols)
column_loss_per_example = -column_dist.log_prob(column_label)
# Part 2: cell loss
# Reduce the labels and logits to per-cell from per-token.
# logits_per_cell: shape (batch_size, max_num_rows*max_num_cols) i.e. (batch_size, 64*32)
logits_per_cell, _ = reduce_mean(token_logits, cell_index)
# labels_per_cell: shape (batch_size, 64*32), indicating whether each cell should be selected (1) or not (0)
labels_per_cell, labels_index = reduce_max(
torch.as_tensor(labels, dtype=torch.long, device=labels.device), cell_index
)
# Mask for the selected column.
# column_id_for_cells: shape (batch_size, 64*32), indicating to which column each cell belongs
column_id_for_cells = cell_index.project_inner(labels_index).indices
# column_mask: shape (batch_size, 64*32), equal to 1 if cell belongs to column to be selected
column_mask = torch.as_tensor(
torch.eq(column_id_for_cells, torch.unsqueeze(column_label, dim=-1)),
dtype=torch.float32,
device=cell_mask.device,
)
# Compute the log-likelihood for cells, but only for the selected column.
cell_dist = torch.distributions.Bernoulli(logits=logits_per_cell) # shape (batch_size, 64*32)
cell_log_prob = cell_dist.log_prob(labels_per_cell.type(torch.float32)) # shape(batch_size, 64*32)
cell_loss = -torch.sum(cell_log_prob * column_mask * cell_mask, dim=1)
# We need to normalize the loss by the number of cells in the column.
cell_loss /= torch.sum(column_mask * cell_mask, dim=1) + EPSILON_ZERO_DIVISION
selection_loss_per_example = column_loss_per_example
selection_loss_per_example += torch.where(
no_cell_selected.view(selection_loss_per_example.size()),
torch.zeros_like(selection_loss_per_example),
cell_loss,
)
# Set the probs outside the selected column (selected by the *model*)
# to 0. This ensures backwards compatibility with models that select
# cells from multiple columns.
selected_column_id = torch.as_tensor(
torch.argmax(column_logits, dim=-1), dtype=torch.long, device=column_logits.device
) # shape (batch_size,)
# selected_column_mask: shape (batch_size, 64*32), equal to 1 if cell belongs to column selected by the model
selected_column_mask = torch.as_tensor(
torch.eq(column_id_for_cells, torch.unsqueeze(selected_column_id, dim=-1)),
dtype=torch.float32,
device=selected_column_id.device,
)
# Never select cells with the special column id 0.
selected_column_mask = torch.where(
torch.eq(column_id_for_cells, 0).view(selected_column_mask.size()),
torch.zeros_like(selected_column_mask),
selected_column_mask,
)
new_logits_per_cell = logits_per_cell + CLOSE_ENOUGH_TO_LOG_ZERO * (1.0 - cell_mask * selected_column_mask)
logits = gather(new_logits_per_cell, cell_index)
return selection_loss_per_example, logits
def compute_token_logits(sequence_output, temperature, output_weights, output_bias):
"""
Computes logits per token
Args:
sequence_output (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Also known as last_hidden_state. Sequence of hidden-states at the output of the last layer of the model.
temperature (`float`):
Temperature for the Bernoulli distribution.
output_weights (`torch.FloatTensor` of shape `(hidden_size,)`):
Weights of the linear layer for cell selection.
output_bias (`torch.FloatTensor` of shape `()`):
Bias of the linear layer for cell selection
Returns:
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Logits per token.
"""
logits = (torch.einsum("bsj,j->bs", sequence_output, output_weights) + output_bias) / temperature
return logits
def _calculate_aggregate_mask(answer, pooled_output, cell_selection_preference, labels, aggregation_classifier):
"""
Finds examples where the model should select cells with no aggregation.
Returns a mask that determines for which examples should the model select answers directly from the table, without
any aggregation function. If the answer is a piece of text the case is unambiguous as aggregation functions only
apply to numbers. If the answer is a number but does not appear in the table then we must use some aggregation
case. The ambiguous case is when the answer is a number that also appears in the table. In this case we use the
aggregation function probabilities predicted by the model to decide whether to select or aggregate. The threshold
for this is a hyperparameter *cell_selection_preference*
Args:
answer (`torch.FloatTensor` of shape `(batch_size, )`):
Answer for every example in the batch. Nan if there is no scalar answer.
pooled_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
Output of the pooler (BertPooler) on top of the encoder layer.
cell_selection_preference (`float`):
Preference for cell selection in ambiguous cases.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Labels per token. aggregation_classifier (`torch.nn.Linear`): Aggregation head
Returns:
aggregate_mask (`torch.FloatTensor` of shape `(batch_size,)`): A mask set to 1 for examples that should use
aggregation functions.
"""
# torch.FloatTensor(batch_size,)
aggregate_mask_init = torch.logical_not(torch.isnan(answer)).type(torch.FloatTensor).to(answer.device)
logits_aggregation = aggregation_classifier(pooled_output)
dist_aggregation = torch.distributions.categorical.Categorical(logits=logits_aggregation)
# Index 0 corresponds to "no aggregation".
aggregation_ops_total_mass = torch.sum(dist_aggregation.probs[:, 1:], dim=1)
# Cell selection examples according to current model.
is_pred_cell_selection = aggregation_ops_total_mass <= cell_selection_preference
# Examples with non-empty cell selection supervision.
is_cell_supervision_available = torch.sum(labels, dim=1) > 0
# torch.where is not equivalent to tf.where (in tensorflow 1)
# hence the added .view on the condition to match the shape of the first tensor
aggregate_mask = torch.where(
torch.logical_and(is_pred_cell_selection, is_cell_supervision_available).view(aggregate_mask_init.size()),
torch.zeros_like(aggregate_mask_init, dtype=torch.float32),
aggregate_mask_init,
)
aggregate_mask = aggregate_mask.detach()
return aggregate_mask
def _calculate_aggregation_loss_known(
logits_aggregation, aggregate_mask, aggregation_labels, use_answer_as_supervision, num_aggregation_labels
):
"""
Calculates aggregation loss when its type is known during training.
In the weakly supervised setting, the only known information is that for cell selection examples, "no aggregation"
should be predicted. For other examples (those that require aggregation), no loss is accumulated. In the setting
where aggregation type is always known, standard cross entropy loss is accumulated for all examples
Args:
logits_aggregation (`torch.FloatTensor` of shape `(batch_size, num_aggregation_labels)`):
Logits per aggregation operation.
aggregate_mask (`torch.FloatTensor` of shape `(batch_size, )`):
A mask set to 1 for examples that should use aggregation functions.
aggregation_labels (`torch.LongTensor` of shape `(batch_size, )`):
Aggregation function id for every example in the batch.
use_answer_as_supervision (`bool`, *optional*):
Whether to use the answer as the only supervision for aggregation examples.
num_aggregation_labels (`int`, *optional*, defaults to 0):
The number of aggregation operators to predict.
Returns:
aggregation_loss_known (`torch.FloatTensor` of shape `(batch_size,)`): Aggregation loss (when its type is known
during training) per example.
"""
if use_answer_as_supervision:
# Prepare "no aggregation" targets for cell selection examples.
target_aggregation = torch.zeros_like(aggregate_mask, dtype=torch.long)
else:
# Use aggregation supervision as the target.
target_aggregation = aggregation_labels
one_hot_labels = nn.functional.one_hot(target_aggregation, num_classes=num_aggregation_labels).type(torch.float32)
log_probs = nn.functional.log_softmax(logits_aggregation, dim=-1)
# torch.FloatTensor[batch_size]
per_example_aggregation_intermediate = -torch.sum(one_hot_labels * log_probs, dim=-1)
if use_answer_as_supervision:
# Accumulate loss only for examples requiring cell selection
# (no aggregation).
return per_example_aggregation_intermediate * (1 - aggregate_mask)
else:
return per_example_aggregation_intermediate
def _calculate_aggregation_loss_unknown(logits_aggregation, aggregate_mask):
"""
Calculates aggregation loss in the case of answer supervision.
Args:
logits_aggregation (`torch.FloatTensor` of shape `(batch_size, num_aggregation_labels)`):
Logits per aggregation operation.
aggregate_mask (`torch.FloatTensor` of shape `(batch_size, )`):
A mask set to 1 for examples that should use aggregation functions
Returns:
aggregation_loss_unknown (`torch.FloatTensor` of shape `(batch_size,)`): Aggregation loss (in case of answer
supervision) per example.
"""
dist_aggregation = torch.distributions.categorical.Categorical(logits=logits_aggregation)
# Index 0 corresponds to "no aggregation".
aggregation_ops_total_mass = torch.sum(dist_aggregation.probs[:, 1:], dim=1)
# Predict some aggregation in case of an answer that needs aggregation.
# This increases the probability of all aggregation functions, in a way
# similar to MML, but without considering whether the function gives the
# correct answer.
return -torch.log(aggregation_ops_total_mass) * aggregate_mask
def _calculate_aggregation_loss(
logits_aggregation,
aggregate_mask,
aggregation_labels,
use_answer_as_supervision,
num_aggregation_labels,
aggregation_loss_weight,
):
"""
Calculates the aggregation loss per example.
Args:
logits_aggregation (`torch.FloatTensor` of shape `(batch_size, num_aggregation_labels)`):
Logits per aggregation operation.
aggregate_mask (`torch.FloatTensor` of shape `(batch_size, )`):
A mask set to 1 for examples that should use aggregation functions.
aggregation_labels (`torch.LongTensor` of shape `(batch_size, )`):
Aggregation function id for every example in the batch.
use_answer_as_supervision (`bool`, *optional*):
Whether to use the answer as the only supervision for aggregation examples.
num_aggregation_labels (`int`, *optional*, defaults to 0):
The number of aggregation operators to predict.
aggregation_loss_weight (`float`, *optional*, defaults to 1.0):
Importance weight for the aggregation loss.
Returns:
aggregation_loss (`torch.FloatTensor` of shape `(batch_size,)`): Aggregation loss per example.
"""
per_example_aggregation_loss = _calculate_aggregation_loss_known(
logits_aggregation, aggregate_mask, aggregation_labels, use_answer_as_supervision, num_aggregation_labels
)
if use_answer_as_supervision:
# Add aggregation loss for numeric answers that need aggregation.
per_example_aggregation_loss += _calculate_aggregation_loss_unknown(logits_aggregation, aggregate_mask)
return aggregation_loss_weight * per_example_aggregation_loss
def _calculate_expected_result(
dist_per_cell, numeric_values, numeric_values_scale, input_mask_float, logits_aggregation, config
):
"""
Calculates the expected result given cell and aggregation probabilities.
Args:
dist_per_cell (`torch.distributions.Bernoulli`):
Cell selection distribution for each cell.
numeric_values (`torch.FloatTensor` of shape `(batch_size, seq_length)`):
Numeric values of every token. Nan for tokens which are not numeric values.
numeric_values_scale (`torch.FloatTensor` of shape `(batch_size, seq_length)`):
Scale of the numeric values of every token.
input_mask_float (`torch.FloatTensor` of shape `(batch_size, seq_length)`):
Mask for the table, without question tokens and table headers.
logits_aggregation (`torch.FloatTensor` of shape `(batch_size, num_aggregation_labels)`):
Logits per aggregation operation.
config ([`TapasConfig`]):
Model configuration class with all the hyperparameters of the model
Returns:
expected_result (`torch.FloatTensor` of shape `(batch_size,)`): The expected result per example.
"""
if config.use_gumbel_for_cells:
gumbel_dist = torch.distributions.RelaxedBernoulli(
# The token logits where already divided by the temperature and used for
# computing cell selection errors so we need to multiply it again here
temperature=config.temperature,
logits=dist_per_cell.logits * config.temperature,
)
scaled_probability_per_cell = gumbel_dist.sample()
else:
scaled_probability_per_cell = dist_per_cell.probs
# <float32>[batch_size, seq_length]
scaled_probability_per_cell = (scaled_probability_per_cell / numeric_values_scale) * input_mask_float
count_result = torch.sum(scaled_probability_per_cell, dim=1)
numeric_values_masked = torch.where(
torch.isnan(numeric_values), torch.zeros_like(numeric_values), numeric_values
) # Mask non-numeric table values to zero.
sum_result = torch.sum(scaled_probability_per_cell * numeric_values_masked, dim=1)
avg_approximation = config.average_approximation_function
if avg_approximation == AverageApproximationFunction.RATIO:
average_result = sum_result / (count_result + EPSILON_ZERO_DIVISION)
elif avg_approximation == AverageApproximationFunction.FIRST_ORDER:
# The sum of all probabilities except that correspond to other cells
# Ex here stands for expectation, more explicitly the expectation of the sum of N-1 Bernoulli random variables plus
# the constant 1, which is computed as adding all N expected values and subtracting the extra one. It corresponds to X_c
# in Appendix D of the original TAPAS paper which is trying to approximate the average of a random set.
ex = torch.sum(scaled_probability_per_cell, dim=1, keepdim=True) - scaled_probability_per_cell + 1
average_result = torch.sum(numeric_values_masked * scaled_probability_per_cell / ex, dim=1)
elif avg_approximation == AverageApproximationFunction.SECOND_ORDER:
# The sum of all probabilities except that correspond to other cells
ex = torch.sum(scaled_probability_per_cell, dim=1, keepdim=True) - scaled_probability_per_cell + 1
pointwise_var = scaled_probability_per_cell * (1 - scaled_probability_per_cell)
var = torch.sum(pointwise_var, dim=1, keepdim=True) - pointwise_var
multiplier = (var / torch.square(ex) + 1) / ex
average_result = torch.sum(numeric_values_masked * scaled_probability_per_cell * multiplier, dim=1)
else:
raise ValueError(f"Invalid average_approximation_function: {config.average_approximation_function}")
if config.use_gumbel_for_aggregation:
gumbel_dist = torch.distributions.RelaxedOneHotCategorical(
config.aggregation_temperature, logits=logits_aggregation[:, 1:]
)
# <float32>[batch_size, num_aggregation_labels - 1]
aggregation_op_only_probs = gumbel_dist.sample()
else:
# <float32>[batch_size, num_aggregation_labels - 1]
aggregation_op_only_probs = nn.functional.softmax(
logits_aggregation[:, 1:] / config.aggregation_temperature, dim=-1
)
all_results = torch.cat(
[
torch.unsqueeze(sum_result, dim=1),
torch.unsqueeze(average_result, dim=1),
torch.unsqueeze(count_result, dim=1),
],
dim=1,
)
expected_result = torch.sum(all_results * aggregation_op_only_probs, dim=1)
return expected_result
# PyTorch does not currently support Huber loss with custom delta so we define it ourself
def huber_loss(input, target, delta: float = 1.0):
errors = torch.abs(input - target) # shape (batch_size,)
return torch.where(errors < delta, 0.5 * errors**2, errors * delta - (0.5 * delta**2))
def _calculate_regression_loss(
answer,
aggregate_mask,
dist_per_cell,
numeric_values,
numeric_values_scale,
input_mask_float,
logits_aggregation,
config,
):
"""
Calculates the regression loss per example.
Args:
answer (`torch.FloatTensor` of shape `(batch_size,)`):
Answer for every example in the batch. Nan if there is no scalar answer.
aggregate_mask (`torch.FloatTensor` of shape `(batch_size,)`):
A mask set to 1 for examples that should use aggregation functions.
dist_per_cell (`torch.distributions.Bernoulli`):
Cell selection distribution for each cell.
numeric_values (`torch.FloatTensor` of shape `(batch_size, seq_length)`):
Numeric values of every token. Nan for tokens which are not numeric values.
numeric_values_scale (`torch.FloatTensor` of shape `(batch_size, seq_length)`):
Scale of the numeric values of every token.
input_mask_float (`torch.FloatTensor` of shape `(batch_size, seq_length)`):
Mask for the table, without question tokens and table headers.
logits_aggregation (`torch.FloatTensor` of shape `(batch_size, num_aggregation_labels)`):
Logits per aggregation operation.
config ([`TapasConfig`]):
Model configuration class with all the parameters of the model
Returns:
per_example_answer_loss_scaled (`torch.FloatTensor` of shape `(batch_size,)`): Scales answer loss for each
example in the batch. large_answer_loss_mask (`torch.FloatTensor` of shape `(batch_size,)`): A mask which is 1
for examples for which their answer loss is larger than the answer_loss_cutoff.
"""
# float32 (batch_size,)
expected_result = _calculate_expected_result(
dist_per_cell, numeric_values, numeric_values_scale, input_mask_float, logits_aggregation, config
)
# float32 (batch_size,)
answer_masked = torch.where(torch.isnan(answer), torch.zeros_like(answer), answer)
if config.use_normalized_answer_loss:
normalizer = (torch.max(torch.abs(expected_result), torch.abs(answer_masked)) + EPSILON_ZERO_DIVISION).detach()
normalized_answer_masked = answer_masked / normalizer
normalized_expected_result = expected_result / normalizer
per_example_answer_loss = huber_loss(
normalized_expected_result * aggregate_mask, normalized_answer_masked * aggregate_mask
)
else:
per_example_answer_loss = huber_loss(
expected_result * aggregate_mask, answer_masked * aggregate_mask, delta=config.huber_loss_delta
)
if config.answer_loss_cutoff is None:
large_answer_loss_mask = torch.ones_like(per_example_answer_loss, dtype=torch.float32)
else:
large_answer_loss_mask = torch.where(
per_example_answer_loss > config.answer_loss_cutoff,
torch.zeros_like(per_example_answer_loss, dtype=torch.float32),
torch.ones_like(per_example_answer_loss, dtype=torch.float32),
)
per_example_answer_loss_scaled = config.answer_loss_importance * (per_example_answer_loss * aggregate_mask)
return per_example_answer_loss_scaled, large_answer_loss_mask
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/tapas/configuration_tapas.py | # coding=utf-8
# Copyright 2020 Google Research and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
TAPAS configuration. Based on the BERT configuration with added parameters.
Hyperparameters are taken from run_task_main.py and hparam_utils.py of the original implementation. URLS:
- https://github.com/google-research/tapas/blob/master/tapas/run_task_main.py
- https://github.com/google-research/tapas/blob/master/tapas/utils/hparam_utils.py
"""
from ...configuration_utils import PretrainedConfig
TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"
),
"google/tapas-base-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"
),
"google/tapas-base-finetuned-tabfact": (
"https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"
),
}
class TapasConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`TapasModel`]. It is used to instantiate a TAPAS
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the TAPAS
[google/tapas-base-finetuned-sqa](https://huggingface.co/google/tapas-base-finetuned-sqa) architecture.
Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Hyperparameters additional to BERT are taken from run_task_main.py and hparam_utils.py of the original
implementation. Original implementation available at https://github.com/google-research/tapas/tree/master.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the TAPAS model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`TapasModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"swish"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_sizes (`List[int]`, *optional*, defaults to `[3, 256, 256, 2, 256, 256, 10]`):
The vocabulary sizes of the `token_type_ids` passed when calling [`TapasModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
positive_label_weight (`float`, *optional*, defaults to 10.0):
Weight for positive labels.
num_aggregation_labels (`int`, *optional*, defaults to 0):
The number of aggregation operators to predict.
aggregation_loss_weight (`float`, *optional*, defaults to 1.0):
Importance weight for the aggregation loss.
use_answer_as_supervision (`bool`, *optional*):
Whether to use the answer as the only supervision for aggregation examples.
answer_loss_importance (`float`, *optional*, defaults to 1.0):
Importance weight for the regression loss.
use_normalized_answer_loss (`bool`, *optional*, defaults to `False`):
Whether to normalize the answer loss by the maximum of the predicted and expected value.
huber_loss_delta (`float`, *optional*):
Delta parameter used to calculate the regression loss.
temperature (`float`, *optional*, defaults to 1.0):
Value used to control (OR change) the skewness of cell logits probabilities.
aggregation_temperature (`float`, *optional*, defaults to 1.0):
Scales aggregation logits to control the skewness of probabilities.
use_gumbel_for_cells (`bool`, *optional*, defaults to `False`):
Whether to apply Gumbel-Softmax to cell selection.
use_gumbel_for_aggregation (`bool`, *optional*, defaults to `False`):
Whether to apply Gumbel-Softmax to aggregation selection.
average_approximation_function (`string`, *optional*, defaults to `"ratio"`):
Method to calculate the expected average of cells in the weak supervision case. One of `"ratio"`,
`"first_order"` or `"second_order"`.
cell_selection_preference (`float`, *optional*):
Preference for cell selection in ambiguous cases. Only applicable in case of weak supervision for
aggregation (WTQ, WikiSQL). If the total mass of the aggregation probabilities (excluding the "NONE"
operator) is higher than this hyperparameter, then aggregation is predicted for an example.
answer_loss_cutoff (`float`, *optional*):
Ignore examples with answer loss larger than cutoff.
max_num_rows (`int`, *optional*, defaults to 64):
Maximum number of rows.
max_num_columns (`int`, *optional*, defaults to 32):
Maximum number of columns.
average_logits_per_cell (`bool`, *optional*, defaults to `False`):
Whether to average logits per cell.
select_one_column (`bool`, *optional*, defaults to `True`):
Whether to constrain the model to only select cells from a single column.
allow_empty_column_selection (`bool`, *optional*, defaults to `False`):
Whether to allow not to select any column.
init_cell_selection_weights_to_zero (`bool`, *optional*, defaults to `False`):
Whether to initialize cell selection weights to 0 so that the initial probabilities are 50%.
reset_position_index_per_cell (`bool`, *optional*, defaults to `True`):
Whether to restart position indexes at every cell (i.e. use relative position embeddings).
disable_per_token_loss (`bool`, *optional*, defaults to `False`):
Whether to disable any (strong or weak) supervision on cells.
aggregation_labels (`Dict[int, label]`, *optional*):
The aggregation labels used to aggregate the results. For example, the WTQ models have the following
aggregation labels: `{0: "NONE", 1: "SUM", 2: "AVERAGE", 3: "COUNT"}`
no_aggregation_label_index (`int`, *optional*):
If the aggregation labels are defined and one of these labels represents "No aggregation", this should be
set to its index. For example, the WTQ models have the "NONE" aggregation label at index 0, so that value
should be set to 0 for these models.
Example:
```python
>>> from transformers import TapasModel, TapasConfig
>>> # Initializing a default (SQA) Tapas configuration
>>> configuration = TapasConfig()
>>> # Initializing a model from the configuration
>>> model = TapasModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "tapas"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=1024,
type_vocab_sizes=[3, 256, 256, 2, 256, 256, 10],
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
positive_label_weight=10.0,
num_aggregation_labels=0,
aggregation_loss_weight=1.0,
use_answer_as_supervision=None,
answer_loss_importance=1.0,
use_normalized_answer_loss=False,
huber_loss_delta=None,
temperature=1.0,
aggregation_temperature=1.0,
use_gumbel_for_cells=False,
use_gumbel_for_aggregation=False,
average_approximation_function="ratio",
cell_selection_preference=None,
answer_loss_cutoff=None,
max_num_rows=64,
max_num_columns=32,
average_logits_per_cell=False,
select_one_column=True,
allow_empty_column_selection=False,
init_cell_selection_weights_to_zero=False,
reset_position_index_per_cell=True,
disable_per_token_loss=False,
aggregation_labels=None,
no_aggregation_label_index=None,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_sizes = type_vocab_sizes
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
# Fine-tuning task hyperparameters
self.positive_label_weight = positive_label_weight
self.num_aggregation_labels = num_aggregation_labels
self.aggregation_loss_weight = aggregation_loss_weight
self.use_answer_as_supervision = use_answer_as_supervision
self.answer_loss_importance = answer_loss_importance
self.use_normalized_answer_loss = use_normalized_answer_loss
self.huber_loss_delta = huber_loss_delta
self.temperature = temperature
self.aggregation_temperature = aggregation_temperature
self.use_gumbel_for_cells = use_gumbel_for_cells
self.use_gumbel_for_aggregation = use_gumbel_for_aggregation
self.average_approximation_function = average_approximation_function
self.cell_selection_preference = cell_selection_preference
self.answer_loss_cutoff = answer_loss_cutoff
self.max_num_rows = max_num_rows
self.max_num_columns = max_num_columns
self.average_logits_per_cell = average_logits_per_cell
self.select_one_column = select_one_column
self.allow_empty_column_selection = allow_empty_column_selection
self.init_cell_selection_weights_to_zero = init_cell_selection_weights_to_zero
self.reset_position_index_per_cell = reset_position_index_per_cell
self.disable_per_token_loss = disable_per_token_loss
# Aggregation hyperparameters
self.aggregation_labels = aggregation_labels
self.no_aggregation_label_index = no_aggregation_label_index
if isinstance(self.aggregation_labels, dict):
self.aggregation_labels = {int(k): v for k, v in aggregation_labels.items()}
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/tapas/modeling_tf_tapas.py | # coding=utf-8
# Copyright 2021 Google Research and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TF 2.0 TAPAS model."""
from __future__ import annotations
import enum
import math
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutputWithPastAndCrossAttentions,
TFBaseModelOutputWithPooling,
TFMaskedLMOutput,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import (
TFMaskedLanguageModelingLoss,
TFModelInputType,
TFPreTrainedModel,
TFSequenceClassificationLoss,
get_initializer,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_tensorflow_probability_available,
logging,
replace_return_docstrings,
requires_backends,
)
from .configuration_tapas import TapasConfig
logger = logging.get_logger(__name__)
# soft dependency
if is_tensorflow_probability_available():
try:
import tensorflow_probability as tfp
# On the first call, check whether a compatible version of TensorFlow is installed
# TensorFlow Probability depends on a recent stable release of TensorFlow
n = tfp.distributions.Normal(loc=0.0, scale=1.0)
except ImportError:
logger.error(
"TAPAS models are not usable since `tensorflow_probability` can't be loaded. "
"It seems you have `tensorflow_probability` installed with the wrong tensorflow version. "
"Please try to reinstall it following the instructions here: https://github.com/tensorflow/probability."
)
_CONFIG_FOR_DOC = "TapasConfig"
_CHECKPOINT_FOR_DOC = "google/tapas-base"
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST = [
# large models
"google/tapas-large",
"google/tapas-large-finetuned-sqa",
"google/tapas-large-finetuned-wtq",
"google/tapas-large-finetuned-wikisql-supervised",
"google/tapas-large-finetuned-tabfact",
# base models
"google/tapas-base",
"google/tapas-base-finetuned-sqa",
"google/tapas-base-finetuned-wtq",
"google/tapas-base-finetuned-wikisql-supervised",
"google/tapas-base-finetuned-tabfact",
# small models
"google/tapas-small",
"google/tapas-small-finetuned-sqa",
"google/tapas-small-finetuned-wtq",
"google/tapas-small-finetuned-wikisql-supervised",
"google/tapas-small-finetuned-tabfact",
# mini models
"google/tapas-mini",
"google/tapas-mini-finetuned-sqa",
"google/tapas-mini-finetuned-wtq",
"google/tapas-mini-finetuned-wikisql-supervised",
"google/tapas-mini-finetuned-tabfact",
# tiny models
"google/tapas-tiny",
"google/tapas-tiny-finetuned-sqa",
"google/tapas-tiny-finetuned-wtq",
"google/tapas-tiny-finetuned-wikisql-supervised",
"google/tapas-tiny-finetuned-tabfact",
# See all TAPAS models at https://huggingface.co/models?filter=tapas
]
EPSILON_ZERO_DIVISION = 1e-10
CLOSE_ENOUGH_TO_LOG_ZERO = -10000.0
@dataclass
class TFTableQuestionAnsweringOutput(ModelOutput):
"""
Output type of [`TFTapasForQuestionAnswering`].
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` (and possibly `answer`, `aggregation_labels`, `numeric_values` and `numeric_values_scale` are provided)):
Total loss as the sum of the hierarchical cell selection log-likelihood loss and (optionally) the
semi-supervised regression loss and (optionally) supervised loss for aggregations.
logits (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Prediction scores of the cell selection head, for every token.
logits_aggregation (`tf.Tensor`, *optional*, of shape `(batch_size, num_aggregation_labels)`):
Prediction scores of the aggregation head, for every aggregation operator.
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus
the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
"""
loss: tf.Tensor | None = None
logits: tf.Tensor = None
logits_aggregation: tf.Tensor | None = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None
class TFTapasEmbeddings(tf.keras.layers.Layer):
"""
Construct the embeddings from word, position and token_type embeddings. Same as BertEmbeddings but with a number of
additional token type embeddings to encode tabular structure.
"""
def __init__(self, config: TapasConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.number_of_token_type_embeddings = len(config.type_vocab_sizes)
self.reset_position_index_per_cell = config.reset_position_index_per_cell
self.hidden_size = config.hidden_size
self.max_position_embeddings = config.max_position_embeddings
self.initializer_range = config.initializer_range
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
def build(self, input_shape=None):
with tf.name_scope("word_embeddings"):
self.weight = self.add_weight(
name="weight",
shape=[self.config.vocab_size, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_scope("position_embeddings"):
self.position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_position_embeddings, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
for i, type_vocab_size in enumerate(self.config.type_vocab_sizes):
with tf.name_scope(f"token_type_embeddings_{i}"):
setattr(
self,
f"token_type_embeddings_{i}",
self.add_weight(
name="embeddings",
shape=[type_vocab_size, self.hidden_size],
initializer=get_initializer(self.initializer_range),
),
)
if self.built:
return
self.built = True
if getattr(self, "LayerNorm", None) is not None:
with tf.name_scope(self.LayerNorm.name):
self.LayerNorm.build([None, None, self.config.hidden_size])
def call(
self,
input_ids: tf.Tensor = None,
position_ids: tf.Tensor = None,
token_type_ids: tf.Tensor = None,
inputs_embeds: tf.Tensor = None,
training: bool = False,
) -> tf.Tensor:
"""
Applies embedding based on inputs tensor.
Returns:
final_embeddings (`tf.Tensor`): output embedding tensor.
"""
assert not (input_ids is None and inputs_embeds is None)
if input_ids is not None:
input_shape = shape_list(input_ids)
else:
input_shape = shape_list(inputs_embeds)[:-1]
seq_length = input_shape[1]
if token_type_ids is None:
token_type_ids = tf.fill(dims=input_shape + [self.number_of_token_type_embeddings], value=0)
if position_ids is None:
# create absolute position embeddings
position_ids = tf.expand_dims(tf.range(start=0, limit=seq_length), axis=0)
position_ids = tf.broadcast_to(position_ids, shape=input_shape)
# when self.config.reset_position_index_per_cell is set to True, create relative position embeddings
if self.reset_position_index_per_cell:
# shape (batch_size, seq_len)
col_index = IndexMap(token_type_ids[:, :, 1], self.config.type_vocab_sizes[1], batch_dims=1)
# shape (batch_size, seq_len)
row_index = IndexMap(token_type_ids[:, :, 2], self.config.type_vocab_sizes[2], batch_dims=1)
# shape (batch_size, seq_len)
full_index = ProductIndexMap(col_index, row_index)
# shape (max_rows * max_columns,). First absolute position for every cell
first_position_per_segment = reduce_min(position_ids, full_index)[0]
# ? shape (batch_size, seq_len). First absolute position of the cell for every token
first_position = gather(first_position_per_segment, full_index)
# shape (1, seq_len)
position = tf.expand_dims(tf.range(start=0, limit=seq_length), axis=0)
position_ids = tf.math.minimum(self.max_position_embeddings - 1, position - first_position)
if input_ids is not None:
check_embeddings_within_bounds(input_ids, self.config.vocab_size)
inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
position_embeddings = tf.gather(self.position_embeddings, indices=position_ids)
final_embeddings = inputs_embeds + position_embeddings
for i in range(self.number_of_token_type_embeddings):
name = f"token_type_embeddings_{i}"
final_embeddings += tf.gather(params=getattr(self, name), indices=token_type_ids[:, :, i])
final_embeddings = self.LayerNorm(inputs=final_embeddings)
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
return final_embeddings
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->Tapas
class TFTapasSelfAttention(tf.keras.layers.Layer):
def __init__(self, config: TapasConfig, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number "
f"of attention heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
self.query = tf.keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
)
self.key = tf.keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
)
self.value = tf.keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
)
self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
self.is_decoder = config.is_decoder
self.config = config
def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
# Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
# Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
return tf.transpose(tensor, perm=[0, 2, 1, 3])
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor,
encoder_attention_mask: tf.Tensor,
past_key_value: Tuple[tf.Tensor],
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
batch_size = shape_list(hidden_states)[0]
mixed_query_layer = self.query(inputs=hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(inputs=encoder_hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=encoder_hidden_states), batch_size)
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size)
key_layer = tf.concat([past_key_value[0], key_layer], axis=2)
value_layer = tf.concat([past_key_value[1], value_layer], axis=2)
else:
key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
if self.is_decoder:
# if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
attention_scores = tf.divide(attention_scores, dk)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in TFTapasModel call() function)
attention_scores = tf.add(attention_scores, attention_mask)
# Normalize the attention scores to probabilities.
attention_probs = stable_softmax(logits=attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(inputs=attention_probs, training=training)
# Mask heads if we want to
if head_mask is not None:
attention_probs = tf.multiply(attention_probs, head_mask)
attention_output = tf.matmul(attention_probs, value_layer)
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
# (batch_size, seq_len_q, all_head_size)
attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "query", None) is not None:
with tf.name_scope(self.query.name):
self.query.build([None, None, self.config.hidden_size])
if getattr(self, "key", None) is not None:
with tf.name_scope(self.key.name):
self.key.build([None, None, self.config.hidden_size])
if getattr(self, "value", None) is not None:
with tf.name_scope(self.value.name):
self.value.build([None, None, self.config.hidden_size])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->Tapas
class TFTapasSelfOutput(tf.keras.layers.Layer):
def __init__(self, config: TapasConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.config = config
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
if getattr(self, "LayerNorm", None) is not None:
with tf.name_scope(self.LayerNorm.name):
self.LayerNorm.build([None, None, self.config.hidden_size])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->Tapas
class TFTapasAttention(tf.keras.layers.Layer):
def __init__(self, config: TapasConfig, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFTapasSelfAttention(config, name="self")
self.dense_output = TFTapasSelfOutput(config, name="output")
def prune_heads(self, heads):
raise NotImplementedError
def call(
self,
input_tensor: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor,
encoder_attention_mask: tf.Tensor,
past_key_value: Tuple[tf.Tensor],
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
self_outputs = self.self_attention(
hidden_states=input_tensor,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_value=past_key_value,
output_attentions=output_attentions,
training=training,
)
attention_output = self.dense_output(
hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
)
# add attentions (possibly with past_key_value) if we output them
outputs = (attention_output,) + self_outputs[1:]
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self_attention", None) is not None:
with tf.name_scope(self.self_attention.name):
self.self_attention.build(None)
if getattr(self, "dense_output", None) is not None:
with tf.name_scope(self.dense_output.name):
self.dense_output.build(None)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->Tapas
class TFTapasIntermediate(tf.keras.layers.Layer):
def __init__(self, config: TapasConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = get_tf_activation(config.hidden_act)
else:
self.intermediate_act_fn = config.hidden_act
self.config = config
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->Tapas
class TFTapasOutput(tf.keras.layers.Layer):
def __init__(self, config: TapasConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.config = config
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.intermediate_size])
if getattr(self, "LayerNorm", None) is not None:
with tf.name_scope(self.LayerNorm.name):
self.LayerNorm.build([None, None, self.config.hidden_size])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->Tapas
class TFTapasLayer(tf.keras.layers.Layer):
def __init__(self, config: TapasConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFTapasAttention(config, name="attention")
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
self.crossattention = TFTapasAttention(config, name="crossattention")
self.intermediate = TFTapasIntermediate(config, name="intermediate")
self.bert_output = TFTapasOutput(config, name="output")
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor | None,
encoder_attention_mask: tf.Tensor | None,
past_key_value: Tuple[tf.Tensor] | None,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
input_tensor=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=self_attn_past_key_value,
output_attentions=output_attentions,
training=training,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and encoder_hidden_states is not None:
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
" by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
input_tensor=attention_output,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
training=training,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
intermediate_output = self.intermediate(hidden_states=attention_output)
layer_output = self.bert_output(
hidden_states=intermediate_output, input_tensor=attention_output, training=training
)
outputs = (layer_output,) + outputs # add attentions if we output them
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "attention", None) is not None:
with tf.name_scope(self.attention.name):
self.attention.build(None)
if getattr(self, "intermediate", None) is not None:
with tf.name_scope(self.intermediate.name):
self.intermediate.build(None)
if getattr(self, "bert_output", None) is not None:
with tf.name_scope(self.bert_output.name):
self.bert_output.build(None)
if getattr(self, "crossattention", None) is not None:
with tf.name_scope(self.crossattention.name):
self.crossattention.build(None)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->Tapas
class TFTapasEncoder(tf.keras.layers.Layer):
def __init__(self, config: TapasConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.layer = [TFTapasLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor | None,
encoder_attention_mask: tf.Tensor | None,
past_key_values: Tuple[Tuple[tf.Tensor]] | None,
use_cache: Optional[bool],
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
past_key_value = past_key_values[i] if past_key_values is not None else None
layer_outputs = layer_module(
hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask[i],
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_value=past_key_value,
output_attentions=output_attentions,
training=training,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if self.config.add_cross_attention and encoder_hidden_states is not None:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v for v in [hidden_states, all_hidden_states, all_attentions, all_cross_attentions] if v is not None
)
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_attentions,
cross_attentions=all_cross_attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layer", None) is not None:
for layer in self.layer:
with tf.name_scope(layer.name):
layer.build(None)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->Tapas
class TFTapasPooler(tf.keras.layers.Layer):
def __init__(self, config: TapasConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
name="dense",
)
self.config = config
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(inputs=first_token_tensor)
return pooled_output
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->Tapas
class TFTapasPredictionHeadTransform(tf.keras.layers.Layer):
def __init__(self, config: TapasConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name="dense",
)
if isinstance(config.hidden_act, str):
self.transform_act_fn = get_tf_activation(config.hidden_act)
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.config = config
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(inputs=hidden_states)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
if getattr(self, "LayerNorm", None) is not None:
with tf.name_scope(self.LayerNorm.name):
self.LayerNorm.build([None, None, self.config.hidden_size])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->Tapas
class TFTapasLMPredictionHead(tf.keras.layers.Layer):
def __init__(self, config: TapasConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.transform = TFTapasPredictionHeadTransform(config, name="transform")
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.input_embeddings = input_embeddings
def build(self, input_shape=None):
self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
if self.built:
return
self.built = True
if getattr(self, "transform", None) is not None:
with tf.name_scope(self.transform.name):
self.transform.build(None)
def get_output_embeddings(self) -> tf.keras.layers.Layer:
return self.input_embeddings
def set_output_embeddings(self, value: tf.Variable):
self.input_embeddings.weight = value
self.input_embeddings.vocab_size = shape_list(value)[0]
def get_bias(self) -> Dict[str, tf.Variable]:
return {"bias": self.bias}
def set_bias(self, value: tf.Variable):
self.bias = value["bias"]
self.config.vocab_size = shape_list(value["bias"])[0]
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.transform(hidden_states=hidden_states)
seq_length = shape_list(hidden_states)[1]
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size])
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
return hidden_states
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->Tapas
class TFTapasMLMHead(tf.keras.layers.Layer):
def __init__(self, config: TapasConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.predictions = TFTapasLMPredictionHead(config, input_embeddings, name="predictions")
def call(self, sequence_output: tf.Tensor) -> tf.Tensor:
prediction_scores = self.predictions(hidden_states=sequence_output)
return prediction_scores
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "predictions", None) is not None:
with tf.name_scope(self.predictions.name):
self.predictions.build(None)
@keras_serializable
class TFTapasMainLayer(tf.keras.layers.Layer):
config_class = TapasConfig
def __init__(self, config: TapasConfig, add_pooling_layer: bool = True, **kwargs):
requires_backends(self, "tensorflow_probability")
super().__init__(**kwargs)
self.config = config
self.embeddings = TFTapasEmbeddings(config, name="embeddings")
self.encoder = TFTapasEncoder(config, name="encoder")
self.pooler = TFTapasPooler(config, name="pooler") if add_pooling_layer else None
def get_input_embeddings(self) -> tf.keras.layers.Layer:
return self.embeddings
def set_input_embeddings(self, value: tf.Variable):
self.embeddings.weight = value
self.embeddings.vocab_size = shape_list(value)[0]
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
raise NotImplementedError
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if attention_mask is None:
attention_mask = tf.fill(dims=input_shape, value=1)
if token_type_ids is None:
token_type_ids = tf.fill(dims=input_shape + [len(self.config.type_vocab_sizes)], value=0)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
training=training,
)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
extended_attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1]))
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype)
one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.config.num_hidden_layers
encoder_outputs = self.encoder(
hidden_states=embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None
if not return_dict:
return (
sequence_output,
pooled_output,
) + encoder_outputs[1:]
return TFBaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embeddings", None) is not None:
with tf.name_scope(self.embeddings.name):
self.embeddings.build(None)
if getattr(self, "encoder", None) is not None:
with tf.name_scope(self.encoder.name):
self.encoder.build(None)
if getattr(self, "pooler", None) is not None:
with tf.name_scope(self.pooler.name):
self.pooler.build(None)
class TFTapasPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = TapasConfig
base_model_prefix = "tapas"
@property
def input_signature(self):
return {
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None), tf.float32, name="attention_mask"),
"token_type_ids": tf.TensorSpec((None, None, 7), tf.int32, name="token_type_ids"),
}
TAPAS_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
<Tip>
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Note that when creating models and layers with
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
</Tip>
Parameters:
config ([`TapasConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
TAPAS_INPUTS_DOCSTRING = r"""
Args:
input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
[`PreTrainedTokenizer.encode`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`np.ndarray` or `tf.Tensor` of shape `({0}, 7)`, *optional*):
Token indices that encode tabular structure. Indices can be obtained using [`AutoTokenizer`]. See this
class for more info.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. If
`reset_position_index_per_cell` of [`TapasConfig`] is set to `True`, relative position embeddings will be
used. Selected in the range `[0, config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`np.ndarray` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False``):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@add_start_docstrings(
"The bare Tapas Model transformer outputting raw hidden-states without any specific head on top.",
TAPAS_START_DOCSTRING,
)
class TFTapasModel(TFTapasPreTrainedModel):
def __init__(self, config: TapasConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.tapas = TFTapasMainLayer(config, name="tapas")
@unpack_inputs
@add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
r"""
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, TapasModel
>>> import pandas as pd
>>> tokenizer = AutoTokenizer.from_pretrained("google/tapas-base")
>>> model = TapasModel.from_pretrained("google/tapas-base")
>>> data = {
... "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
... "Age": ["56", "45", "59"],
... "Number of movies": ["87", "53", "69"],
... }
>>> table = pd.DataFrame.from_dict(data)
>>> queries = ["How many movies has George Clooney played in?", "How old is Brad Pitt?"]
>>> inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="tf")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
```"""
outputs = self.tapas(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "tapas", None) is not None:
with tf.name_scope(self.tapas.name):
self.tapas.build(None)
@add_start_docstrings("""Tapas Model with a `language modeling` head on top.""", TAPAS_START_DOCSTRING)
class TFTapasForMaskedLM(TFTapasPreTrainedModel, TFMaskedLanguageModelingLoss):
def __init__(self, config: TapasConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
if config.is_decoder:
logger.warning(
"If you want to use `TFTapasForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
self.tapas = TFTapasMainLayer(config, add_pooling_layer=False, name="tapas")
self.lm_head = TFTapasMLMHead(config, input_embeddings=self.tapas.embeddings, name="cls")
def get_lm_head(self) -> tf.keras.layers.Layer:
return self.lm_head.predictions
@unpack_inputs
@add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, TapasForMaskedLM
>>> import pandas as pd
>>> tokenizer = AutoTokenizer.from_pretrained("google/tapas-base")
>>> model = TapasForMaskedLM.from_pretrained("google/tapas-base")
>>> data = {
... "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
... "Age": ["56", "45", "59"],
... "Number of movies": ["87", "53", "69"],
... }
>>> table = pd.DataFrame.from_dict(data)
>>> inputs = tokenizer(
... table=table, queries="How many [MASK] has George [MASK] played in?", return_tensors="tf"
... )
>>> labels = tokenizer(
... table=table, queries="How many movies has George Clooney played in?", return_tensors="tf"
... )["input_ids"]
>>> outputs = model(**inputs, labels=labels)
>>> logits = outputs.logits
```"""
outputs = self.tapas(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=prediction_scores)
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFMaskedLMOutput(
loss=loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "tapas", None) is not None:
with tf.name_scope(self.tapas.name):
self.tapas.build(None)
if getattr(self, "lm_head", None) is not None:
with tf.name_scope(self.lm_head.name):
self.lm_head.build(None)
class TFTapasComputeTokenLogits(tf.keras.layers.Layer):
def __init__(self, config: TapasConfig, **kwargs):
super().__init__(**kwargs)
self.temperature = config.temperature
# cell selection heads
with tf.name_scope("output"):
self.output_weights = self.add_weight(
name="output_weights",
shape=(config.hidden_size,),
dtype=tf.float32,
trainable=True,
initializer=tf.zeros_initializer()
if config.init_cell_selection_weights_to_zero
else tf.keras.initializers.TruncatedNormal(stddev=config.initializer_range),
)
self.output_bias = self.add_weight(
name="output_bias", shape=(), trainable=True, initializer=tf.zeros_initializer()
)
def call(self, sequence_output: tf.Tensor) -> tf.Tensor:
"""
Computes logits per token
Args:
sequence_output (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Also known as last_hidden_state. Sequence of hidden-states at the output of the last layer of the
model.
Returns:
logits (`tf.Tensor` of shape `(batch_size, sequence_length)`): Logits per token.
"""
logits = (tf.einsum("bsj,j->bs", sequence_output, self.output_weights) + self.output_bias) / self.temperature
return logits
class TFTapasComputeColumnLogits(tf.keras.layers.Layer):
def __init__(self, config: TapasConfig, **kwargs):
super().__init__(**kwargs)
with tf.name_scope("column_output"):
self.column_output_weights = self.add_weight(
name="column_output_weights",
shape=[config.hidden_size],
dtype=tf.float32,
trainable=True,
initializer=tf.zeros_initializer()
if config.init_cell_selection_weights_to_zero
else tf.keras.initializers.TruncatedNormal(stddev=config.initializer_range),
)
self.column_output_bias = self.add_weight(
name="column_output_bias", shape=(), trainable=True, initializer=tf.zeros_initializer()
)
def call(self, sequence_output, cell_index, cell_mask, allow_empty_column_selection) -> tf.Tensor:
"""
Computes the column logits.
Args:
sequence_output (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Also known as last_hidden_state. Sequence of hidden-states at the output of the last layer of the
model.
cell_index (`ProductIndexMap`):
Index that groups tokens into cells.
cell_mask (`tf.Tensor` of shape `(batch_size, max_num_rows * max_num_cols)`):
Mask for cells that exist in the table (i.e. that are not padding).
allow_empty_column_selection (`bool`):
Whether to allow not to select any column
Returns:
column_logits (`tf.Tensor`of shape `(batch_size, max_num_cols)`): Tensor containing the column logits for
every example in the batch.
"""
# First, compute the token logits (batch_size, seq_len) - without temperature
token_logits = tf.einsum("bsj,j->bs", sequence_output, self.column_output_weights) + self.column_output_bias
# Next, average the logits per cell (batch_size, max_num_cols*max_num_rows)
cell_logits, cell_logits_index = reduce_mean(token_logits, cell_index)
# Finally, average the logits per column (batch_size, max_num_cols)
column_index = cell_index.project_inner(cell_logits_index)
column_logits, out_index = reduce_sum(cell_logits * cell_mask, column_index)
cell_count, _ = reduce_sum(cell_mask, column_index)
column_logits /= cell_count + EPSILON_ZERO_DIVISION
# Mask columns that do not appear in the example.
is_padding = tf.logical_and(cell_count < 0.5, tf.not_equal(out_index.indices, 0))
column_logits += CLOSE_ENOUGH_TO_LOG_ZERO * tf.cast(is_padding, tf.float32)
if not allow_empty_column_selection:
column_logits += CLOSE_ENOUGH_TO_LOG_ZERO * tf.cast(tf.equal(out_index.indices, 0), tf.float32)
return column_logits
@add_start_docstrings(
"""
Tapas Model with a cell selection head and optional aggregation head on top for question-answering tasks on tables
(linear layers on top of the hidden-states output to compute `logits` and optional `logits_aggregation`), e.g. for
SQA, WTQ or WikiSQL-supervised tasks.
""",
TAPAS_START_DOCSTRING,
)
class TFTapasForQuestionAnswering(TFTapasPreTrainedModel):
def __init__(self, config: TapasConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
# base model
self.tapas = TFTapasMainLayer(config, name="tapas")
# dropout
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.compute_token_logits = TFTapasComputeTokenLogits(config, name="compute_token_logits")
self.compute_column_logits = TFTapasComputeColumnLogits(config, name="compute_column_logits")
if config.num_aggregation_labels > 0:
self.aggregation_classifier = tf.keras.layers.Dense(
config.num_aggregation_labels,
kernel_initializer=get_initializer(config.initializer_range),
name="aggregation_classifier",
)
self.config = config
@unpack_inputs
@add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFTableQuestionAnsweringOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
table_mask: np.ndarray | tf.Tensor | None = None,
aggregation_labels: np.ndarray | tf.Tensor | None = None,
float_answer: np.ndarray | tf.Tensor | None = None,
numeric_values: np.ndarray | tf.Tensor | None = None,
numeric_values_scale: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFTableQuestionAnsweringOutput, Tuple[tf.Tensor]]:
r"""
table_mask (`tf.Tensor` of shape `(batch_size, seq_length)`, *optional*):
Mask for the table. Indicates which tokens belong to the table (1). Question tokens, table headers and
padding are 0.
labels (`tf.Tensor` of shape `(batch_size, seq_length)`, *optional*):
Labels per token for computing the hierarchical cell selection loss. This encodes the positions of the
answer appearing in the table. Can be obtained using [`AutoTokenizer`].
- 1 for tokens that are **part of the answer**,
- 0 for tokens that are **not part of the answer**.
aggregation_labels (`tf.Tensor` of shape `(batch_size, )`, *optional*):
Aggregation function index for every example in the batch for computing the aggregation loss. Indices
should be in `[0, ..., config.num_aggregation_labels - 1]`. Only required in case of strong supervision for
aggregation (WikiSQL-supervised).
float_answer (`tf.Tensor` of shape `(batch_size, )`, *optional*):
Float answer for every example in the batch. Set to *float('nan')* for cell selection questions. Only
required in case of weak supervision (WTQ) to calculate the aggregate mask and regression loss.
numeric_values (`tf.Tensor` of shape `(batch_size, seq_length)`, *optional*):
Numeric values of every token, NaN for tokens which are not numeric values. Can be obtained using
[`AutoTokenizer`]. Only required in case of weak supervision for aggregation (WTQ) to calculate the
regression loss.
numeric_values_scale (`tf.Tensor` of shape `(batch_size, seq_length)`, *optional*):
Scale of the numeric values of every token. Can be obtained using [`AutoTokenizer`]. Only required in case
of weak supervision for aggregation (WTQ) to calculate the regression loss.
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, TapasForQuestionAnswering
>>> import pandas as pd
>>> tokenizer = AutoTokenizer.from_pretrained("google/tapas-base-finetuned-wtq")
>>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq")
>>> data = {
... "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
... "Age": ["56", "45", "59"],
... "Number of movies": ["87", "53", "69"],
... }
>>> table = pd.DataFrame.from_dict(data)
>>> queries = ["How many movies has George Clooney played in?", "How old is Brad Pitt?"]
>>> inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="tf")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> logits_aggregation = outputs.logits_aggregation
```"""
outputs = self.tapas(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
pooled_output = outputs[1]
sequence_output = self.dropout(sequence_output)
if input_ids is not None:
input_shape = shape_list(input_ids)
else:
input_shape = shape_list(inputs_embeds)[:-1]
# Construct indices for the table.
if token_type_ids is None:
token_type_ids = tf.fill(input_shape + [len(self.config.type_vocab_sizes)], 0)
token_types = [
"segment_ids",
"column_ids",
"row_ids",
"prev_labels",
"column_ranks",
"inv_column_ranks",
"numeric_relations",
]
row_ids = token_type_ids[:, :, token_types.index("row_ids")]
column_ids = token_type_ids[:, :, token_types.index("column_ids")]
# Construct indices for the table.
row_index = IndexMap(
indices=tf.minimum(tf.cast(row_ids, tf.int32), self.config.max_num_rows - 1),
num_segments=self.config.max_num_rows,
batch_dims=1,
)
col_index = IndexMap(
indices=tf.minimum(tf.cast(column_ids, tf.int32), self.config.max_num_columns - 1),
num_segments=self.config.max_num_columns,
batch_dims=1,
)
cell_index = ProductIndexMap(row_index, col_index)
# Masks.
input_shape = shape_list(input_ids) if input_ids is not None else shape_list(inputs_embeds)[:-1]
if attention_mask is None:
attention_mask = tf.ones(input_shape)
# Table cells only, without question tokens and table headers.
if table_mask is None:
table_mask = tf.where(row_ids > 0, tf.ones_like(row_ids), tf.zeros_like(row_ids))
# <float32>[batch_size, seq_length]
input_mask_float = tf.cast(attention_mask, tf.float32)
table_mask_float = tf.cast(table_mask, tf.float32)
# Mask for cells that exist in the table (i.e. that are not padding).
cell_mask, _ = reduce_mean(input_mask_float, cell_index)
# Compute logits per token. These are used to select individual cells.
logits = self.compute_token_logits(sequence_output)
# Compute logits per column. These are used to select a column.
column_logits = None
if self.config.select_one_column:
column_logits = self.compute_column_logits(
sequence_output, cell_index, cell_mask, self.config.allow_empty_column_selection
)
# Aggregate logits.
logits_aggregation = None
if self.config.num_aggregation_labels > 0:
logits_aggregation = self.aggregation_classifier(pooled_output)
# Total loss calculation
total_loss = tf.zeros(shape=(1,), dtype=tf.float32)
calculate_loss = False
if labels is not None:
calculate_loss = True
is_supervised = not self.config.num_aggregation_labels > 0 or not self.config.use_answer_as_supervision
# Semi-supervised cell selection in case of no aggregation:
# If the answer (the denotation) appears directly in the table we might
# select the answer without applying any aggregation function. There are
# some ambiguous cases, see utils._calculate_aggregate_mask for more info.
# `aggregate_mask` is 1 for examples where we chose to aggregate and 0
# for examples where we chose to select the answer directly.
# `labels` encodes the positions of the answer appearing in the table.
if is_supervised:
aggregate_mask = None
else:
if float_answer is not None:
assert (
shape_list(labels)[0] == shape_list(float_answer)[0]
), "Make sure the answers are a FloatTensor of shape (batch_size,)"
# <float32>[batch_size]
aggregate_mask = _calculate_aggregate_mask(
float_answer,
pooled_output,
self.config.cell_selection_preference,
labels,
self.aggregation_classifier,
)
else:
aggregate_mask = None
raise ValueError("You have to specify float answers in order to calculate the aggregate mask")
# Cell selection log-likelihood
if self.config.average_logits_per_cell:
logits_per_cell, _ = reduce_mean(logits, cell_index)
logits = gather(logits_per_cell, cell_index)
dist_per_token = tfp.distributions.Bernoulli(logits=logits)
# Compute cell selection loss per example.
selection_loss_per_example = None
if not self.config.select_one_column:
weight = tf.where(
labels == 0,
tf.ones_like(labels, dtype=tf.float32),
self.config.positive_label_weight * tf.ones_like(labels, dtype=tf.float32),
)
selection_loss_per_token = -dist_per_token.log_prob(labels) * weight
selection_loss_per_example = tf.reduce_sum(selection_loss_per_token * input_mask_float, axis=1) / (
tf.reduce_sum(input_mask_float, axis=1) + EPSILON_ZERO_DIVISION
)
else:
selection_loss_per_example, logits = _single_column_cell_selection_loss(
logits, column_logits, labels, cell_index, col_index, cell_mask
)
dist_per_token = tfp.distributions.Bernoulli(logits=logits)
# Supervised cell selection
if self.config.disable_per_token_loss:
pass
elif is_supervised:
total_loss += tf.reduce_mean(selection_loss_per_example)
else:
# For the not supervised case, do not assign loss for cell selection
total_loss += tf.reduce_mean(selection_loss_per_example * (1.0 - aggregate_mask))
# Semi-supervised regression loss and supervised loss for aggregations
if self.config.num_aggregation_labels > 0:
if is_supervised:
# Note that `aggregate_mask` is None if the setting is supervised.
if aggregation_labels is not None:
assert (
shape_list(labels)[0] == shape_list(aggregation_labels)[0]
), "Make sure the aggregation labels are a LongTensor of shape (batch_size,)"
per_example_additional_loss = _calculate_aggregation_loss(
logits_aggregation,
aggregate_mask,
aggregation_labels,
self.config.use_answer_as_supervision,
self.config.num_aggregation_labels,
self.config.aggregation_loss_weight,
)
else:
raise ValueError(
"You have to specify aggregation labels in order to calculate the aggregation loss"
)
else:
aggregation_labels = tf.zeros(shape_list(labels)[0], dtype=tf.int32)
per_example_additional_loss = _calculate_aggregation_loss(
logits_aggregation,
aggregate_mask,
aggregation_labels,
self.config.use_answer_as_supervision,
self.config.num_aggregation_labels,
self.config.aggregation_loss_weight,
)
if self.config.use_answer_as_supervision:
if numeric_values is not None and numeric_values_scale is not None:
assert shape_list(numeric_values) == shape_list(numeric_values_scale)
# Add regression loss for numeric answers which require aggregation.
answer_loss, large_answer_loss_mask = _calculate_regression_loss(
float_answer,
aggregate_mask,
dist_per_token,
numeric_values,
numeric_values_scale,
table_mask_float,
logits_aggregation,
self.config,
)
per_example_additional_loss += answer_loss
# Zero loss for examples with answer_loss > cutoff.
per_example_additional_loss *= large_answer_loss_mask
else:
raise ValueError(
"You have to specify numeric values and numeric values scale in order to calculate the"
" regression loss"
)
total_loss += tf.reduce_mean(per_example_additional_loss)
else:
# if no label ids are provided, set them to zeros in order to properly compute logits
labels = tf.zeros_like(logits)
_, logits = _single_column_cell_selection_loss(
logits, column_logits, labels, cell_index, col_index, cell_mask
)
if not return_dict:
output = (logits, logits_aggregation) + outputs[2:]
return ((total_loss,) + output) if calculate_loss else output
return TFTableQuestionAnsweringOutput(
loss=total_loss if calculate_loss else None,
logits=logits,
logits_aggregation=logits_aggregation,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "tapas", None) is not None:
with tf.name_scope(self.tapas.name):
self.tapas.build(None)
if getattr(self, "compute_token_logits", None) is not None:
with tf.name_scope(self.compute_token_logits.name):
self.compute_token_logits.build(None)
if getattr(self, "compute_column_logits", None) is not None:
with tf.name_scope(self.compute_column_logits.name):
self.compute_column_logits.build(None)
if getattr(self, "aggregation_classifier", None) is not None:
with tf.name_scope(self.aggregation_classifier.name):
self.aggregation_classifier.build([None, None, self.config.hidden_size])
@add_start_docstrings(
"""
Tapas Model with a sequence classification head on top (a linear layer on top of the pooled output), e.g. for table
entailment tasks, such as TabFact (Chen et al., 2020).
""",
TAPAS_START_DOCSTRING,
)
class TFTapasForSequenceClassification(TFTapasPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: TapasConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.tapas = TFTapasMainLayer(config, name="tapas")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob, name="dropout")
self.classifier = tf.keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
self.config = config
@unpack_inputs
@add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy). Note: this is called
"classification_class_index" in the original implementation.
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, TapasForSequenceClassification
>>> import tensorflow as tf
>>> import pandas as pd
>>> tokenizer = AutoTokenizer.from_pretrained("google/tapas-base-finetuned-tabfact")
>>> model = TapasForSequenceClassification.from_pretrained("google/tapas-base-finetuned-tabfact")
>>> data = {
... "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
... "Age": ["56", "45", "59"],
... "Number of movies": ["87", "53", "69"],
... }
>>> table = pd.DataFrame.from_dict(data)
>>> queries = [
... "There is only one actor who is 45 years old",
... "There are 3 actors which played in more than 60 movies",
... ]
>>> inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="tf")
>>> labels = tf.convert_to_tensor([1, 0]) # 1 means entailed, 0 means refuted
>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
```"""
outputs = self.tapas(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
pooled_output = outputs[1]
pooled_output = self.dropout(inputs=pooled_output, training=training)
logits = self.classifier(inputs=pooled_output)
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "tapas", None) is not None:
with tf.name_scope(self.tapas.name):
self.tapas.build(None)
if getattr(self, "dropout", None) is not None:
with tf.name_scope(self.dropout.name):
self.dropout.build(None)
if getattr(self, "classifier", None) is not None:
with tf.name_scope(self.classifier.name):
self.classifier.build([None, None, self.config.hidden_size])
""" TAPAS utilities."""
class AverageApproximationFunction(str, enum.Enum):
RATIO = "ratio"
FIRST_ORDER = "first_order"
SECOND_ORDER = "second_order"
# Beginning of everything related to segmented tensors
class IndexMap(object):
"""Index grouping entries within a tensor."""
def __init__(self, indices, num_segments, batch_dims=0):
"""
Creates an index.
Args:
indices: <int32> Tensor of indices, same shape as `values`.
num_segments: <int32> Scalar tensor, the number of segments. All elements
in a batched segmented tensor must have the same number of segments (although many segments can be empty).
batch_dims: Python integer, the number of batch dimensions. The first
`batch_dims` dimensions of a SegmentedTensor are treated as batch dimensions. Segments in different batch
elements are always distinct even if they have the same index.
"""
self.indices = tf.convert_to_tensor(indices)
self.num_segments = tf.convert_to_tensor(num_segments)
self.batch_dims = batch_dims
def batch_shape(self):
return tf.shape(self.indices)[: self.batch_dims]
class ProductIndexMap(IndexMap):
"""The product of two indices."""
def __init__(self, outer_index, inner_index):
"""
Combines indices i and j into pairs (i, j). The result is an index where each segment (i, j) is the
intersection of segments i and j. For example if the inputs represent table cells indexed by respectively rows
and columns the output will be a table indexed by (row, column) pairs, i.e. by cell. The implementation
combines indices {0, .., n - 1} and {0, .., m - 1} into {0, .., nm - 1}. The output has `num_segments` equal to
`outer_index.num_segements` * `inner_index.num_segments`.
Args:
outer_index: IndexMap.
inner_index: IndexMap, must have the same shape as `outer_index`.
"""
if outer_index.batch_dims != inner_index.batch_dims:
raise ValueError("outer_index.batch_dims and inner_index.batch_dims must be the same.")
super(ProductIndexMap, self).__init__(
indices=(
inner_index.indices
+ outer_index.indices * tf.cast(inner_index.num_segments, inner_index.indices.dtype)
),
num_segments=inner_index.num_segments * outer_index.num_segments,
batch_dims=inner_index.batch_dims,
)
self.outer_index = outer_index
self.inner_index = inner_index
def project_outer(self, index):
"""Projects an index with the same index set onto the outer components."""
return IndexMap(
indices=tf.math.floordiv(index.indices, self.inner_index.num_segments),
num_segments=self.outer_index.num_segments,
batch_dims=index.batch_dims,
)
def project_inner(self, index):
"""Projects an index with the same index set onto the inner components."""
return IndexMap(
indices=tf.math.floormod(index.indices, self.inner_index.num_segments),
num_segments=self.inner_index.num_segments,
batch_dims=index.batch_dims,
)
def gather(values, index, name="segmented_gather"):
"""
Gathers from `values` using the index map. For each element in the domain of the index map this operation looks up
a value for that index in `values`. Two elements from the same segment always get assigned the same value.
Args:
values: [B1, ..., Bn, num_segments, V1, ...] Tensor with segment values.
index: [B1, ..., Bn, I1, ..., Ik] IndexMap.
name: Name for the TensorFlow operation.
Returns:
[B1, ..., Bn, I1, ..., Ik, V1, ...] Tensor with the gathered values.
"""
return tf.gather(values, index.indices, batch_dims=index.batch_dims, name=name)
def flatten(index, name="segmented_flatten"):
"""
Flattens a batched index map to a 1d index map. This operation relabels the segments to keep batch elements
distinct. The k-th batch element will have indices shifted by `num_segments` * (k - 1). The result is a tensor with
`num_segments` multiplied by the number of elements in the batch.
Args:
index: IndexMap to flatten.
name: Name for the TensorFlow operation.
Returns:
The flattened IndexMap.
"""
batch_size = tf.reduce_prod(index.batch_shape())
offset = tf.range(batch_size) * index.num_segments
offset = tf.reshape(offset, index.batch_shape())
for _ in range(index.batch_dims, index.indices.shape.rank):
offset = tf.expand_dims(offset, -1)
indices = tf.cast(offset, index.indices.dtype) + index.indices
return IndexMap(indices=tf.reshape(indices, [-1]), num_segments=index.num_segments * batch_size, batch_dims=0)
def range_index_map(batch_shape, num_segments, name="range_index_map"):
"""
Constructs an index map equal to range(num_segments).
Args:
batch_shape (`tf.Tensor`):
Batch shape
num_segments (`int`):
Number of segments
name (`str`, *optional*, defaults to 'range_index_map'):
Name for the operation. Currently not used
Returns:
(`IndexMap`): IndexMap of shape batch_shape with elements equal to range(num_segments).
"""
batch_shape = tf.convert_to_tensor(batch_shape)
batch_shape.shape.assert_has_rank(1)
num_segments = tf.convert_to_tensor(num_segments)
num_segments.shape.assert_has_rank(0)
indices = tf.range(num_segments)
shape = tf.concat([tf.ones_like(batch_shape, dtype=tf.int32), tf.expand_dims(num_segments, axis=0)], axis=0)
indices = tf.reshape(indices, shape)
multiples = tf.concat([batch_shape, [1]], axis=0)
indices = tf.tile(indices, multiples)
return IndexMap(indices=indices, num_segments=num_segments, batch_dims=batch_shape.shape.as_list()[0])
def _segment_reduce(values, index, segment_reduce_fn, name):
"""
Applies a segment reduction segment-wise.
Args:
values (`tf.Tensor`):
Tensor with segment values.
index (`IndexMap`):
IndexMap.
segment_reduce_fn (`str`):
Name for the reduce operation. One of "sum", "mean", "max" or "min".
name (`str`):
Name for the operation. Currently not used
Returns:
(`IndexMap`): IndexMap of shape batch_shape with elements equal to range(num_segments).
"""
# Flatten the batch dimensions, as segments ops do not support batching.
# However if `values` has extra dimensions to the right keep them
# unflattened. Segmented ops support vector-valued operations.
flat_index = flatten(index)
vector_shape = tf.shape(values)[index.indices.shape.rank :]
flattened_shape = tf.concat([[-1], vector_shape], axis=0)
flat_values = tf.reshape(values, flattened_shape)
segment_means = segment_reduce_fn(
data=flat_values, segment_ids=flat_index.indices, num_segments=flat_index.num_segments
)
# Unflatten the values.
new_shape = tf.concat([index.batch_shape(), [index.num_segments], vector_shape], axis=0)
output_values = tf.reshape(segment_means, new_shape)
output_index = range_index_map(index.batch_shape(), index.num_segments)
return output_values, output_index
def reduce_mean(values, index, name="segmented_reduce_mean"):
"""
Averages a tensor over its segments. Outputs 0 for empty segments. This operations computes the mean over segments,
with support for:
- Batching using the first dimensions [B1, B2, ..., Bn]. Each element in a batch can have different indices.
- Vectorization using the last dimension [V1, V2, ...]. If they are present the output will be a mean of vectors
rather than scalars.
Only the middle dimensions [I1, ..., Ik] are reduced by the operation.
Args:
values: [B1, B2, ..., Bn, I1, .., Ik, V1, V2, ..] tensor of values to be
averaged.
index: IndexMap [B1, B2, ..., Bn, I1, .., Ik] index defining the segments.
name: Name for the TensorFlow ops.
Returns:
A pair (output_values, output_index) where `output_values` is a tensor of shape [B1, B2, ..., Bn, num_segments,
V1, V2, ..] and `index` is an IndexMap with shape [B1, B2, ..., Bn, num_segments].
"""
return _segment_reduce(values, index, tf.math.unsorted_segment_mean, name)
def reduce_sum(values, index, name="segmented_reduce_sum"):
"""
Sums a tensor over its segments. Outputs 0 for empty segments. This operations computes the sum over segments, with
support for:
- Batching using the first dimensions [B1, B2, ..., Bn]. Each element in a batch can have different indices.
- Vectorization using the last dimension [V1, V2, ...]. If they are present the output will be a sum of vectors
rather than scalars.
Only the middle dimensions [I1, ..., Ik] are reduced by the operation.
Args:
values: [B1, B2, ..., Bn, I1, .., Ik, V1, V2, ..] tensor of values to be
averaged.
index: IndexMap [B1, B2, ..., Bn, I1, .., Ik] index defining the segments.
name: Name for the TensorFlow ops.
Returns:
A pair (output_values, output_index) where `output_values` is a tensor of shape [B1, B2, ..., Bn, num_segments,
V1, V2, ..] and `index` is an IndexMap with shape [B1, B2, ..., Bn, num_segments].
"""
return _segment_reduce(values, index, tf.math.unsorted_segment_sum, name)
def reduce_max(values, index, name="segmented_reduce_max"):
"""
Computes the maximum over segments. This operations computes the maximum over segments, with support for:
- Batching using the first dimensions [B1, B2, ..., Bn]. Each element in a batch can have different indices.
- Vectorization using the last dimension [V1, V2, ...]. If they are present the output will be an element-wise
maximum of vectors rather than scalars.
Only the middle dimensions [I1, ..., Ik] are reduced by the operation.
Args:
values: [B1, B2, ..., Bn, I1, .., Ik, V1, V2, ..] tensor of values to be
averaged.
index: IndexMap [B1, B2, ..., Bn, I1, .., Ik] index defining the segments.
name: Name for the TensorFlow ops.
Returns:
A pair (output_values, output_index) where `output_values` is a tensor of shape [B1, B2, ..., Bn, num_segments,
V1, V2, ..] and `index` is an IndexMap with shape [B1, B2, ..., Bn, num_segments].
"""
return _segment_reduce(values, index, tf.math.unsorted_segment_max, name)
def reduce_min(values, index, name="segmented_reduce_min"):
"""Computes the minimum over segments."""
return _segment_reduce(values, index, tf.math.unsorted_segment_min, name)
def _single_column_cell_selection_loss(token_logits, column_logits, labels, cell_index, col_index, cell_mask):
"""
Computes the loss for cell selection constrained to a single column. The loss is a hierarchical log-likelihood. The
model first predicts a column and then selects cells within that column (conditioned on the column). Cells outside
the selected column are never selected.
Args:
token_logits (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Tensor containing the logits per token.
column_logits (`tf.Tensor` of shape `(batch_size, max_num_cols)`):
Tensor containing the logits per column.
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Labels per token.
cell_index (`ProductIndexMap`):
Index that groups tokens into cells.
col_index (`IndexMap`):
Index that groups tokens into columns.
cell_mask (`tf.Tensor` of shape `(batch_size, max_num_rows * max_num_cols)`):
Mask for cells that exist in the table (i.e. that are not padding).
Returns:
selection_loss_per_example (`tf.Tensor` of shape `(batch_size,)`): Loss for each example. logits (`tf.Tensor`
of shape `(batch_size, sequence_length)`): New logits which are only allowed to select cells in a single
column. Logits outside of the most likely column according to *column_logits* will be set to a very low value
(such that the probabilities are 0).
"""
# First find the column we should select. We use the column with maximum
# number of selected cells.
labels_per_column, _ = reduce_sum(tf.cast(labels, tf.float32), col_index)
column_label = tf.argmax(labels_per_column, axis=-1, output_type=tf.int32)
# Check if there are no selected cells in the column. In that case the model
# should predict the special column id 0, which means "select nothing".
no_cell_selected = tf.equal(tf.reduce_max(labels_per_column, axis=-1), 0)
column_label = tf.where(no_cell_selected, tf.zeros_like(column_label), column_label)
column_dist = tfp.distributions.Categorical(logits=column_logits)
column_loss_per_example = -column_dist.log_prob(column_label)
# Reduce the labels and logits to per-cell from per-token.
logits_per_cell, _ = reduce_mean(token_logits, cell_index)
labels_per_cell, labels_index = reduce_max(tf.cast(labels, tf.int32), cell_index)
# Mask for the selected column.
column_id_for_cells = cell_index.project_inner(labels_index).indices
column_mask = tf.cast(tf.equal(column_id_for_cells, tf.expand_dims(column_label, axis=1)), tf.float32)
# Compute the log-likelihood for cells, but only for the selected column.
cell_dist = tfp.distributions.Bernoulli(logits=logits_per_cell)
cell_log_prob = cell_dist.log_prob(labels_per_cell)
cell_loss = -tf.reduce_sum(cell_log_prob * column_mask * cell_mask, axis=1)
# We need to normalize the loss by the number of cells in the column.
cell_loss /= tf.reduce_sum(column_mask * cell_mask, axis=1) + EPSILON_ZERO_DIVISION
selection_loss_per_example = column_loss_per_example
selection_loss_per_example += tf.where(no_cell_selected, tf.zeros_like(selection_loss_per_example), cell_loss)
# Set the probs outside the selected column (selected by the *model*)
# to 0. This ensures backwards compatibility with models that select
# cells from multiple columns.
selected_column_id = tf.argmax(column_logits, axis=-1, output_type=tf.int32)
selected_column_mask = tf.cast(
tf.equal(column_id_for_cells, tf.expand_dims(selected_column_id, axis=-1)), tf.float32
)
# Never select cells with the special column id 0.
selected_column_mask = tf.where(
tf.equal(column_id_for_cells, 0), tf.zeros_like(selected_column_mask), selected_column_mask
)
logits_per_cell += CLOSE_ENOUGH_TO_LOG_ZERO * (1.0 - cell_mask * selected_column_mask)
logits = gather(logits_per_cell, cell_index)
return selection_loss_per_example, logits
def _calculate_aggregate_mask(answer, pooled_output, cell_selection_preference, labels, aggregation_classifier):
"""
Finds examples where the model should select cells with no aggregation.
Returns a mask that determines for which examples should the model select answers directly from the table, without
any aggregation function. If the answer is a piece of text the case is unambiguous as aggregation functions only
apply to numbers. If the answer is a number but does not appear in the table then we must use some aggregation
case. The ambiguous case is when the answer is a number that also appears in the table. In this case we use the
aggregation function probabilities predicted by the model to decide whether to select or aggregate. The threshold
for this is a hyperparameter *cell_selection_preference*
Args:
answer (`tf.Tensor` of shape `(batch_size, )`):
Answer for every example in the batch. Nan if there is no scalar answer.
pooled_output (`tf.Tensor` of shape `(batch_size, hidden_size)`):
Output of the pooler (BertPooler) on top of the encoder layer.
cell_selection_preference (`float`):
Preference for cell selection in ambiguous cases.
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Labels per token. aggregation_classifier (`torch.nn.Linear`): Aggregation head
Returns:
aggregate_mask (`tf.Tensor` of shape `(batch_size,)`): A mask set to 1 for examples that should use aggregation
functions.
"""
# tf.Tensor(batch_size,)
aggregate_mask_init = tf.cast(tf.logical_not(tf.math.is_nan(answer)), tf.float32)
logits_aggregation = aggregation_classifier(pooled_output)
dist_aggregation = tfp.distributions.Categorical(logits=logits_aggregation)
# Index 0 corresponds to "no aggregation".
aggregation_ops_total_mass = tf.reduce_sum(dist_aggregation.probs_parameter()[:, 1:], axis=1)
# Cell selection examples according to current model.
is_pred_cell_selection = aggregation_ops_total_mass <= cell_selection_preference
# Examples with non-empty cell selection supervision.
is_cell_supervision_available = tf.reduce_sum(labels, axis=1) > 0
aggregate_mask = tf.where(
tf.logical_and(is_pred_cell_selection, is_cell_supervision_available),
tf.zeros_like(aggregate_mask_init, dtype=tf.float32),
aggregate_mask_init,
)
aggregate_mask = tf.stop_gradient(aggregate_mask)
return aggregate_mask
def _calculate_aggregation_loss_known(
logits_aggregation, aggregate_mask, aggregation_labels, use_answer_as_supervision, num_aggregation_labels
):
"""
Calculates aggregation loss when its type is known during training.
In the weakly supervised setting, the only known information is that for cell selection examples, "no aggregation"
should be predicted. For other examples (those that require aggregation), no loss is accumulated. In the setting
where aggregation type is always known, standard cross entropy loss is accumulated for all examples
Args:
logits_aggregation (`tf.Tensor` of shape `(batch_size, num_aggregation_labels)`):
Logits per aggregation operation.
aggregate_mask (`tf.Tensor` of shape `(batch_size, )`):
A mask set to 1 for examples that should use aggregation functions.
aggregation_labels (`tf.Tensor` of shape `(batch_size, )`):
Aggregation function id for every example in the batch.
use_answer_as_supervision (`bool`, *optional*):
Whether to use the answer as the only supervision for aggregation examples.
num_aggregation_labels (`int`, *optional*, defaults to 0):
The number of aggregation operators to predict.
Returns:
aggregation_loss_known (`tf.Tensor` of shape `(batch_size,)`): Aggregation loss (when its type is known during
training) per example.
"""
if use_answer_as_supervision:
# Prepare "no aggregation" targets for cell selection examples.
target_aggregation = tf.zeros_like(aggregate_mask, dtype=tf.int32)
else:
# Use aggregation supervision as the target.
target_aggregation = aggregation_labels
one_hot_labels = tf.one_hot(target_aggregation, depth=num_aggregation_labels, dtype=tf.float32)
log_probs = tf.nn.log_softmax(logits_aggregation, axis=-1)
# <float32>[batch_size]
per_example_aggregation_intermediate = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
if use_answer_as_supervision:
# Accumulate loss only for examples requiring cell selection
# (no aggregation).
return per_example_aggregation_intermediate * (1 - aggregate_mask)
else:
return per_example_aggregation_intermediate
def _calculate_aggregation_loss_unknown(logits_aggregation, aggregate_mask):
"""
Calculates aggregation loss in the case of answer supervision.
Args:
logits_aggregation (`tf.Tensor` of shape `(batch_size, num_aggregation_labels)`):
Logits per aggregation operation.
aggregate_mask (`tf.Tensor` of shape `(batch_size, )`):
A mask set to 1 for examples that should use aggregation functions
Returns:
aggregation_loss_unknown (`tf.Tensor` of shape `(batch_size,)`): Aggregation loss (in case of answer
supervision) per example.
"""
dist_aggregation = tfp.distributions.Categorical(logits=logits_aggregation)
# Index 0 corresponds to "no aggregation".
aggregation_ops_total_mass = tf.reduce_sum(dist_aggregation.probs_parameter()[:, 1:], axis=1)
# Predict some aggregation in case of an answer that needs aggregation.
# This increases the probability of all aggregation functions, in a way
# similar to MML, but without considering whether the function gives the
# correct answer.
return -tf.math.log(aggregation_ops_total_mass) * aggregate_mask
def _calculate_aggregation_loss(
logits_aggregation,
aggregate_mask,
aggregation_labels,
use_answer_as_supervision,
num_aggregation_labels,
aggregation_loss_weight,
):
"""
Calculates the aggregation loss per example.
Args:
logits_aggregation (`tf.Tensor` of shape `(batch_size, num_aggregation_labels)`):
Logits per aggregation operation.
aggregate_mask (`tf.Tensor` of shape `(batch_size, )`):
A mask set to 1 for examples that should use aggregation functions.
aggregation_labels (`tf.Tensor` of shape `(batch_size, )`):
Aggregation function id for every example in the batch.
use_answer_as_supervision (`bool`, *optional*):
Whether to use the answer as the only supervision for aggregation examples.
num_aggregation_labels (`int`, *optional*, defaults to 0):
The number of aggregation operators to predict.
aggregation_loss_weight (`float`, *optional*, defaults to 1.0):
Importance weight for the aggregation loss.
Returns:
aggregation_loss (`tf.Tensor` of shape `(batch_size,)`): Aggregation loss per example.
"""
per_example_aggregation_loss = _calculate_aggregation_loss_known(
logits_aggregation, aggregate_mask, aggregation_labels, use_answer_as_supervision, num_aggregation_labels
)
if use_answer_as_supervision:
# Add aggregation loss for numeric answers that need aggregation.
per_example_aggregation_loss += _calculate_aggregation_loss_unknown(logits_aggregation, aggregate_mask)
return aggregation_loss_weight * per_example_aggregation_loss
def _calculate_expected_result(
dist_per_cell, numeric_values, numeric_values_scale, input_mask_float, logits_aggregation, config
):
"""
Calculates the expected result given cell and aggregation probabilities.
Args:
dist_per_cell (`tfp.distributions.Bernoulli`):
Cell selection distribution for each cell.
numeric_values (`tf.Tensor` of shape `(batch_size, seq_length)`):
Numeric values of every token. Nan for tokens which are not numeric values.
numeric_values_scale (`tf.Tensor` of shape `(batch_size, seq_length)`):
Scale of the numeric values of every token.
input_mask_float (`tf.Tensor` of shape `(batch_size, seq_length)`):
Mask for the table, without question tokens and table headers.
logits_aggregation (`tf.Tensor` of shape `(batch_size, num_aggregation_labels)`):
Logits per aggregation operation.
config ([`TapasConfig`]):
Model configuration class with all the hyperparameters of the model
Returns:
expected_result (`tf.Tensor` of shape `(batch_size,)`): The expected result per example.
"""
if config.use_gumbel_for_cells:
gumbel_dist = tfp.distributions.RelaxedBernoulli(
# The token logits where already divided by the temperature and used for
# computing cell selection errors so we need to multiply it again here
config.temperature,
logits=dist_per_cell.logits_parameter() * config.temperature,
)
scaled_probability_per_cell = gumbel_dist.sample()
else:
scaled_probability_per_cell = dist_per_cell.probs_parameter()
# <float32>[batch_size, seq_length]
scaled_probability_per_cell = (scaled_probability_per_cell / numeric_values_scale) * input_mask_float
count_result = tf.reduce_sum(scaled_probability_per_cell, axis=1)
numeric_values_masked = tf.where(
tf.math.is_nan(numeric_values), tf.zeros_like(numeric_values), numeric_values
) # Mask non-numeric table values to zero.
sum_result = tf.reduce_sum(scaled_probability_per_cell * numeric_values_masked, axis=1)
avg_approximation = config.average_approximation_function
if avg_approximation == AverageApproximationFunction.RATIO:
average_result = sum_result / (count_result + EPSILON_ZERO_DIVISION)
elif avg_approximation == AverageApproximationFunction.FIRST_ORDER:
# The sum of all probabilities exept that correspond to other cells
ex = tf.reduce_sum(scaled_probability_per_cell, axis=1, keepdims=True) - scaled_probability_per_cell + 1
average_result = tf.reduce_sum(numeric_values_masked * scaled_probability_per_cell / ex, axis=1)
elif avg_approximation == AverageApproximationFunction.SECOND_ORDER:
# The sum of all probabilities exept that correspond to other cells
ex = tf.reduce_sum(scaled_probability_per_cell, axis=1, keepdims=True) - scaled_probability_per_cell + 1
pointwise_var = scaled_probability_per_cell * (1 - scaled_probability_per_cell)
var = tf.reduce_sum(pointwise_var, axis=1, keepdims=True) - pointwise_var
multiplier = (var / tf.math.square(ex) + 1) / ex
average_result = tf.reduce_sum(numeric_values_masked * scaled_probability_per_cell * multiplier, axis=1)
else:
raise ValueError("Invalid average_approximation_function: %s", config.average_approximation_function)
if config.use_gumbel_for_aggregation:
gumbel_dist = tfp.distributions.RelaxedOneHotCategorical(
config.aggregation_temperature, logits=logits_aggregation[:, 1:]
)
# <float32>[batch_size, num_aggregation_labels - 1]
aggregation_op_only_probs = gumbel_dist.sample()
else:
# <float32>[batch_size, num_aggregation_labels - 1]
aggregation_op_only_probs = stable_softmax(logits_aggregation[:, 1:] / config.aggregation_temperature, axis=-1)
all_results = tf.concat(
[
tf.expand_dims(sum_result, axis=1),
tf.expand_dims(average_result, axis=1),
tf.expand_dims(count_result, axis=1),
],
axis=1,
)
expected_result = tf.reduce_sum(all_results * aggregation_op_only_probs, axis=1)
return expected_result
def _calculate_regression_loss(
answer,
aggregate_mask,
dist_per_cell,
numeric_values,
numeric_values_scale,
input_mask_float,
logits_aggregation,
config,
):
"""
Calculates the regression loss per example.
Args:
answer (`tf.Tensor` of shape `(batch_size,)`):
Answer for every example in the batch. Nan if there is no scalar answer.
aggregate_mask (`tf.Tensor` of shape `(batch_size,)`):
A mask set to 1 for examples that should use aggregation functions.
dist_per_cell (`torch.distributions.Bernoulli`):
Cell selection distribution for each cell.
numeric_values (`tf.Tensor` of shape `(batch_size, seq_length)`):
Numeric values of every token. Nan for tokens which are not numeric values.
numeric_values_scale (`tf.Tensor` of shape `(batch_size, seq_length)`):
Scale of the numeric values of every token.
input_mask_float (`tf.Tensor` of shape `(batch_size, seq_length)`):
Mask for the table, without question tokens and table headers.
logits_aggregation (`tf.Tensor` of shape `(batch_size, num_aggregation_labels)`):
Logits per aggregation operation.
config ([`TapasConfig`]):
Model configuration class with all the parameters of the model
Returns:
per_example_answer_loss_scaled (`tf.Tensor` of shape `(batch_size,)`): Scales answer loss for each example in
the batch. large_answer_loss_mask (`tf.Tensor` of shape `(batch_size,)`): A mask which is 1 for examples for
which their answer loss is larger than the answer_loss_cutoff.
"""
# float32 (batch_size,)
expected_result = _calculate_expected_result(
dist_per_cell, numeric_values, numeric_values_scale, input_mask_float, logits_aggregation, config
)
# <float32>[batch_size]
answer_masked = tf.where(tf.math.is_nan(answer), tf.zeros_like(answer), answer)
if config.use_normalized_answer_loss:
normalizer = tf.stop_gradient(
tf.math.maximum(tf.math.abs(expected_result), tf.math.abs(answer_masked)) + EPSILON_ZERO_DIVISION
)
normalized_answer_masked = answer_masked / normalizer
normalized_expected_result = expected_result / normalizer
per_example_answer_loss = tf.compat.v1.losses.huber_loss(
normalized_answer_masked * aggregate_mask,
normalized_expected_result * aggregate_mask,
delta=tf.cast(1.0, tf.float32),
reduction=tf.losses.Reduction.NONE,
)
else:
per_example_answer_loss = tf.compat.v1.losses.huber_loss(
answer_masked * aggregate_mask,
expected_result * aggregate_mask,
delta=tf.cast(config.huber_loss_delta, tf.float32),
reduction=tf.losses.Reduction.NONE,
)
if config.answer_loss_cutoff is None:
large_answer_loss_mask = tf.ones_like(per_example_answer_loss, dtype=tf.float32)
else:
large_answer_loss_mask = tf.where(
per_example_answer_loss > config.answer_loss_cutoff,
tf.zeros_like(per_example_answer_loss, dtype=tf.float32),
tf.ones_like(per_example_answer_loss, dtype=tf.float32),
)
per_example_answer_loss_scaled = config.answer_loss_importance * (per_example_answer_loss * aggregate_mask)
return per_example_answer_loss_scaled, large_answer_loss_mask
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/tapas/__init__.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_import_structure = {
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_tapas": ["TapasTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tapas"] = [
"TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
"TapasForMaskedLM",
"TapasForQuestionAnswering",
"TapasForSequenceClassification",
"TapasModel",
"TapasPreTrainedModel",
"load_tf_weights_in_tapas",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_tapas"] = [
"TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFTapasForMaskedLM",
"TFTapasForQuestionAnswering",
"TFTapasForSequenceClassification",
"TFTapasModel",
"TFTapasPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/tapas/tokenization_tapas.py | # coding=utf-8
# Copyright 2020 Google Research and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tokenization class for TAPAS model."""
import collections
import datetime
import enum
import itertools
import math
import os
import re
import unicodedata
from dataclasses import dataclass
from typing import Callable, Dict, Generator, List, Optional, Text, Tuple, Union
import numpy as np
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...tokenization_utils_base import (
ENCODE_KWARGS_DOCSTRING,
VERY_LARGE_INTEGER,
BatchEncoding,
EncodedInput,
PreTokenizedInput,
TextInput,
)
from ...utils import ExplicitEnum, PaddingStrategy, TensorType, add_end_docstrings, is_pandas_available, logging
if is_pandas_available():
import pandas as pd
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
# large models
"google/tapas-large-finetuned-sqa": (
"https://huggingface.co/google/tapas-large-finetuned-sqa/resolve/main/vocab.txt"
),
"google/tapas-large-finetuned-wtq": (
"https://huggingface.co/google/tapas-large-finetuned-wtq/resolve/main/vocab.txt"
),
"google/tapas-large-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-large-finetuned-wikisql-supervised/resolve/main/vocab.txt"
),
"google/tapas-large-finetuned-tabfact": (
"https://huggingface.co/google/tapas-large-finetuned-tabfact/resolve/main/vocab.txt"
),
# base models
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/vocab.txt"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/vocab.txt"
),
"google/tapas-base-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/vocab.txt"
),
"google/tapas-base-finetuned-tabfact": (
"https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/vocab.txt"
),
# medium models
"google/tapas-medium-finetuned-sqa": (
"https://huggingface.co/google/tapas-medium-finetuned-sqa/resolve/main/vocab.txt"
),
"google/tapas-medium-finetuned-wtq": (
"https://huggingface.co/google/tapas-medium-finetuned-wtq/resolve/main/vocab.txt"
),
"google/tapas-medium-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-medium-finetuned-wikisql-supervised/resolve/main/vocab.txt"
),
"google/tapas-medium-finetuned-tabfact": (
"https://huggingface.co/google/tapas-medium-finetuned-tabfact/resolve/main/vocab.txt"
),
# small models
"google/tapas-small-finetuned-sqa": (
"https://huggingface.co/google/tapas-small-finetuned-sqa/resolve/main/vocab.txt"
),
"google/tapas-small-finetuned-wtq": (
"https://huggingface.co/google/tapas-small-finetuned-wtq/resolve/main/vocab.txt"
),
"google/tapas-small-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-small-finetuned-wikisql-supervised/resolve/main/vocab.txt"
),
"google/tapas-small-finetuned-tabfact": (
"https://huggingface.co/google/tapas-small-finetuned-tabfact/resolve/main/vocab.txt"
),
# tiny models
"google/tapas-tiny-finetuned-sqa": (
"https://huggingface.co/google/tapas-tiny-finetuned-sqa/resolve/main/vocab.txt"
),
"google/tapas-tiny-finetuned-wtq": (
"https://huggingface.co/google/tapas-tiny-finetuned-wtq/resolve/main/vocab.txt"
),
"google/tapas-tiny-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-tiny-finetuned-wikisql-supervised/resolve/main/vocab.txt"
),
"google/tapas-tiny-finetuned-tabfact": (
"https://huggingface.co/google/tapas-tiny-finetuned-tabfact/resolve/main/vocab.txt"
),
# mini models
"google/tapas-mini-finetuned-sqa": (
"https://huggingface.co/google/tapas-mini-finetuned-sqa/resolve/main/vocab.txt"
),
"google/tapas-mini-finetuned-wtq": (
"https://huggingface.co/google/tapas-mini-finetuned-wtq/resolve/main/vocab.txt"
),
"google/tapas-mini-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-mini-finetuned-wikisql-supervised/resolve/main/vocab.txt"
),
"google/tapas-mini-finetuned-tabfact": (
"https://huggingface.co/google/tapas-mini-finetuned-tabfact/resolve/main/vocab.txt"
),
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {name: 512 for name in PRETRAINED_VOCAB_FILES_MAP.keys()}
PRETRAINED_INIT_CONFIGURATION = {name: {"do_lower_case": True} for name in PRETRAINED_VOCAB_FILES_MAP.keys()}
class TapasTruncationStrategy(ExplicitEnum):
"""
Possible values for the `truncation` argument in [`~TapasTokenizer.__call__`]. Useful for tab-completion in an IDE.
"""
DROP_ROWS_TO_FIT = "drop_rows_to_fit"
DO_NOT_TRUNCATE = "do_not_truncate"
TableValue = collections.namedtuple("TokenValue", ["token", "column_id", "row_id"])
@dataclass(frozen=True)
class TokenCoordinates:
column_index: int
row_index: int
token_index: int
@dataclass
class TokenizedTable:
rows: List[List[List[Text]]]
selected_tokens: List[TokenCoordinates]
@dataclass(frozen=True)
class SerializedExample:
tokens: List[Text]
column_ids: List[int]
row_ids: List[int]
segment_ids: List[int]
def _is_inner_wordpiece(token: Text):
return token.startswith("##")
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
with open(vocab_file, "r", encoding="utf-8") as reader:
tokens = reader.readlines()
for index, token in enumerate(tokens):
token = token.rstrip("\n")
vocab[token] = index
return vocab
def whitespace_tokenize(text):
"""Runs basic whitespace cleaning and splitting on a piece of text."""
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens
TAPAS_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
add_special_tokens (`bool`, *optional*, defaults to `True`):
Whether or not to encode the sequences with the special tokens relative to their model.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Activates and controls padding. Accepts the following values:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, `str` or [`TapasTruncationStrategy`], *optional*, defaults to `False`):
Activates and controls truncation. Accepts the following values:
- `True` or `'drop_rows_to_fit'`: Truncate to a maximum length specified with the argument `max_length`
or to the maximum acceptable input length for the model if that argument is not provided. This will
truncate row by row, removing rows from the table.
- `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
max_length (`int`, *optional*):
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
is_split_into_words (`bool`, *optional*, defaults to `False`):
Whether or not the input is already pre-tokenized (e.g., split into words). If set to `True`, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.
pad_to_multiple_of (`int`, *optional*):
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
"""
class TapasTokenizer(PreTrainedTokenizer):
r"""
Construct a TAPAS tokenizer. Based on WordPiece. Flattens a table and one or more related sentences to be used by
TAPAS models.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods. [`TapasTokenizer`] creates several token type ids to
encode tabular structure. To be more precise, it adds 7 token type ids, in the following order: `segment_ids`,
`column_ids`, `row_ids`, `prev_labels`, `column_ranks`, `inv_column_ranks` and `numeric_relations`:
- segment_ids: indicate whether a token belongs to the question (0) or the table (1). 0 for special tokens and
padding.
- column_ids: indicate to which column of the table a token belongs (starting from 1). Is 0 for all question
tokens, special tokens and padding.
- row_ids: indicate to which row of the table a token belongs (starting from 1). Is 0 for all question tokens,
special tokens and padding. Tokens of column headers are also 0.
- prev_labels: indicate whether a token was (part of) an answer to the previous question (1) or not (0). Useful in
a conversational setup (such as SQA).
- column_ranks: indicate the rank of a table token relative to a column, if applicable. For example, if you have a
column "number of movies" with values 87, 53 and 69, then the column ranks of these tokens are 3, 1 and 2
respectively. 0 for all question tokens, special tokens and padding.
- inv_column_ranks: indicate the inverse rank of a table token relative to a column, if applicable. For example, if
you have a column "number of movies" with values 87, 53 and 69, then the inverse column ranks of these tokens are
1, 3 and 2 respectively. 0 for all question tokens, special tokens and padding.
- numeric_relations: indicate numeric relations between the question and the tokens of the table. 0 for all
question tokens, special tokens and padding.
[`TapasTokenizer`] runs end-to-end tokenization on a table and associated sentences: punctuation splitting and
wordpiece.
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
do_basic_tokenize (`bool`, *optional*, defaults to `True`):
Whether or not to do basic tokenization before WordPiece.
never_split (`Iterable`, *optional*):
Collection of tokens which will never be split during tokenization. Only has an effect when
`do_basic_tokenize=True`
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
empty_token (`str`, *optional*, defaults to `"[EMPTY]"`):
The token used for empty cell values in a table. Empty cell values include "", "n/a", "nan" and "?".
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original BERT).
cell_trim_length (`int`, *optional*, defaults to -1):
If > 0: Trim cells so that the length is <= this value. Also disables further cell trimming, should thus be
used with `truncation` set to `True`.
max_column_id (`int`, *optional*):
Max column id to extract.
max_row_id (`int`, *optional*):
Max row id to extract.
strip_column_names (`bool`, *optional*, defaults to `False`):
Whether to add empty strings instead of column names.
update_answer_coordinates (`bool`, *optional*, defaults to `False`):
Whether to recompute the answer coordinates from the answer text.
min_question_length (`int`, *optional*):
Minimum length of each question in terms of tokens (will be skipped otherwise).
max_question_length (`int`, *optional*):
Maximum length of each question in terms of tokens (will be skipped otherwise).
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(
self,
vocab_file,
do_lower_case=True,
do_basic_tokenize=True,
never_split=None,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
empty_token="[EMPTY]",
tokenize_chinese_chars=True,
strip_accents=None,
cell_trim_length: int = -1,
max_column_id: int = None,
max_row_id: int = None,
strip_column_names: bool = False,
update_answer_coordinates: bool = False,
min_question_length=None,
max_question_length=None,
model_max_length: int = 512,
additional_special_tokens: Optional[List[str]] = None,
**kwargs,
):
if not is_pandas_available():
raise ImportError("Pandas is required for the TAPAS tokenizer.")
if additional_special_tokens is not None:
if empty_token not in additional_special_tokens:
additional_special_tokens.append(empty_token)
else:
additional_special_tokens = [empty_token]
if not os.path.isfile(vocab_file):
raise ValueError(
f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
" model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
)
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
self.do_basic_tokenize = do_basic_tokenize
if do_basic_tokenize:
self.basic_tokenizer = BasicTokenizer(
do_lower_case=do_lower_case,
never_split=never_split,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))
# Additional properties
self.cell_trim_length = cell_trim_length
self.max_column_id = (
max_column_id
if max_column_id is not None
else model_max_length
if model_max_length is not None
else VERY_LARGE_INTEGER
)
self.max_row_id = (
max_row_id
if max_row_id is not None
else model_max_length
if model_max_length is not None
else VERY_LARGE_INTEGER
)
self.strip_column_names = strip_column_names
self.update_answer_coordinates = update_answer_coordinates
self.min_question_length = min_question_length
self.max_question_length = max_question_length
super().__init__(
do_lower_case=do_lower_case,
do_basic_tokenize=do_basic_tokenize,
never_split=never_split,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
empty_token=empty_token,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
cell_trim_length=cell_trim_length,
max_column_id=max_column_id,
max_row_id=max_row_id,
strip_column_names=strip_column_names,
update_answer_coordinates=update_answer_coordinates,
min_question_length=min_question_length,
max_question_length=max_question_length,
model_max_length=model_max_length,
additional_special_tokens=additional_special_tokens,
**kwargs,
)
@property
def do_lower_case(self):
return self.basic_tokenizer.do_lower_case
@property
def vocab_size(self):
return len(self.vocab)
def get_vocab(self):
return dict(self.vocab, **self.added_tokens_encoder)
def _tokenize(self, text):
if format_text(text) == EMPTY_TEXT:
return [self.additional_special_tokens[0]]
split_tokens = []
if self.do_basic_tokenize:
for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
# If the token is part of the never_split set
if token in self.basic_tokenizer.never_split:
split_tokens.append(token)
else:
split_tokens += self.wordpiece_tokenizer.tokenize(token)
else:
split_tokens = self.wordpiece_tokenizer.tokenize(text)
return split_tokens
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.vocab.get(token, self.vocab.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.ids_to_tokens.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
else:
vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
with open(vocab_file, "w", encoding="utf-8") as writer:
for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!"
)
index = token_index
writer.write(token + "\n")
index += 1
return (vocab_file,)
def create_attention_mask_from_sequences(self, query_ids: List[int], table_values: List[TableValue]) -> List[int]:
"""
Creates the attention mask according to the query token IDs and a list of table values.
Args:
query_ids (`List[int]`): list of token IDs corresponding to the ID.
table_values (`List[TableValue]`): lift of table values, which are named tuples containing the
token value, the column ID and the row ID of said token.
Returns:
`List[int]`: List of ints containing the attention mask values.
"""
return [1] * (1 + len(query_ids) + 1 + len(table_values))
def create_segment_token_type_ids_from_sequences(
self, query_ids: List[int], table_values: List[TableValue]
) -> List[int]:
"""
Creates the segment token type IDs according to the query token IDs and a list of table values.
Args:
query_ids (`List[int]`): list of token IDs corresponding to the ID.
table_values (`List[TableValue]`): lift of table values, which are named tuples containing the
token value, the column ID and the row ID of said token.
Returns:
`List[int]`: List of ints containing the segment token type IDs values.
"""
table_ids = list(zip(*table_values))[0] if table_values else []
return [0] * (1 + len(query_ids) + 1) + [1] * len(table_ids)
def create_column_token_type_ids_from_sequences(
self, query_ids: List[int], table_values: List[TableValue]
) -> List[int]:
"""
Creates the column token type IDs according to the query token IDs and a list of table values.
Args:
query_ids (`List[int]`): list of token IDs corresponding to the ID.
table_values (`List[TableValue]`): lift of table values, which are named tuples containing the
token value, the column ID and the row ID of said token.
Returns:
`List[int]`: List of ints containing the column token type IDs values.
"""
table_column_ids = list(zip(*table_values))[1] if table_values else []
return [0] * (1 + len(query_ids) + 1) + list(table_column_ids)
def create_row_token_type_ids_from_sequences(
self, query_ids: List[int], table_values: List[TableValue]
) -> List[int]:
"""
Creates the row token type IDs according to the query token IDs and a list of table values.
Args:
query_ids (`List[int]`): list of token IDs corresponding to the ID.
table_values (`List[TableValue]`): lift of table values, which are named tuples containing the
token value, the column ID and the row ID of said token.
Returns:
`List[int]`: List of ints containing the row token type IDs values.
"""
table_row_ids = list(zip(*table_values))[2] if table_values else []
return [0] * (1 + len(query_ids) + 1) + list(table_row_ids)
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a question and flattened table for question answering or sequence classification tasks
by concatenating and adding special tokens.
Args:
token_ids_0 (`List[int]`): The ids of the question.
token_ids_1 (`List[int]`, *optional*): The ids of the flattened table.
Returns:
`List[int]`: The model input with special tokens.
"""
if token_ids_1 is None:
raise ValueError("With TAPAS, you must provide both question IDs and table IDs.")
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + token_ids_1
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of question IDs.
token_ids_1 (`List[int]`, *optional*):
List of flattened table IDs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
return [1] + ([0] * len(token_ids_0)) + [1]
@add_end_docstrings(TAPAS_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def __call__(
self,
table: "pd.DataFrame",
queries: Optional[
Union[
TextInput,
PreTokenizedInput,
EncodedInput,
List[TextInput],
List[PreTokenizedInput],
List[EncodedInput],
]
] = None,
answer_coordinates: Optional[Union[List[Tuple], List[List[Tuple]]]] = None,
answer_text: Optional[Union[List[TextInput], List[List[TextInput]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TapasTruncationStrategy] = False,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Main method to tokenize and prepare for the model one or several sequence(s) related to a table.
Args:
table (`pd.DataFrame`):
Table containing tabular data. Note that all cell values must be text. Use *.astype(str)* on a Pandas
dataframe to convert it to string.
queries (`str` or `List[str]`):
Question or batch of questions related to a table to be encoded. Note that in case of a batch, all
questions must refer to the **same** table.
answer_coordinates (`List[Tuple]` or `List[List[Tuple]]`, *optional*):
Answer coordinates of each table-question pair in the batch. In case only a single table-question pair
is provided, then the answer_coordinates must be a single list of one or more tuples. Each tuple must
be a (row_index, column_index) pair. The first data row (not the column header row) has index 0. The
first column has index 0. In case a batch of table-question pairs is provided, then the
answer_coordinates must be a list of lists of tuples (each list corresponding to a single
table-question pair).
answer_text (`List[str]` or `List[List[str]]`, *optional*):
Answer text of each table-question pair in the batch. In case only a single table-question pair is
provided, then the answer_text must be a single list of one or more strings. Each string must be the
answer text of a corresponding answer coordinate. In case a batch of table-question pairs is provided,
then the answer_coordinates must be a list of lists of strings (each list corresponding to a single
table-question pair).
"""
assert isinstance(table, pd.DataFrame), "Table must be of type pd.DataFrame"
# Input type checking for clearer error
valid_query = False
# Check that query has a valid type
if queries is None or isinstance(queries, str):
valid_query = True
elif isinstance(queries, (list, tuple)):
if len(queries) == 0 or isinstance(queries[0], str):
valid_query = True
if not valid_query:
raise ValueError(
"queries input must of type `str` (single example), `List[str]` (batch or single pretokenized"
" example). "
)
is_batched = isinstance(queries, (list, tuple))
if is_batched:
return self.batch_encode_plus(
table=table,
queries=queries,
answer_coordinates=answer_coordinates,
answer_text=answer_text,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
else:
return self.encode_plus(
table=table,
query=queries,
answer_coordinates=answer_coordinates,
answer_text=answer_text,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPAS_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def batch_encode_plus(
self,
table: "pd.DataFrame",
queries: Optional[
Union[
List[TextInput],
List[PreTokenizedInput],
List[EncodedInput],
]
] = None,
answer_coordinates: Optional[List[List[Tuple]]] = None,
answer_text: Optional[List[List[TextInput]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TapasTruncationStrategy] = False,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Prepare a table and a list of strings for the model.
<Tip warning={true}>
This method is deprecated, `__call__` should be used instead.
</Tip>
Args:
table (`pd.DataFrame`):
Table containing tabular data. Note that all cell values must be text. Use *.astype(str)* on a Pandas
dataframe to convert it to string.
queries (`List[str]`):
Batch of questions related to a table to be encoded. Note that all questions must refer to the **same**
table.
answer_coordinates (`List[Tuple]` or `List[List[Tuple]]`, *optional*):
Answer coordinates of each table-question pair in the batch. Each tuple must be a (row_index,
column_index) pair. The first data row (not the column header row) has index 0. The first column has
index 0. The answer_coordinates must be a list of lists of tuples (each list corresponding to a single
table-question pair).
answer_text (`List[str]` or `List[List[str]]`, *optional*):
Answer text of each table-question pair in the batch. In case a batch of table-question pairs is
provided, then the answer_coordinates must be a list of lists of strings (each list corresponding to a
single table-question pair). Each string must be the answer text of a corresponding answer coordinate.
"""
if return_token_type_ids is not None and not add_special_tokens:
raise ValueError(
"Asking to return token_type_ids while setting add_special_tokens to False "
"results in an undefined behavior. Please set add_special_tokens to True or "
"set return_token_type_ids to None."
)
if (answer_coordinates and not answer_text) or (not answer_coordinates and answer_text):
raise ValueError("In case you provide answers, both answer_coordinates and answer_text should be provided")
elif answer_coordinates is None and answer_text is None:
answer_coordinates = answer_text = [None] * len(queries)
if "is_split_into_words" in kwargs:
raise NotImplementedError("Currently TapasTokenizer only supports questions as strings.")
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast."
)
return self._batch_encode_plus(
table=table,
queries=queries,
answer_coordinates=answer_coordinates,
answer_text=answer_text,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
def _get_question_tokens(self, query):
"""Tokenizes the query, taking into account the max and min question length."""
query_tokens = self.tokenize(query)
if self.max_question_length is not None and len(query_tokens) > self.max_question_length:
logger.warning("Skipping query as its tokens are longer than the max question length")
return "", []
if self.min_question_length is not None and len(query_tokens) < self.min_question_length:
logger.warning("Skipping query as its tokens are shorter than the min question length")
return "", []
return query, query_tokens
def _batch_encode_plus(
self,
table,
queries: Union[
List[TextInput],
List[PreTokenizedInput],
List[EncodedInput],
],
answer_coordinates: Optional[List[List[Tuple]]] = None,
answer_text: Optional[List[List[TextInput]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TapasTruncationStrategy] = False,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = True,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
table_tokens = self._tokenize_table(table)
queries_tokens = []
for idx, query in enumerate(queries):
query, query_tokens = self._get_question_tokens(query)
queries[idx] = query
queries_tokens.append(query_tokens)
batch_outputs = self._batch_prepare_for_model(
table,
queries,
tokenized_table=table_tokens,
queries_tokens=queries_tokens,
answer_coordinates=answer_coordinates,
padding=padding,
truncation=truncation,
answer_text=answer_text,
add_special_tokens=add_special_tokens,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
prepend_batch_axis=True,
return_attention_mask=return_attention_mask,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
verbose=verbose,
)
return BatchEncoding(batch_outputs)
def _batch_prepare_for_model(
self,
raw_table: "pd.DataFrame",
raw_queries: Union[
List[TextInput],
List[PreTokenizedInput],
List[EncodedInput],
],
tokenized_table: Optional[TokenizedTable] = None,
queries_tokens: Optional[List[List[str]]] = None,
answer_coordinates: Optional[List[List[Tuple]]] = None,
answer_text: Optional[List[List[TextInput]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TapasTruncationStrategy] = False,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = True,
return_attention_mask: Optional[bool] = True,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
prepend_batch_axis: bool = False,
**kwargs,
) -> BatchEncoding:
batch_outputs = {}
for index, example in enumerate(zip(raw_queries, queries_tokens, answer_coordinates, answer_text)):
raw_query, query_tokens, answer_coords, answer_txt = example
outputs = self.prepare_for_model(
raw_table,
raw_query,
tokenized_table=tokenized_table,
query_tokens=query_tokens,
answer_coordinates=answer_coords,
answer_text=answer_txt,
add_special_tokens=add_special_tokens,
padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterwards
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=None, # we pad in batch afterwards
return_attention_mask=False, # we pad in batch afterwards
return_token_type_ids=return_token_type_ids,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
return_tensors=None, # We convert the whole batch to tensors at the end
prepend_batch_axis=False,
verbose=verbose,
prev_answer_coordinates=answer_coordinates[index - 1] if index != 0 else None,
prev_answer_text=answer_text[index - 1] if index != 0 else None,
)
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
batch_outputs = self.pad(
batch_outputs,
padding=padding,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
return batch_outputs
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING)
def encode(
self,
table: "pd.DataFrame",
query: Optional[
Union[
TextInput,
PreTokenizedInput,
EncodedInput,
]
] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TapasTruncationStrategy] = False,
max_length: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs,
) -> List[int]:
"""
Prepare a table and a string for the model. This method does not return token type IDs, attention masks, etc.
which are necessary for the model to work correctly. Use that method if you want to build your processing on
your own, otherwise refer to `__call__`.
Args:
table (`pd.DataFrame`):
Table containing tabular data. Note that all cell values must be text. Use *.astype(str)* on a Pandas
dataframe to convert it to string.
query (`str` or `List[str]`):
Question related to a table to be encoded.
"""
encoded_inputs = self.encode_plus(
table,
query=query,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
return_tensors=return_tensors,
**kwargs,
)
return encoded_inputs["input_ids"]
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPAS_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def encode_plus(
self,
table: "pd.DataFrame",
query: Optional[
Union[
TextInput,
PreTokenizedInput,
EncodedInput,
]
] = None,
answer_coordinates: Optional[List[Tuple]] = None,
answer_text: Optional[List[TextInput]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TapasTruncationStrategy] = False,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Prepare a table and a string for the model.
Args:
table (`pd.DataFrame`):
Table containing tabular data. Note that all cell values must be text. Use *.astype(str)* on a Pandas
dataframe to convert it to string.
query (`str` or `List[str]`):
Question related to a table to be encoded.
answer_coordinates (`List[Tuple]` or `List[List[Tuple]]`, *optional*):
Answer coordinates of each table-question pair in the batch. The answer_coordinates must be a single
list of one or more tuples. Each tuple must be a (row_index, column_index) pair. The first data row
(not the column header row) has index 0. The first column has index 0.
answer_text (`List[str]` or `List[List[str]]`, *optional*):
Answer text of each table-question pair in the batch. The answer_text must be a single list of one or
more strings. Each string must be the answer text of a corresponding answer coordinate.
"""
if return_token_type_ids is not None and not add_special_tokens:
raise ValueError(
"Asking to return token_type_ids while setting add_special_tokens to False "
"results in an undefined behavior. Please set add_special_tokens to True or "
"set return_token_type_ids to None."
)
if (answer_coordinates and not answer_text) or (not answer_coordinates and answer_text):
raise ValueError("In case you provide answers, both answer_coordinates and answer_text should be provided")
if "is_split_into_words" in kwargs:
raise NotImplementedError("Currently TapasTokenizer only supports questions as strings.")
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast."
)
return self._encode_plus(
table=table,
query=query,
answer_coordinates=answer_coordinates,
answer_text=answer_text,
add_special_tokens=add_special_tokens,
truncation=truncation,
padding=padding,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
def _encode_plus(
self,
table: "pd.DataFrame",
query: Union[
TextInput,
PreTokenizedInput,
EncodedInput,
],
answer_coordinates: Optional[List[Tuple]] = None,
answer_text: Optional[List[TextInput]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TapasTruncationStrategy] = False,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = True,
return_attention_mask: Optional[bool] = True,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
):
if query is None:
query = ""
logger.warning(
"TAPAS is a question answering model but you have not passed a query. Please be aware that the "
"model will probably not behave correctly."
)
table_tokens = self._tokenize_table(table)
query, query_tokens = self._get_question_tokens(query)
return self.prepare_for_model(
table,
query,
tokenized_table=table_tokens,
query_tokens=query_tokens,
answer_coordinates=answer_coordinates,
answer_text=answer_text,
add_special_tokens=add_special_tokens,
truncation=truncation,
padding=padding,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
prepend_batch_axis=True,
return_attention_mask=return_attention_mask,
return_token_type_ids=return_token_type_ids,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
verbose=verbose,
)
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPAS_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def prepare_for_model(
self,
raw_table: "pd.DataFrame",
raw_query: Union[
TextInput,
PreTokenizedInput,
EncodedInput,
],
tokenized_table: Optional[TokenizedTable] = None,
query_tokens: Optional[TokenizedTable] = None,
answer_coordinates: Optional[List[Tuple]] = None,
answer_text: Optional[List[TextInput]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TapasTruncationStrategy] = False,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = True,
return_attention_mask: Optional[bool] = True,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
prepend_batch_axis: bool = False,
**kwargs,
) -> BatchEncoding:
"""
Prepares a sequence of input id so that it can be used by the model. It adds special tokens, truncates
sequences if overflowing while taking into account the special tokens.
Args:
raw_table (`pd.DataFrame`):
The original table before any transformation (like tokenization) was applied to it.
raw_query (`TextInput` or `PreTokenizedInput` or `EncodedInput`):
The original query before any transformation (like tokenization) was applied to it.
tokenized_table (`TokenizedTable`):
The table after tokenization.
query_tokens (`List[str]`):
The query after tokenization.
answer_coordinates (`List[Tuple]` or `List[List[Tuple]]`, *optional*):
Answer coordinates of each table-question pair in the batch. The answer_coordinates must be a single
list of one or more tuples. Each tuple must be a (row_index, column_index) pair. The first data row
(not the column header row) has index 0. The first column has index 0.
answer_text (`List[str]` or `List[List[str]]`, *optional*):
Answer text of each table-question pair in the batch. The answer_text must be a single list of one or
more strings. Each string must be the answer text of a corresponding answer coordinate.
"""
if isinstance(padding, bool):
if padding and (max_length is not None or pad_to_multiple_of is not None):
padding = PaddingStrategy.MAX_LENGTH
else:
padding = PaddingStrategy.DO_NOT_PAD
elif not isinstance(padding, PaddingStrategy):
padding = PaddingStrategy(padding)
if isinstance(truncation, bool):
if truncation:
truncation = TapasTruncationStrategy.DROP_ROWS_TO_FIT
else:
truncation = TapasTruncationStrategy.DO_NOT_TRUNCATE
elif not isinstance(truncation, TapasTruncationStrategy):
truncation = TapasTruncationStrategy(truncation)
encoded_inputs = {}
is_part_of_batch = False
prev_answer_coordinates, prev_answer_text = None, None
if "prev_answer_coordinates" in kwargs and "prev_answer_text" in kwargs:
is_part_of_batch = True
prev_answer_coordinates = kwargs["prev_answer_coordinates"]
prev_answer_text = kwargs["prev_answer_text"]
num_rows = self._get_num_rows(raw_table, truncation != TapasTruncationStrategy.DO_NOT_TRUNCATE)
num_columns = self._get_num_columns(raw_table)
_, _, num_tokens = self._get_table_boundaries(tokenized_table)
if truncation != TapasTruncationStrategy.DO_NOT_TRUNCATE:
num_rows, num_tokens = self._get_truncated_table_rows(
query_tokens, tokenized_table, num_rows, num_columns, max_length, truncation_strategy=truncation
)
table_data = list(self._get_table_values(tokenized_table, num_columns, num_rows, num_tokens))
query_ids = self.convert_tokens_to_ids(query_tokens)
table_ids = list(zip(*table_data))[0] if len(table_data) > 0 else list(zip(*table_data))
table_ids = self.convert_tokens_to_ids(list(table_ids))
if "return_overflowing_tokens" in kwargs and kwargs["return_overflowing_tokens"]:
raise ValueError("TAPAS does not return overflowing tokens as it works on tables.")
if add_special_tokens:
input_ids = self.build_inputs_with_special_tokens(query_ids, table_ids)
else:
input_ids = query_ids + table_ids
if max_length is not None and len(input_ids) > max_length:
raise ValueError(
"Could not encode the query and table header given the maximum length. Encoding the query and table "
f"header results in a length of {len(input_ids)} which is higher than the max_length of {max_length}"
)
encoded_inputs["input_ids"] = input_ids
segment_ids = self.create_segment_token_type_ids_from_sequences(query_ids, table_data)
column_ids = self.create_column_token_type_ids_from_sequences(query_ids, table_data)
row_ids = self.create_row_token_type_ids_from_sequences(query_ids, table_data)
if not is_part_of_batch or (prev_answer_coordinates is None and prev_answer_text is None):
# simply set the prev_labels to zeros
prev_labels = [0] * len(row_ids)
else:
prev_labels = self.get_answer_ids(
column_ids, row_ids, table_data, prev_answer_text, prev_answer_coordinates
)
# FIRST: parse both the table and question in terms of numeric values
raw_table = add_numeric_table_values(raw_table)
raw_query = add_numeric_values_to_question(raw_query)
# SECOND: add numeric-related features (and not parse them in these functions):
column_ranks, inv_column_ranks = self._get_numeric_column_ranks(column_ids, row_ids, raw_table)
numeric_relations = self._get_numeric_relations(raw_query, column_ids, row_ids, raw_table)
# Load from model defaults
if return_token_type_ids is None:
return_token_type_ids = "token_type_ids" in self.model_input_names
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
if return_attention_mask:
attention_mask = self.create_attention_mask_from_sequences(query_ids, table_data)
encoded_inputs["attention_mask"] = attention_mask
if answer_coordinates is not None and answer_text is not None:
labels = self.get_answer_ids(column_ids, row_ids, table_data, answer_text, answer_coordinates)
numeric_values = self._get_numeric_values(raw_table, column_ids, row_ids)
numeric_values_scale = self._get_numeric_values_scale(raw_table, column_ids, row_ids)
encoded_inputs["labels"] = labels
encoded_inputs["numeric_values"] = numeric_values
encoded_inputs["numeric_values_scale"] = numeric_values_scale
if return_token_type_ids:
token_type_ids = [
segment_ids,
column_ids,
row_ids,
prev_labels,
column_ranks,
inv_column_ranks,
numeric_relations,
]
token_type_ids = [list(ids) for ids in list(zip(*token_type_ids))]
encoded_inputs["token_type_ids"] = token_type_ids
if return_special_tokens_mask:
if add_special_tokens:
encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(query_ids, table_ids)
else:
encoded_inputs["special_tokens_mask"] = [0] * len(input_ids)
# Check lengths
if max_length is None and len(encoded_inputs["input_ids"]) > self.model_max_length and verbose:
if not self.deprecation_warnings.get("sequence-length-is-longer-than-the-specified-maximum", False):
logger.warning(
"Token indices sequence length is longer than the specified maximum sequence length "
f"for this model ({len(encoded_inputs['input_ids'])} > {self.model_max_length}). Running this "
"sequence through the model will result in indexing errors."
)
self.deprecation_warnings["sequence-length-is-longer-than-the-specified-maximum"] = True
# Padding
if padding != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
encoded_inputs = self.pad(
encoded_inputs,
max_length=max_length,
padding=padding.value,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
if return_length:
encoded_inputs["length"] = len(encoded_inputs["input_ids"])
batch_outputs = BatchEncoding(
encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
)
return batch_outputs
def _get_truncated_table_rows(
self,
query_tokens: List[str],
tokenized_table: TokenizedTable,
num_rows: int,
num_columns: int,
max_length: int,
truncation_strategy: Union[str, TapasTruncationStrategy],
) -> Tuple[int, int]:
"""
Truncates a sequence pair in-place following the strategy.
Args:
query_tokens (`List[str]`):
List of strings corresponding to the tokenized query.
tokenized_table (`TokenizedTable`):
Tokenized table
num_rows (`int`):
Total number of table rows
num_columns (`int`):
Total number of table columns
max_length (`int`):
Total maximum length.
truncation_strategy (`str` or [`TapasTruncationStrategy`]):
Truncation strategy to use. Seeing as this method should only be called when truncating, the only
available strategy is the `"drop_rows_to_fit"` strategy.
Returns:
`Tuple(int, int)`: tuple containing the number of rows after truncation, and the number of tokens available
for each table element.
"""
if not isinstance(truncation_strategy, TapasTruncationStrategy):
truncation_strategy = TapasTruncationStrategy(truncation_strategy)
if max_length is None:
max_length = self.model_max_length
if truncation_strategy == TapasTruncationStrategy.DROP_ROWS_TO_FIT:
while True:
num_tokens = self._get_max_num_tokens(
query_tokens, tokenized_table, num_rows=num_rows, num_columns=num_columns, max_length=max_length
)
if num_tokens is not None:
# We could fit the table.
break
# Try to drop a row to fit the table.
num_rows -= 1
if num_rows < 1:
break
elif truncation_strategy != TapasTruncationStrategy.DO_NOT_TRUNCATE:
raise ValueError(f"Unknown truncation strategy {truncation_strategy}.")
return num_rows, num_tokens or 1
def _tokenize_table(
self,
table=None,
):
"""
Tokenizes column headers and cell texts of a table.
Args:
table (`pd.Dataframe`):
Table. Returns: `TokenizedTable`: TokenizedTable object.
"""
tokenized_rows = []
tokenized_row = []
# tokenize column headers
for column in table:
if self.strip_column_names:
tokenized_row.append(self.tokenize(""))
else:
tokenized_row.append(self.tokenize(column))
tokenized_rows.append(tokenized_row)
# tokenize cell values
for idx, row in table.iterrows():
tokenized_row = []
for cell in row:
tokenized_row.append(self.tokenize(cell))
tokenized_rows.append(tokenized_row)
token_coordinates = []
for row_index, row in enumerate(tokenized_rows):
for column_index, cell in enumerate(row):
for token_index, _ in enumerate(cell):
token_coordinates.append(
TokenCoordinates(
row_index=row_index,
column_index=column_index,
token_index=token_index,
)
)
return TokenizedTable(
rows=tokenized_rows,
selected_tokens=token_coordinates,
)
def _question_encoding_cost(self, question_tokens):
# Two extra spots of SEP and CLS.
return len(question_tokens) + 2
def _get_token_budget(self, question_tokens, max_length=None):
"""
Computes the number of tokens left for the table after tokenizing a question, taking into account the max
sequence length of the model.
Args:
question_tokens (`List[String]`):
List of question tokens. Returns: `int`: the number of tokens left for the table, given the model max
length.
"""
return (max_length if max_length is not None else self.model_max_length) - self._question_encoding_cost(
question_tokens
)
def _get_table_values(self, table, num_columns, num_rows, num_tokens) -> Generator[TableValue, None, None]:
"""Iterates over partial table and returns token, column and row indexes."""
for tc in table.selected_tokens:
# First row is header row.
if tc.row_index >= num_rows + 1:
continue
if tc.column_index >= num_columns:
continue
cell = table.rows[tc.row_index][tc.column_index]
token = cell[tc.token_index]
word_begin_index = tc.token_index
# Don't add partial words. Find the starting word piece and check if it
# fits in the token budget.
while word_begin_index >= 0 and _is_inner_wordpiece(cell[word_begin_index]):
word_begin_index -= 1
if word_begin_index >= num_tokens:
continue
yield TableValue(token, tc.column_index + 1, tc.row_index)
def _get_table_boundaries(self, table):
"""Return maximal number of rows, columns and tokens."""
max_num_tokens = 0
max_num_columns = 0
max_num_rows = 0
for tc in table.selected_tokens:
max_num_columns = max(max_num_columns, tc.column_index + 1)
max_num_rows = max(max_num_rows, tc.row_index + 1)
max_num_tokens = max(max_num_tokens, tc.token_index + 1)
max_num_columns = min(self.max_column_id, max_num_columns)
max_num_rows = min(self.max_row_id, max_num_rows)
return max_num_rows, max_num_columns, max_num_tokens
def _get_table_cost(self, table, num_columns, num_rows, num_tokens):
return sum(1 for _ in self._get_table_values(table, num_columns, num_rows, num_tokens))
def _get_max_num_tokens(self, question_tokens, tokenized_table, num_columns, num_rows, max_length):
"""Computes max number of tokens that can be squeezed into the budget."""
token_budget = self._get_token_budget(question_tokens, max_length)
_, _, max_num_tokens = self._get_table_boundaries(tokenized_table)
if self.cell_trim_length >= 0 and max_num_tokens > self.cell_trim_length:
max_num_tokens = self.cell_trim_length
num_tokens = 0
for num_tokens in range(max_num_tokens + 1):
cost = self._get_table_cost(tokenized_table, num_columns, num_rows, num_tokens + 1)
if cost > token_budget:
break
if num_tokens < max_num_tokens:
if self.cell_trim_length >= 0:
# We don't allow dynamic trimming if a cell_trim_length is set.
return None
if num_tokens == 0:
return None
return num_tokens
def _get_num_columns(self, table):
num_columns = table.shape[1]
if num_columns >= self.max_column_id:
raise ValueError("Too many columns")
return num_columns
def _get_num_rows(self, table, drop_rows_to_fit):
num_rows = table.shape[0]
if num_rows >= self.max_row_id:
if drop_rows_to_fit:
num_rows = self.max_row_id - 1
else:
raise ValueError("Too many rows")
return num_rows
def _serialize_text(self, question_tokens):
"""Serializes texts in index arrays."""
tokens = []
segment_ids = []
column_ids = []
row_ids = []
# add [CLS] token at the beginning
tokens.append(self.cls_token)
segment_ids.append(0)
column_ids.append(0)
row_ids.append(0)
for token in question_tokens:
tokens.append(token)
segment_ids.append(0)
column_ids.append(0)
row_ids.append(0)
return tokens, segment_ids, column_ids, row_ids
def _serialize(
self,
question_tokens,
table,
num_columns,
num_rows,
num_tokens,
):
"""Serializes table and text."""
tokens, segment_ids, column_ids, row_ids = self._serialize_text(question_tokens)
# add [SEP] token between question and table tokens
tokens.append(self.sep_token)
segment_ids.append(0)
column_ids.append(0)
row_ids.append(0)
for token, column_id, row_id in self._get_table_values(table, num_columns, num_rows, num_tokens):
tokens.append(token)
segment_ids.append(1)
column_ids.append(column_id)
row_ids.append(row_id)
return SerializedExample(
tokens=tokens,
segment_ids=segment_ids,
column_ids=column_ids,
row_ids=row_ids,
)
def _get_column_values(self, table, col_index):
table_numeric_values = {}
for row_index, row in table.iterrows():
cell = row[col_index]
if cell.numeric_value is not None:
table_numeric_values[row_index] = cell.numeric_value
return table_numeric_values
def _get_cell_token_indexes(self, column_ids, row_ids, column_id, row_id):
for index in range(len(column_ids)):
if column_ids[index] - 1 == column_id and row_ids[index] - 1 == row_id:
yield index
def _get_numeric_column_ranks(self, column_ids, row_ids, table):
"""Returns column ranks for all numeric columns."""
ranks = [0] * len(column_ids)
inv_ranks = [0] * len(column_ids)
# original code from tf_example_utils.py of the original implementation
if table is not None:
for col_index in range(len(table.columns)):
table_numeric_values = self._get_column_values(table, col_index)
if not table_numeric_values:
continue
try:
key_fn = get_numeric_sort_key_fn(table_numeric_values.values())
except ValueError:
continue
table_numeric_values = {row_index: key_fn(value) for row_index, value in table_numeric_values.items()}
table_numeric_values_inv = collections.defaultdict(list)
for row_index, value in table_numeric_values.items():
table_numeric_values_inv[value].append(row_index)
unique_values = sorted(table_numeric_values_inv.keys())
for rank, value in enumerate(unique_values):
for row_index in table_numeric_values_inv[value]:
for index in self._get_cell_token_indexes(column_ids, row_ids, col_index, row_index):
ranks[index] = rank + 1
inv_ranks[index] = len(unique_values) - rank
return ranks, inv_ranks
def _get_numeric_sort_key_fn(self, table_numeric_values, value):
"""
Returns the sort key function for comparing value to table values. The function returned will be a suitable
input for the key param of the sort(). See number_annotation_utils._get_numeric_sort_key_fn for details
Args:
table_numeric_values: Numeric values of a column
value: Numeric value in the question
Returns:
A function key function to compare column and question values.
"""
if not table_numeric_values:
return None
all_values = list(table_numeric_values.values())
all_values.append(value)
try:
return get_numeric_sort_key_fn(all_values)
except ValueError:
return None
def _get_numeric_relations(self, question, column_ids, row_ids, table):
"""
Returns numeric relations embeddings
Args:
question: Question object.
column_ids: Maps word piece position to column id.
row_ids: Maps word piece position to row id.
table: The table containing the numeric cell values.
"""
numeric_relations = [0] * len(column_ids)
# first, we add any numeric value spans to the question:
# Create a dictionary that maps a table cell to the set of all relations
# this cell has with any value in the question.
cell_indices_to_relations = collections.defaultdict(set)
if question is not None and table is not None:
for numeric_value_span in question.numeric_spans:
for value in numeric_value_span.values:
for column_index in range(len(table.columns)):
table_numeric_values = self._get_column_values(table, column_index)
sort_key_fn = self._get_numeric_sort_key_fn(table_numeric_values, value)
if sort_key_fn is None:
continue
for row_index, cell_value in table_numeric_values.items():
relation = get_numeric_relation(value, cell_value, sort_key_fn)
if relation is not None:
cell_indices_to_relations[column_index, row_index].add(relation)
# For each cell add a special feature for all its word pieces.
for (column_index, row_index), relations in cell_indices_to_relations.items():
relation_set_index = 0
for relation in relations:
assert relation.value >= Relation.EQ.value
relation_set_index += 2 ** (relation.value - Relation.EQ.value)
for cell_token_index in self._get_cell_token_indexes(column_ids, row_ids, column_index, row_index):
numeric_relations[cell_token_index] = relation_set_index
return numeric_relations
def _get_numeric_values(self, table, column_ids, row_ids):
"""Returns numeric values for computation of answer loss."""
numeric_values = [float("nan")] * len(column_ids)
if table is not None:
num_rows = table.shape[0]
num_columns = table.shape[1]
for col_index in range(num_columns):
for row_index in range(num_rows):
numeric_value = table.iloc[row_index, col_index].numeric_value
if numeric_value is not None:
if numeric_value.float_value is None:
continue
float_value = numeric_value.float_value
if float_value == float("inf"):
continue
for index in self._get_cell_token_indexes(column_ids, row_ids, col_index, row_index):
numeric_values[index] = float_value
return numeric_values
def _get_numeric_values_scale(self, table, column_ids, row_ids):
"""Returns a scale to each token to down weigh the value of long words."""
numeric_values_scale = [1.0] * len(column_ids)
if table is None:
return numeric_values_scale
num_rows = table.shape[0]
num_columns = table.shape[1]
for col_index in range(num_columns):
for row_index in range(num_rows):
indices = list(self._get_cell_token_indexes(column_ids, row_ids, col_index, row_index))
num_indices = len(indices)
if num_indices > 1:
for index in indices:
numeric_values_scale[index] = float(num_indices)
return numeric_values_scale
def _pad_to_seq_length(self, inputs):
while len(inputs) > self.model_max_length:
inputs.pop()
while len(inputs) < self.model_max_length:
inputs.append(0)
def _get_all_answer_ids_from_coordinates(
self,
column_ids,
row_ids,
answers_list,
):
"""Maps lists of answer coordinates to token indexes."""
answer_ids = [0] * len(column_ids)
found_answers = set()
all_answers = set()
for answers in answers_list:
column_index, row_index = answers
all_answers.add((column_index, row_index))
for index in self._get_cell_token_indexes(column_ids, row_ids, column_index, row_index):
found_answers.add((column_index, row_index))
answer_ids[index] = 1
missing_count = len(all_answers) - len(found_answers)
return answer_ids, missing_count
def _get_all_answer_ids(self, column_ids, row_ids, answer_coordinates):
"""
Maps answer coordinates of a question to token indexes.
In the SQA format (TSV), the coordinates are given as (row, column) tuples. Here, we first swap them to
(column, row) format before calling _get_all_answer_ids_from_coordinates.
"""
def _to_coordinates(answer_coordinates_question):
return [(coords[1], coords[0]) for coords in answer_coordinates_question]
return self._get_all_answer_ids_from_coordinates(
column_ids, row_ids, answers_list=(_to_coordinates(answer_coordinates))
)
def _find_tokens(self, text, segment):
"""Return start index of segment in text or None."""
logging.info(f"text: {text} {segment}")
for index in range(1 + len(text) - len(segment)):
for seg_index, seg_token in enumerate(segment):
if text[index + seg_index].piece != seg_token.piece:
break
else:
return index
return None
def _find_answer_coordinates_from_answer_text(
self,
tokenized_table,
answer_text,
):
"""Returns all occurrences of answer_text in the table."""
logging.info(f"answer text: {answer_text}")
for row_index, row in enumerate(tokenized_table.rows):
if row_index == 0:
# We don't search for answers in the header.
continue
for col_index, cell in enumerate(row):
token_index = self._find_tokens(cell, answer_text)
if token_index is not None:
yield TokenCoordinates(
row_index=row_index,
column_index=col_index,
token_index=token_index,
)
def _find_answer_ids_from_answer_texts(
self,
column_ids,
row_ids,
tokenized_table,
answer_texts,
):
"""Maps question with answer texts to the first matching token indexes."""
answer_ids = [0] * len(column_ids)
for answer_text in answer_texts:
for coordinates in self._find_answer_coordinates_from_answer_text(
tokenized_table,
answer_text,
):
# Maps answer coordinates to indexes this can fail if tokens / rows have
# been pruned.
indexes = list(
self._get_cell_token_indexes(
column_ids,
row_ids,
column_id=coordinates.column_index,
row_id=coordinates.row_index - 1,
)
)
indexes.sort()
coordinate_answer_ids = []
if indexes:
begin_index = coordinates.token_index + indexes[0]
end_index = begin_index + len(answer_text)
for index in indexes:
if index >= begin_index and index < end_index:
coordinate_answer_ids.append(index)
if len(coordinate_answer_ids) == len(answer_text):
for index in coordinate_answer_ids:
answer_ids[index] = 1
break
return answer_ids
def _get_answer_ids(self, column_ids, row_ids, answer_coordinates):
"""Maps answer coordinates of a question to token indexes."""
answer_ids, missing_count = self._get_all_answer_ids(column_ids, row_ids, answer_coordinates)
if missing_count:
raise ValueError("Couldn't find all answers")
return answer_ids
def get_answer_ids(self, column_ids, row_ids, tokenized_table, answer_texts_question, answer_coordinates_question):
if self.update_answer_coordinates:
return self._find_answer_ids_from_answer_texts(
column_ids,
row_ids,
tokenized_table,
answer_texts=[self.tokenize(at) for at in answer_texts_question],
)
return self._get_answer_ids(column_ids, row_ids, answer_coordinates_question)
def _pad(
self,
encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
) -> dict:
"""
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
Args:
encoded_inputs:
Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The tokenizer padding sides are defined in self.padding_side:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
`>= 7.5` (Volta).
return_attention_mask:
(optional) Set to False to avoid returning attention mask (default: set to model specifics)
"""
# Load from model defaults
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(encoded_inputs["input_ids"])
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
needs_to_be_padded = (
padding_strategy != PaddingStrategy.DO_NOT_PAD and len(encoded_inputs["input_ids"]) != max_length
)
# Initialize attention mask if not present.
if return_attention_mask and "attention_mask" not in encoded_inputs:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"])
if needs_to_be_padded:
difference = max_length - len(encoded_inputs["input_ids"])
if self.padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = (
encoded_inputs["token_type_ids"] + [[self.pad_token_type_id] * 7] * difference
)
if "labels" in encoded_inputs:
encoded_inputs["labels"] = encoded_inputs["labels"] + [0] * difference
if "numeric_values" in encoded_inputs:
encoded_inputs["numeric_values"] = encoded_inputs["numeric_values"] + [float("nan")] * difference
if "numeric_values_scale" in encoded_inputs:
encoded_inputs["numeric_values_scale"] = (
encoded_inputs["numeric_values_scale"] + [1.0] * difference
)
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
encoded_inputs["input_ids"] = encoded_inputs["input_ids"] + [self.pad_token_id] * difference
elif self.padding_side == "left":
if return_attention_mask:
encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = [[self.pad_token_type_id] * 7] * difference + encoded_inputs[
"token_type_ids"
]
if "labels" in encoded_inputs:
encoded_inputs["labels"] = [0] * difference + encoded_inputs["labels"]
if "numeric_values" in encoded_inputs:
encoded_inputs["numeric_values"] = [float("nan")] * difference + encoded_inputs["numeric_values"]
if "numeric_values_scale" in encoded_inputs:
encoded_inputs["numeric_values_scale"] = [1.0] * difference + encoded_inputs[
"numeric_values_scale"
]
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
encoded_inputs["input_ids"] = [self.pad_token_id] * difference + encoded_inputs["input_ids"]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side))
return encoded_inputs
# Everything related to converting logits to predictions
def _get_cell_token_probs(self, probabilities, segment_ids, row_ids, column_ids):
for i, p in enumerate(probabilities):
segment_id = segment_ids[i]
col = column_ids[i] - 1
row = row_ids[i] - 1
if col >= 0 and row >= 0 and segment_id == 1:
yield i, p
def _get_mean_cell_probs(self, probabilities, segment_ids, row_ids, column_ids):
"""Computes average probability per cell, aggregating over tokens."""
coords_to_probs = collections.defaultdict(list)
for i, prob in self._get_cell_token_probs(probabilities, segment_ids, row_ids, column_ids):
col = column_ids[i] - 1
row = row_ids[i] - 1
coords_to_probs[(col, row)].append(prob)
return {coords: np.array(cell_probs).mean() for coords, cell_probs in coords_to_probs.items()}
def convert_logits_to_predictions(self, data, logits, logits_agg=None, cell_classification_threshold=0.5):
"""
Converts logits of [`TapasForQuestionAnswering`] to actual predicted answer coordinates and optional
aggregation indices.
The original implementation, on which this function is based, can be found
[here](https://github.com/google-research/tapas/blob/4908213eb4df7aa988573350278b44c4dbe3f71b/tapas/experiments/prediction_utils.py#L288).
Args:
data (`dict`):
Dictionary mapping features to actual values. Should be created using [`TapasTokenizer`].
logits (`torch.Tensor` or `tf.Tensor` of shape `(batch_size, sequence_length)`):
Tensor containing the logits at the token level.
logits_agg (`torch.Tensor` or `tf.Tensor` of shape `(batch_size, num_aggregation_labels)`, *optional*):
Tensor containing the aggregation logits.
cell_classification_threshold (`float`, *optional*, defaults to 0.5):
Threshold to be used for cell selection. All table cells for which their probability is larger than
this threshold will be selected.
Returns:
`tuple` comprising various elements depending on the inputs:
- predicted_answer_coordinates (`List[List[[tuple]]` of length `batch_size`): Predicted answer coordinates
as a list of lists of tuples. Each element in the list contains the predicted answer coordinates of a
single example in the batch, as a list of tuples. Each tuple is a cell, i.e. (row index, column index).
- predicted_aggregation_indices (`List[int]`of length `batch_size`, *optional*, returned when
`logits_aggregation` is provided): Predicted aggregation operator indices of the aggregation head.
"""
# converting to numpy arrays to work with PT/TF
logits = logits.numpy()
if logits_agg is not None:
logits_agg = logits_agg.numpy()
data = {key: value.numpy() for key, value in data.items() if key != "training"}
# input data is of type float32
# np.log(np.finfo(np.float32).max) = 88.72284
# Any value over 88.72284 will overflow when passed through the exponential, sending a warning
# We disable this warning by truncating the logits.
logits[logits < -88.7] = -88.7
# Compute probabilities from token logits
probabilities = 1 / (1 + np.exp(-logits)) * data["attention_mask"]
token_types = [
"segment_ids",
"column_ids",
"row_ids",
"prev_labels",
"column_ranks",
"inv_column_ranks",
"numeric_relations",
]
# collect input_ids, segment ids, row ids and column ids of batch. Shape (batch_size, seq_len)
input_ids = data["input_ids"]
segment_ids = data["token_type_ids"][:, :, token_types.index("segment_ids")]
row_ids = data["token_type_ids"][:, :, token_types.index("row_ids")]
column_ids = data["token_type_ids"][:, :, token_types.index("column_ids")]
# next, get answer coordinates for every example in the batch
num_batch = input_ids.shape[0]
predicted_answer_coordinates = []
for i in range(num_batch):
probabilities_example = probabilities[i].tolist()
segment_ids_example = segment_ids[i]
row_ids_example = row_ids[i]
column_ids_example = column_ids[i]
max_width = column_ids_example.max()
max_height = row_ids_example.max()
if max_width == 0 and max_height == 0:
continue
cell_coords_to_prob = self._get_mean_cell_probs(
probabilities_example,
segment_ids_example.tolist(),
row_ids_example.tolist(),
column_ids_example.tolist(),
)
# Select the answers above the classification threshold.
answer_coordinates = []
for col in range(max_width):
for row in range(max_height):
cell_prob = cell_coords_to_prob.get((col, row), None)
if cell_prob is not None:
if cell_prob > cell_classification_threshold:
answer_coordinates.append((row, col))
answer_coordinates = sorted(answer_coordinates)
predicted_answer_coordinates.append(answer_coordinates)
output = (predicted_answer_coordinates,)
if logits_agg is not None:
predicted_aggregation_indices = logits_agg.argmax(axis=-1)
output = (predicted_answer_coordinates, predicted_aggregation_indices.tolist())
return output
# End of everything related to converting logits to predictions
# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
class BasicTokenizer(object):
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *optional*):
Collection of tokens which will never be split during tokenization. Only has an effect when
`do_basic_tokenize=True`
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
Whether or not to tokenize Chinese characters.
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original BERT).
do_split_on_punc (`bool`, *optional*, defaults to `True`):
In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
the full context of the words, such as contractions.
"""
def __init__(
self,
do_lower_case=True,
never_split=None,
tokenize_chinese_chars=True,
strip_accents=None,
do_split_on_punc=True,
):
if never_split is None:
never_split = []
self.do_lower_case = do_lower_case
self.never_split = set(never_split)
self.tokenize_chinese_chars = tokenize_chinese_chars
self.strip_accents = strip_accents
self.do_split_on_punc = do_split_on_punc
def tokenize(self, text, never_split=None):
"""
Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.
Args:
never_split (`List[str]`, *optional*)
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
[`PreTrainedTokenizer.tokenize`]) List of token not to split.
"""
# union() returns a new set by concatenating the two sets.
never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
text = self._clean_text(text)
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
# characters in the vocabulary because Wikipedia does have some Chinese
# words in the English Wikipedia.).
if self.tokenize_chinese_chars:
text = self._tokenize_chinese_chars(text)
# prevents treating the same character with different unicode codepoints as different characters
unicode_normalized_text = unicodedata.normalize("NFC", text)
orig_tokens = whitespace_tokenize(unicode_normalized_text)
split_tokens = []
for token in orig_tokens:
if token not in never_split:
if self.do_lower_case:
token = token.lower()
if self.strip_accents is not False:
token = self._run_strip_accents(token)
elif self.strip_accents:
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token, never_split))
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output)
def _run_split_on_punc(self, text, never_split=None):
"""Splits punctuation on a piece of text."""
if not self.do_split_on_punc or (never_split is not None and text in never_split):
return [text]
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if _is_punctuation(char):
output.append([char])
start_new_word = True
else:
if start_new_word:
output.append([])
start_new_word = False
output[-1].append(char)
i += 1
return ["".join(x) for x in output]
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
else:
output.append(char)
return "".join(output)
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
or (cp >= 0x3400 and cp <= 0x4DBF) #
or (cp >= 0x20000 and cp <= 0x2A6DF) #
or (cp >= 0x2A700 and cp <= 0x2B73F) #
or (cp >= 0x2B740 and cp <= 0x2B81F) #
or (cp >= 0x2B820 and cp <= 0x2CEAF) #
or (cp >= 0xF900 and cp <= 0xFAFF)
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xFFFD or _is_control(char):
continue
if _is_whitespace(char):
output.append(" ")
else:
output.append(char)
return "".join(output)
# Copied from transformers.models.bert.tokenization_bert.WordpieceTokenizer
class WordpieceTokenizer(object):
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
tokenization using the given vocabulary.
For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.
Args:
text: A single token or whitespace separated tokens. This should have
already been passed through *BasicTokenizer*.
Returns:
A list of wordpiece tokens.
"""
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
while start < len(chars):
end = len(chars)
cur_substr = None
while start < end:
substr = "".join(chars[start:end])
if start > 0:
substr = "##" + substr
if substr in self.vocab:
cur_substr = substr
break
end -= 1
if cur_substr is None:
is_bad = True
break
sub_tokens.append(cur_substr)
start = end
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens
# Below: utilities for TAPAS tokenizer (independent from PyTorch/Tensorflow).
# This includes functions to parse numeric values (dates and numbers) from both the table and questions in order
# to create the column_ranks, inv_column_ranks, numeric_values, numeric values_scale and numeric_relations in
# prepare_for_model of TapasTokenizer.
# These are meant to be used in an academic setup, for production use cases Gold mine or Aqua should be used.
# taken from constants.py of the original implementation
# URL: https://github.com/google-research/tapas/blob/master/tapas/utils/constants.py
class Relation(enum.Enum):
HEADER_TO_CELL = 1 # Connects header to cell.
CELL_TO_HEADER = 2 # Connects cell to header.
QUERY_TO_HEADER = 3 # Connects query to headers.
QUERY_TO_CELL = 4 # Connects query to cells.
ROW_TO_CELL = 5 # Connects row to cells.
CELL_TO_ROW = 6 # Connects cells to row.
EQ = 7 # Annotation value is same as cell value
LT = 8 # Annotation value is less than cell value
GT = 9 # Annotation value is greater than cell value
@dataclass
class Date:
year: Optional[int] = None
month: Optional[int] = None
day: Optional[int] = None
@dataclass
class NumericValue:
float_value: Optional[float] = None
date: Optional[Date] = None
@dataclass
class NumericValueSpan:
begin_index: int = None
end_index: int = None
values: List[NumericValue] = None
@dataclass
class Cell:
text: Text
numeric_value: Optional[NumericValue] = None
@dataclass
class Question:
original_text: Text # The original raw question string.
text: Text # The question string after normalization.
numeric_spans: Optional[List[NumericValueSpan]] = None
# Below: all functions from number_utils.py as well as 2 functions (namely get_all_spans and normalize_for_match)
# from text_utils.py of the original implementation. URL's:
# - https://github.com/google-research/tapas/blob/master/tapas/utils/number_utils.py
# - https://github.com/google-research/tapas/blob/master/tapas/utils/text_utils.py
# Constants for parsing date expressions.
# Masks that specify (by a bool) which of (year, month, day) will be populated.
_DateMask = collections.namedtuple("_DateMask", ["year", "month", "day"])
_YEAR = _DateMask(True, False, False)
_YEAR_MONTH = _DateMask(True, True, False)
_YEAR_MONTH_DAY = _DateMask(True, True, True)
_MONTH = _DateMask(False, True, False)
_MONTH_DAY = _DateMask(False, True, True)
# Pairs of patterns to pass to 'datetime.strptime' and masks specifying which
# fields will be set by the corresponding pattern.
_DATE_PATTERNS = (
("%B", _MONTH),
("%Y", _YEAR),
("%Ys", _YEAR),
("%b %Y", _YEAR_MONTH),
("%B %Y", _YEAR_MONTH),
("%B %d", _MONTH_DAY),
("%b %d", _MONTH_DAY),
("%d %b", _MONTH_DAY),
("%d %B", _MONTH_DAY),
("%B %d, %Y", _YEAR_MONTH_DAY),
("%d %B %Y", _YEAR_MONTH_DAY),
("%m-%d-%Y", _YEAR_MONTH_DAY),
("%Y-%m-%d", _YEAR_MONTH_DAY),
("%Y-%m", _YEAR_MONTH),
("%B %Y", _YEAR_MONTH),
("%d %b %Y", _YEAR_MONTH_DAY),
("%Y-%m-%d", _YEAR_MONTH_DAY),
("%b %d, %Y", _YEAR_MONTH_DAY),
("%d.%m.%Y", _YEAR_MONTH_DAY),
("%A, %b %d", _MONTH_DAY),
("%A, %B %d", _MONTH_DAY),
)
# This mapping is used to convert date patterns to regex patterns.
_FIELD_TO_REGEX = (
("%A", r"\w+"), # Weekday as locale’s full name.
("%B", r"\w+"), # Month as locale’s full name.
("%Y", r"\d{4}"), # Year with century as a decimal number.
("%b", r"\w{3}"), # Month as locale’s abbreviated name.
("%d", r"\d{1,2}"), # Day of the month as a zero-padded decimal number.
("%m", r"\d{1,2}"), # Month as a zero-padded decimal number.
)
def _process_date_pattern(dp):
"""Compute a regex for each date pattern to use as a prefilter."""
pattern, mask = dp
regex = pattern
regex = regex.replace(".", re.escape("."))
regex = regex.replace("-", re.escape("-"))
regex = regex.replace(" ", r"\s+")
for field, field_regex in _FIELD_TO_REGEX:
regex = regex.replace(field, field_regex)
# Make sure we didn't miss any of the fields.
assert "%" not in regex, regex
return pattern, mask, re.compile("^" + regex + "$")
def _process_date_patterns():
return tuple(_process_date_pattern(dp) for dp in _DATE_PATTERNS)
_PROCESSED_DATE_PATTERNS = _process_date_patterns()
_MAX_DATE_NGRAM_SIZE = 5
# Following DynSp:
# https://github.com/Microsoft/DynSP/blob/master/util.py#L414.
_NUMBER_WORDS = [
"zero",
"one",
"two",
"three",
"four",
"five",
"six",
"seven",
"eight",
"nine",
"ten",
"eleven",
"twelve",
]
_ORDINAL_WORDS = [
"zeroth",
"first",
"second",
"third",
"fourth",
"fith",
"sixth",
"seventh",
"eighth",
"ninth",
"tenth",
"eleventh",
"twelfth",
]
_ORDINAL_SUFFIXES = ["st", "nd", "rd", "th"]
_NUMBER_PATTERN = re.compile(r"((^|\s)[+-])?((\.\d+)|(\d+(,\d\d\d)*(\.\d*)?))")
# Following DynSp:
# https://github.com/Microsoft/DynSP/blob/master/util.py#L293.
_MIN_YEAR = 1700
_MAX_YEAR = 2016
_INF = float("INF")
def _get_numeric_value_from_date(date, mask):
"""Converts date (datetime Python object) to a NumericValue object with a Date object value."""
if date.year < _MIN_YEAR or date.year > _MAX_YEAR:
raise ValueError(f"Invalid year: {date.year}")
new_date = Date()
if mask.year:
new_date.year = date.year
if mask.month:
new_date.month = date.month
if mask.day:
new_date.day = date.day
return NumericValue(date=new_date)
def _get_span_length_key(span):
"""Sorts span by decreasing length first and increasing first index second."""
return span[1] - span[0], -span[0]
def _get_numeric_value_from_float(value):
"""Converts float (Python) to a NumericValue object with a float value."""
return NumericValue(float_value=value)
# Doesn't parse ordinal expressions such as '18th of february 1655'.
def _parse_date(text):
"""Attempts to format a text as a standard date string (yyyy-mm-dd)."""
text = re.sub(r"Sept\b", "Sep", text)
for in_pattern, mask, regex in _PROCESSED_DATE_PATTERNS:
if not regex.match(text):
continue
try:
date = datetime.datetime.strptime(text, in_pattern).date()
except ValueError:
continue
try:
return _get_numeric_value_from_date(date, mask)
except ValueError:
continue
return None
def _parse_number(text):
"""Parses simple cardinal and ordinals numbers."""
for suffix in _ORDINAL_SUFFIXES:
if text.endswith(suffix):
text = text[: -len(suffix)]
break
text = text.replace(",", "")
try:
value = float(text)
except ValueError:
return None
if math.isnan(value):
return None
if value == _INF:
return None
return value
def get_all_spans(text, max_ngram_length):
"""
Split a text into all possible ngrams up to 'max_ngram_length'. Split points are white space and punctuation.
Args:
text: Text to split.
max_ngram_length: maximal ngram length.
Yields:
Spans, tuples of begin-end index.
"""
start_indexes = []
for index, char in enumerate(text):
if not char.isalnum():
continue
if index == 0 or not text[index - 1].isalnum():
start_indexes.append(index)
if index + 1 == len(text) or not text[index + 1].isalnum():
for start_index in start_indexes[-max_ngram_length:]:
yield start_index, index + 1
def normalize_for_match(text):
return " ".join(text.lower().split())
def format_text(text):
"""Lowercases and strips punctuation."""
text = text.lower().strip()
if text == "n/a" or text == "?" or text == "nan":
text = EMPTY_TEXT
text = re.sub(r"[^\w\d]+", " ", text).replace("_", " ")
text = " ".join(text.split())
text = text.strip()
if text:
return text
return EMPTY_TEXT
def parse_text(text):
"""
Extracts longest number and date spans.
Args:
text: text to annotate
Returns:
List of longest numeric value spans.
"""
span_dict = collections.defaultdict(list)
for match in _NUMBER_PATTERN.finditer(text):
span_text = text[match.start() : match.end()]
number = _parse_number(span_text)
if number is not None:
span_dict[match.span()].append(_get_numeric_value_from_float(number))
for begin_index, end_index in get_all_spans(text, max_ngram_length=1):
if (begin_index, end_index) in span_dict:
continue
span_text = text[begin_index:end_index]
number = _parse_number(span_text)
if number is not None:
span_dict[begin_index, end_index].append(_get_numeric_value_from_float(number))
for number, word in enumerate(_NUMBER_WORDS):
if span_text == word:
span_dict[begin_index, end_index].append(_get_numeric_value_from_float(float(number)))
break
for number, word in enumerate(_ORDINAL_WORDS):
if span_text == word:
span_dict[begin_index, end_index].append(_get_numeric_value_from_float(float(number)))
break
for begin_index, end_index in get_all_spans(text, max_ngram_length=_MAX_DATE_NGRAM_SIZE):
span_text = text[begin_index:end_index]
date = _parse_date(span_text)
if date is not None:
span_dict[begin_index, end_index].append(date)
spans = sorted(span_dict.items(), key=lambda span_value: _get_span_length_key(span_value[0]), reverse=True)
selected_spans = []
for span, value in spans:
for selected_span, _ in selected_spans:
if selected_span[0] <= span[0] and span[1] <= selected_span[1]:
break
else:
selected_spans.append((span, value))
selected_spans.sort(key=lambda span_value: span_value[0][0])
numeric_value_spans = []
for span, values in selected_spans:
numeric_value_spans.append(NumericValueSpan(begin_index=span[0], end_index=span[1], values=values))
return numeric_value_spans
# Below: all functions from number_annotation_utils.py and 2 functions (namely filter_invalid_unicode
# and filter_invalid_unicode_from_table) from text_utils.py of the original implementation. URL's:
# - https://github.com/google-research/tapas/blob/master/tapas/utils/number_annotation_utils.py
# - https://github.com/google-research/tapas/blob/master/tapas/utils/text_utils.py
_PrimitiveNumericValue = Union[float, Tuple[Optional[float], Optional[float], Optional[float]]]
_SortKeyFn = Callable[[NumericValue], Tuple[float, Ellipsis]]
_DATE_TUPLE_SIZE = 3
EMPTY_TEXT = "EMPTY"
NUMBER_TYPE = "number"
DATE_TYPE = "date"
def _get_value_type(numeric_value):
if numeric_value.float_value is not None:
return NUMBER_TYPE
elif numeric_value.date is not None:
return DATE_TYPE
raise ValueError(f"Unknown type: {numeric_value}")
def _get_value_as_primitive_value(numeric_value):
"""Maps a NumericValue proto to a float or tuple of float."""
if numeric_value.float_value is not None:
return numeric_value.float_value
if numeric_value.date is not None:
date = numeric_value.date
value_tuple = [None, None, None]
# All dates fields are cased to float to produce a simple primitive value.
if date.year is not None:
value_tuple[0] = float(date.year)
if date.month is not None:
value_tuple[1] = float(date.month)
if date.day is not None:
value_tuple[2] = float(date.day)
return tuple(value_tuple)
raise ValueError(f"Unknown type: {numeric_value}")
def _get_all_types(numeric_values):
return {_get_value_type(value) for value in numeric_values}
def get_numeric_sort_key_fn(numeric_values):
"""
Creates a function that can be used as a sort key or to compare the values. Maps to primitive types and finds the
biggest common subset. Consider the values "05/05/2010" and "August 2007". With the corresponding primitive values
(2010.,5.,5.) and (2007.,8., None). These values can be compared by year and date so we map to the sequence (2010.,
5.), (2007., 8.). If we added a third value "2006" with primitive value (2006., None, None), we could only compare
by the year so we would map to (2010.,), (2007.,) and (2006.,).
Args:
numeric_values: Values to compare
Returns:
A function that can be used as a sort key function (mapping numeric values to a comparable tuple)
Raises:
ValueError if values don't have a common type or are not comparable.
"""
value_types = _get_all_types(numeric_values)
if len(value_types) != 1:
raise ValueError(f"No common value type in {numeric_values}")
value_type = next(iter(value_types))
if value_type == NUMBER_TYPE:
# Primitive values are simple floats, nothing to do here.
return _get_value_as_primitive_value
# The type can only be Date at this point which means the primitive type
# is a float triple.
valid_indexes = set(range(_DATE_TUPLE_SIZE))
for numeric_value in numeric_values:
value = _get_value_as_primitive_value(numeric_value)
assert isinstance(value, tuple)
for tuple_index, inner_value in enumerate(value):
if inner_value is None:
valid_indexes.discard(tuple_index)
if not valid_indexes:
raise ValueError(f"No common value in {numeric_values}")
def _sort_key_fn(numeric_value):
value = _get_value_as_primitive_value(numeric_value)
return tuple(value[index] for index in valid_indexes)
return _sort_key_fn
def _consolidate_numeric_values(row_index_to_values, min_consolidation_fraction, debug_info):
"""
Finds the most common numeric values in a column and returns them
Args:
row_index_to_values:
For each row index all the values in that cell.
min_consolidation_fraction:
Fraction of cells that need to have consolidated value.
debug_info:
Additional information only used for logging
Returns:
For each row index the first value that matches the most common value. Rows that don't have a matching value
are dropped. Empty list if values can't be consolidated.
"""
type_counts = collections.Counter()
for numeric_values in row_index_to_values.values():
type_counts.update(_get_all_types(numeric_values))
if not type_counts:
return {}
max_count = max(type_counts.values())
if max_count < len(row_index_to_values) * min_consolidation_fraction:
# logging.log_every_n(logging.INFO, f'Can\'t consolidate types: {debug_info} {row_index_to_values} {max_count}', 100)
return {}
valid_types = set()
for value_type, count in type_counts.items():
if count == max_count:
valid_types.add(value_type)
if len(valid_types) > 1:
assert DATE_TYPE in valid_types
max_type = DATE_TYPE
else:
max_type = next(iter(valid_types))
new_row_index_to_value = {}
for index, values in row_index_to_values.items():
# Extract the first matching value.
for value in values:
if _get_value_type(value) == max_type:
new_row_index_to_value[index] = value
break
return new_row_index_to_value
def _get_numeric_values(text):
"""Parses text and returns numeric values."""
numeric_spans = parse_text(text)
return itertools.chain(*(span.values for span in numeric_spans))
def _get_column_values(table, col_index):
"""
Parses text in column and returns a dict mapping row_index to values. This is the _get_column_values function from
number_annotation_utils.py of the original implementation
Args:
table: Pandas dataframe
col_index: integer, indicating the index of the column to get the numeric values of
"""
index_to_values = {}
for row_index, row in table.iterrows():
text = normalize_for_match(row[col_index].text)
index_to_values[row_index] = list(_get_numeric_values(text))
return index_to_values
def get_numeric_relation(value, other_value, sort_key_fn):
"""Compares two values and returns their relation or None."""
value = sort_key_fn(value)
other_value = sort_key_fn(other_value)
if value == other_value:
return Relation.EQ
if value < other_value:
return Relation.LT
if value > other_value:
return Relation.GT
return None
def add_numeric_values_to_question(question):
"""Adds numeric value spans to a question."""
original_text = question
question = normalize_for_match(question)
numeric_spans = parse_text(question)
return Question(original_text=original_text, text=question, numeric_spans=numeric_spans)
def filter_invalid_unicode(text):
"""Return an empty string and True if 'text' is in invalid unicode."""
return ("", True) if isinstance(text, bytes) else (text, False)
def filter_invalid_unicode_from_table(table):
"""
Removes invalid unicode from table. Checks whether a table cell text contains an invalid unicode encoding. If yes,
reset the table cell text to an empty str and log a warning for each invalid cell
Args:
table: table to clean.
"""
# to do: add table id support
if not hasattr(table, "table_id"):
table.table_id = 0
for row_index, row in table.iterrows():
for col_index, cell in enumerate(row):
cell, is_invalid = filter_invalid_unicode(cell)
if is_invalid:
logging.warning(
f"Scrub an invalid table body @ table_id: {table.table_id}, row_index: {row_index}, "
f"col_index: {col_index}",
)
for col_index, column in enumerate(table.columns):
column, is_invalid = filter_invalid_unicode(column)
if is_invalid:
logging.warning(f"Scrub an invalid table header @ table_id: {table.table_id}, col_index: {col_index}")
def add_numeric_table_values(table, min_consolidation_fraction=0.7, debug_info=None):
"""
Parses text in table column-wise and adds the consolidated values. Consolidation refers to finding values with a
common types (date or number)
Args:
table:
Table to annotate.
min_consolidation_fraction:
Fraction of cells in a column that need to have consolidated value.
debug_info:
Additional information used for logging.
"""
table = table.copy()
# First, filter table on invalid unicode
filter_invalid_unicode_from_table(table)
# Second, replace cell values by Cell objects
for row_index, row in table.iterrows():
for col_index, cell in enumerate(row):
table.iloc[row_index, col_index] = Cell(text=cell)
# Third, add numeric_value attributes to these Cell objects
for col_index, column in enumerate(table.columns):
column_values = _consolidate_numeric_values(
_get_column_values(table, col_index),
min_consolidation_fraction=min_consolidation_fraction,
debug_info=(debug_info, column),
)
for row_index, numeric_value in column_values.items():
table.iloc[row_index, col_index].numeric_value = numeric_value
return table
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/qdqbert/modeling_qdqbert.py | # coding=utf-8
# Copyright 2021 NVIDIA Corporation and The HuggingFace Team.
# Copyright (c) 2018-2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch QDQBERT model."""
import math
import os
import warnings
from typing import Dict, List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
MaskedLMOutput,
MultipleChoiceModelOutput,
NextSentencePredictorOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_pytorch_quantization_available,
logging,
replace_return_docstrings,
requires_backends,
)
from .configuration_qdqbert import QDQBertConfig
logger = logging.get_logger(__name__)
# soft dependency
if is_pytorch_quantization_available():
try:
from pytorch_quantization import nn as quant_nn
from pytorch_quantization.nn.modules.tensor_quantizer import TensorQuantizer
except OSError:
logger.error(
"QDQBERT model are not usable since `pytorch_quantization` can't be loaded. Please try to reinstall it"
" following the instructions here:"
" https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization."
)
_CHECKPOINT_FOR_DOC = "bert-base-uncased"
_CONFIG_FOR_DOC = "QDQBertConfig"
QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"bert-base-uncased",
# See all BERT models at https://huggingface.co/models?filter=bert
]
def load_tf_weights_in_qdqbert(model, tf_checkpoint_path):
"""Load tf checkpoints in a pytorch model."""
try:
import re
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions."
)
raise
tf_path = os.path.abspath(tf_checkpoint_path)
logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
logger.info(f"Loading TF weight {name} with shape {shape}")
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array)
for name, array in zip(names, arrays):
name = name.split("/")
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model
if any(
n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
for n in name
):
logger.info(f"Skipping {'/'.join(name)}")
continue
pointer = model
for m_name in name:
if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
scope_names = re.split(r"_(\d+)", m_name)
else:
scope_names = [m_name]
if scope_names[0] == "kernel" or scope_names[0] == "gamma":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
pointer = getattr(pointer, "bias")
elif scope_names[0] == "output_weights":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "squad":
pointer = getattr(pointer, "classifier")
else:
try:
pointer = getattr(pointer, scope_names[0])
except AttributeError:
logger.info(f"Skipping {'/'.join(name)}")
continue
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
if m_name[-11:] == "_embeddings":
pointer = getattr(pointer, "weight")
elif m_name == "kernel":
array = np.transpose(array)
try:
if pointer.shape != array.shape:
raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
logger.info(f"Initialize PyTorch weight {name}")
pointer.data = torch.from_numpy(array)
return model
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings with Bert -> QDQBert
class QDQBertEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.register_buffer(
"token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
past_key_values_length: int = 0,
) -> torch.Tensor:
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class QDQBertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = quant_nn.QuantLinear(config.hidden_size, self.all_head_size)
self.key = quant_nn.QuantLinear(config.hidden_size, self.all_head_size)
self.value = quant_nn.QuantLinear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
self.is_decoder = config.is_decoder
self.matmul_q_input_quantizer = TensorQuantizer(quant_nn.QuantLinear.default_quant_desc_input)
self.matmul_k_input_quantizer = TensorQuantizer(quant_nn.QuantLinear.default_quant_desc_input)
self.matmul_v_input_quantizer = TensorQuantizer(quant_nn.QuantLinear.default_quant_desc_input)
self.matmul_a_input_quantizer = TensorQuantizer(quant_nn.QuantLinear.default_quant_desc_input)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(
self.matmul_q_input_quantizer(query_layer), self.matmul_k_input_quantizer(key_layer.transpose(-1, -2))
)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
seq_length = hidden_states.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in QDQBertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.Softmax(dim=-1)(attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(
self.matmul_a_input_quantizer(attention_probs), self.matmul_v_input_quantizer(value_layer)
)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
class QDQBertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
# Quantize Linear layer
self.dense = quant_nn.QuantLinear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# Quantize the inputs to the residual add
self.add_local_input_quantizer = TensorQuantizer(quant_nn.QuantLinear.default_quant_desc_input)
self.add_residual_input_quantizer = TensorQuantizer(quant_nn.QuantLinear.default_quant_desc_input)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
# Quantize the inputs to the residual add
add_local = self.add_local_input_quantizer(hidden_states)
add_residual = self.add_residual_input_quantizer(input_tensor)
hidden_states = self.LayerNorm(add_local + add_residual)
return hidden_states
# Based on transformers.models.bert.modeling_bert.BertAttention with Bert -> QDQBert
class QDQBertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = QDQBertSelfAttention(config)
self.output = QDQBertSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
class QDQBertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
# Quantize Linear layer
self.dense = quant_nn.QuantLinear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class QDQBertOutput(nn.Module):
def __init__(self, config):
super().__init__()
# Quantize Linear layer
self.dense = quant_nn.QuantLinear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# Quantize the inputs to the residual add
self.add_local_input_quantizer = TensorQuantizer(quant_nn.QuantLinear.default_quant_desc_input)
self.add_residual_input_quantizer = TensorQuantizer(quant_nn.QuantLinear.default_quant_desc_input)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
# Quantize the inputs to the residual add
add_local = self.add_local_input_quantizer(hidden_states)
add_residual = self.add_residual_input_quantizer(input_tensor)
hidden_states = self.LayerNorm(add_local + add_residual)
return hidden_states
# Based on transformers.models.bert.modeling_bert.BertLayer with Bert -> QDQBert
class QDQBertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_len_dim = 1
self.attention = QDQBertAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
self.crossattention = QDQBertAttention(config)
self.intermediate = QDQBertIntermediate(config)
self.output = QDQBertOutput(config)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and encoder_hidden_states is not None:
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
" by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
cross_attn_past_key_value,
output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
layer_output = self.feed_forward_chunk(attention_output)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
# Based on transformers.models.bert.modeling_bert.BertEncoder with Bert -> QDQBert
class QDQBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([QDQBertLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_values[i] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert -> QDQBert
class QDQBertPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert -> QDQBert
class QDQBertPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
# Based on transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert -> QDQBert
class QDQBertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = QDQBertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
# Based on transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert -> QDQBert
class QDQBertOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = QDQBertLMPredictionHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores
# Copied from transformers.models.bert.modeling_bert.BertOnlyNSPHead with Bert -> QDQBert
class QDQBertOnlyNSPHead(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score
# Based on transformers.models.bert.modeling_bert.BertPreTrainingHeads with Bert -> QDQBert
class QDQBertPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = QDQBertLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predictions(sequence_output)
seq_relationship_score = self.seq_relationship(pooled_output)
return prediction_scores, seq_relationship_score
# Based on transformers.models.bert.modeling_bert.BertPreTrainedModel with Bert -> QDQBert
class QDQBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = QDQBertConfig
load_tf_weights = load_tf_weights_in_qdqbert
base_model_prefix = "bert"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
QDQBERT_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`QDQBertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
QDQBERT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare QDQBERT Model transformer outputting raw hidden-states without any specific head on top.",
QDQBERT_START_DOCSTRING,
)
class QDQBertModel(QDQBertPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
"""
def __init__(self, config, add_pooling_layer: bool = True):
requires_backends(self, "pytorch_quantization")
super().__init__(config)
self.config = config
self.embeddings = QDQBertEmbeddings(config)
self.encoder = QDQBertEncoder(config)
self.pooler = QDQBertPooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(QDQBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPoolingAndCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
batch_size, seq_length = input_shape
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size, seq_length = input_shape
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
@add_start_docstrings(
"""QDQBERT Model with a `language modeling` head on top for CLM fine-tuning.""", QDQBERT_START_DOCSTRING
)
class QDQBertLMHeadModel(QDQBertPreTrainedModel):
_tied_weights_keys = ["predictions.decoder.weight", "predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `QDQBertLMHeadModel` as a standalone, add `is_decoder=True.`")
self.bert = QDQBertModel(config, add_pooling_layer=False)
self.cls = QDQBertOnlyMLMHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward(QDQBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.LongTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, QDQBertLMHeadModel, QDQBertConfig
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
>>> config = QDQBertConfig.from_pretrained("bert-base-cased")
>>> config.is_decoder = True
>>> model = QDQBertLMHeadModel.from_pretrained("bert-base-cased", config=config)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
lm_loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((lm_loss,) + output) if lm_loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=lm_loss,
logits=prediction_scores,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
def prepare_inputs_for_generation(
self,
input_ids: Optional[torch.LongTensor],
past_key_values=None,
attention_mask: Optional[torch.Tensor] = None,
**model_kwargs,
):
input_shape = input_ids.shape
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_shape)
# cut decoder_input_ids if past_key_values is used
if past_key_values is not None:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_length:]
return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values}
def _reorder_cache(self, past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
@add_start_docstrings("""QDQBERT Model with a `language modeling` head on top.""", QDQBERT_START_DOCSTRING)
class QDQBertForMaskedLM(QDQBertPreTrainedModel):
_tied_weights_keys = ["predictions.decoder.weight", "predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `QDQBertForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
self.bert = QDQBertModel(config, add_pooling_layer=False)
self.cls = QDQBertOnlyMLMHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward(QDQBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, MaskedLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss() # -100 index = padding token
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self, input_ids: torch.LongTensor, attention_mask: Optional[torch.FloatTensor] = None, **model_kwargs
):
input_shape = input_ids.shape
effective_batch_size = input_shape[0]
# add a dummy token
if self.config.pad_token_id is None:
raise ValueError("The PAD token should be defined for generation")
attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
dummy_token = torch.full(
(effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
)
input_ids = torch.cat([input_ids, dummy_token], dim=1)
return {"input_ids": input_ids, "attention_mask": attention_mask}
@add_start_docstrings(
"""Bert Model with a `next sentence prediction (classification)` head on top.""",
QDQBERT_START_DOCSTRING,
)
class QDQBertForNextSentencePrediction(QDQBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = QDQBertModel(config)
self.cls = QDQBertOnlyNSPHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(QDQBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[Tuple, NextSentencePredictorOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
(see `input_ids` docstring). Indices should be in `[0, 1]`:
- 0 indicates sequence B is a continuation of sequence A,
- 1 indicates sequence B is a random sequence.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, QDQBertForNextSentencePrediction
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
>>> model = QDQBertForNextSentencePrediction.from_pretrained("bert-base-uncased")
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
>>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
>>> outputs = model(**encoding, labels=torch.LongTensor([1]))
>>> logits = outputs.logits
>>> assert logits[0, 0] < logits[0, 1] # next sentence was random
```"""
if "next_sentence_label" in kwargs:
warnings.warn(
"The `next_sentence_label` argument is deprecated and will be removed in a future version, use"
" `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("next_sentence_label")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
seq_relationship_scores = self.cls(pooled_output)
next_sentence_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1))
if not return_dict:
output = (seq_relationship_scores,) + outputs[2:]
return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
return NextSentencePredictorOutput(
loss=next_sentence_loss,
logits=seq_relationship_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
""",
QDQBERT_START_DOCSTRING,
)
class QDQBertForSequenceClassification(QDQBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.bert = QDQBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(QDQBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
Bert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
QDQBERT_START_DOCSTRING,
)
class QDQBertForMultipleChoice(QDQBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = QDQBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(QDQBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, MultipleChoiceModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
QDQBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
QDQBERT_START_DOCSTRING,
)
class QDQBertForTokenClassification(QDQBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = QDQBertModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(QDQBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
QDQBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
QDQBERT_START_DOCSTRING,
)
class QDQBertForQuestionAnswering(QDQBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = QDQBertModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(QDQBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/qdqbert/__init__.py | # Copyright 2021 NVIDIA Corporation and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {"configuration_qdqbert": ["QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "QDQBertConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_qdqbert"] = [
"QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"QDQBertForMaskedLM",
"QDQBertForMultipleChoice",
"QDQBertForNextSentencePrediction",
"QDQBertForQuestionAnswering",
"QDQBertForSequenceClassification",
"QDQBertForTokenClassification",
"QDQBertLayer",
"QDQBertLMHeadModel",
"QDQBertModel",
"QDQBertPreTrainedModel",
"load_tf_weights_in_qdqbert",
]
if TYPE_CHECKING:
from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_qdqbert import (
QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
QDQBertForMaskedLM,
QDQBertForMultipleChoice,
QDQBertForNextSentencePrediction,
QDQBertForQuestionAnswering,
QDQBertForSequenceClassification,
QDQBertForTokenClassification,
QDQBertLayer,
QDQBertLMHeadModel,
QDQBertModel,
QDQBertPreTrainedModel,
load_tf_weights_in_qdqbert,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/qdqbert/configuration_qdqbert.py | # coding=utf-8
# Copyright 2021 NVIDIA Corporation and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" QDQBERT model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
# QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}
class QDQBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`QDQBertModel`]. It is used to instantiate an
QDQBERT model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the BERT
[bert-base-uncased](https://huggingface.co/bert-base-uncased) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the QDQBERT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`QDQBertModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimension of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`QDQBertModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
is_decoder (`bool`, *optional*, defaults to `False`):
Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
Examples:
```python
>>> from transformers import QDQBertModel, QDQBertConfig
>>> # Initializing a QDQBERT bert-base-uncased style configuration
>>> configuration = QDQBertConfig()
>>> # Initializing a model from the bert-base-uncased style configuration
>>> model = QDQBertModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "qdqbert"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
use_cache=True,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.type_vocab_size = type_vocab_size
self.layer_norm_eps = layer_norm_eps
self.use_cache = use_cache
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/xlm_roberta_xl/convert_xlm_roberta_xl_original_pytorch_checkpoint_to_pytorch.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert RoBERTa checkpoint."""
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("1.0.0a"):
raise Exception("requires fairseq >= 1.0.0a")
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
SAMPLE_TEXT = "Hello world! cécé herlolip"
def convert_xlm_roberta_xl_checkpoint_to_pytorch(
roberta_checkpoint_path: str, pytorch_dump_folder_path: str, classification_head: bool
):
"""
Copy/paste/tweak roberta's weights to our BERT structure.
"""
roberta = FairseqRobertaModel.from_pretrained(roberta_checkpoint_path)
roberta.eval() # disable dropout
roberta_sent_encoder = roberta.model.encoder.sentence_encoder
config = XLMRobertaConfig(
vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,
hidden_size=roberta.cfg.model.encoder_embed_dim,
num_hidden_layers=roberta.cfg.model.encoder_layers,
num_attention_heads=roberta.cfg.model.encoder_attention_heads,
intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim,
max_position_embeddings=514,
type_vocab_size=1,
layer_norm_eps=1e-5, # PyTorch default used in fairseq
)
if classification_head:
config.num_labels = roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
print("Our RoBERTa config:", config)
model = XLMRobertaXLForSequenceClassification(config) if classification_head else XLMRobertaXLForMaskedLM(config)
model.eval()
# Now let's copy all the weights.
# Embeddings
model.roberta.embeddings.word_embeddings.weight = roberta_sent_encoder.embed_tokens.weight
model.roberta.embeddings.position_embeddings.weight = roberta_sent_encoder.embed_positions.weight
model.roberta.embeddings.token_type_embeddings.weight.data = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight
) # just zero them out b/c RoBERTa doesn't use them.
model.roberta.encoder.LayerNorm.weight = roberta_sent_encoder.layer_norm.weight
model.roberta.encoder.LayerNorm.bias = roberta_sent_encoder.layer_norm.bias
for i in range(config.num_hidden_layers):
# Encoder: start of layer
layer: BertLayer = model.roberta.encoder.layer[i]
roberta_layer: TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i]
attention: RobertaAttention = layer.attention
attention.self_attn_layer_norm.weight = roberta_layer.self_attn_layer_norm.weight
attention.self_attn_layer_norm.bias = roberta_layer.self_attn_layer_norm.bias
# self attention
self_attn: BertSelfAttention = layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size))
)
self_attn.query.weight.data = roberta_layer.self_attn.q_proj.weight
self_attn.query.bias.data = roberta_layer.self_attn.q_proj.bias
self_attn.key.weight.data = roberta_layer.self_attn.k_proj.weight
self_attn.key.bias.data = roberta_layer.self_attn.k_proj.bias
self_attn.value.weight.data = roberta_layer.self_attn.v_proj.weight
self_attn.value.bias.data = roberta_layer.self_attn.v_proj.bias
# self-attention output
self_output: BertSelfOutput = layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
self_output.dense.weight = roberta_layer.self_attn.out_proj.weight
self_output.dense.bias = roberta_layer.self_attn.out_proj.bias
# this one is final layer norm
layer.LayerNorm.weight = roberta_layer.final_layer_norm.weight
layer.LayerNorm.bias = roberta_layer.final_layer_norm.bias
# intermediate
intermediate: BertIntermediate = layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fc1.weight.shape
intermediate.dense.weight = roberta_layer.fc1.weight
intermediate.dense.bias = roberta_layer.fc1.bias
# output
bert_output: BertOutput = layer.output
assert bert_output.dense.weight.shape == roberta_layer.fc2.weight.shape
bert_output.dense.weight = roberta_layer.fc2.weight
bert_output.dense.bias = roberta_layer.fc2.bias
# end of layer
if classification_head:
model.classifier.dense.weight = roberta.model.classification_heads["mnli"].dense.weight
model.classifier.dense.bias = roberta.model.classification_heads["mnli"].dense.bias
model.classifier.out_proj.weight = roberta.model.classification_heads["mnli"].out_proj.weight
model.classifier.out_proj.bias = roberta.model.classification_heads["mnli"].out_proj.bias
else:
# LM Head
model.lm_head.dense.weight = roberta.model.encoder.lm_head.dense.weight
model.lm_head.dense.bias = roberta.model.encoder.lm_head.dense.bias
model.lm_head.layer_norm.weight = roberta.model.encoder.lm_head.layer_norm.weight
model.lm_head.layer_norm.bias = roberta.model.encoder.lm_head.layer_norm.bias
model.lm_head.decoder.weight = roberta.model.encoder.lm_head.weight
model.lm_head.decoder.bias = roberta.model.encoder.lm_head.bias
# Let's check that we get the same results.
input_ids: torch.Tensor = roberta.encode(SAMPLE_TEXT).unsqueeze(0) # batch of size 1
our_output = model(input_ids)[0]
if classification_head:
their_output = roberta.model.classification_heads["mnli"](roberta.extract_features(input_ids))
else:
their_output = roberta.model(input_ids)[0]
print(our_output.shape, their_output.shape)
max_absolute_diff = torch.max(torch.abs(our_output - their_output)).item()
print(f"max_absolute_diff = {max_absolute_diff}") # ~ 1e-7
success = torch.allclose(our_output, their_output, atol=1e-3)
print("Do both models output the same tensors?", "🔥" if success else "💩")
if not success:
raise Exception("Something went wRoNg")
pathlib.Path(pytorch_dump_folder_path).mkdir(parents=True, exist_ok=True)
print(f"Saving model to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--classification_head", action="store_true", help="Whether to convert a final classification head."
)
args = parser.parse_args()
convert_xlm_roberta_xl_checkpoint_to_pytorch(
args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/xlm_roberta_xl/configuration_xlm_roberta_xl.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" XLM_ROBERTa_XL configuration"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
"facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class XLMRobertaXLConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`XLMRobertaXLModel`] or a [`TFXLMRobertaXLModel`].
It is used to instantiate a XLM_ROBERTA_XL model according to the specified arguments, defining the model
architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the
XLM_ROBERTA_XL [facebook/xlm-roberta-xl](https://huggingface.co/facebook/xlm-roberta-xl) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 250880):
Vocabulary size of the XLM_ROBERTA_XL model. Defines the number of different tokens that can be represented
by the `inputs_ids` passed when calling [`XLMRobertaXLModel`].
hidden_size (`int`, *optional*, defaults to 2560):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 36):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 10240):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 514):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 1):
The vocabulary size of the `token_type_ids` passed when calling [`XLMRobertaXLModel`] or
[`TFXLMRobertaXLModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
classifier_dropout (`float`, *optional*):
The dropout ratio for the classification head.
Examples:
```python
>>> from transformers import XLMRobertaXLConfig, XLMRobertaXLModel
>>> # Initializing a XLM_ROBERTA_XL bert-base-uncased style configuration
>>> configuration = XLMRobertaXLConfig()
>>> # Initializing a model (with random weights) from the bert-base-uncased style configuration
>>> model = XLMRobertaXLModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "xlm-roberta-xl"
def __init__(
self,
vocab_size=250880,
hidden_size=2560,
num_hidden_layers=36,
num_attention_heads=32,
intermediate_size=10240,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=514,
type_vocab_size=1,
initializer_range=0.02,
layer_norm_eps=1e-05,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
position_embedding_type="absolute",
use_cache=True,
classifier_dropout=None,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.use_cache = use_cache
self.classifier_dropout = classifier_dropout
# Copied from transformers.models.roberta.configuration_roberta.RobertaOnnxConfig with Roberta->XLMRobertaXL
class XLMRobertaXLOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
]
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/xlm_roberta_xl/__init__.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
"configuration_xlm_roberta_xl": [
"XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XLMRobertaXLConfig",
"XLMRobertaXLOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_xlm_roberta_xl"] = [
"XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLMRobertaXLForCausalLM",
"XLMRobertaXLForMaskedLM",
"XLMRobertaXLForMultipleChoice",
"XLMRobertaXLForQuestionAnswering",
"XLMRobertaXLForSequenceClassification",
"XLMRobertaXLForTokenClassification",
"XLMRobertaXLModel",
"XLMRobertaXLPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaXLConfig,
XLMRobertaXLOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaXLForCausalLM,
XLMRobertaXLForMaskedLM,
XLMRobertaXLForMultipleChoice,
XLMRobertaXLForQuestionAnswering,
XLMRobertaXLForSequenceClassification,
XLMRobertaXLForTokenClassification,
XLMRobertaXLModel,
XLMRobertaXLPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch XLM RoBERTa xl,xxl model."""
import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN, gelu
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
MaskedLMOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_xlm_roberta_xl import XLMRobertaXLConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "xlm-roberta-xlarge"
_CONFIG_FOR_DOC = "XLMRobertaXLConfig"
XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/xlm-roberta-xl",
"facebook/xlm-roberta-xxl",
# See all RoBERTa models at https://huggingface.co/models?filter=xlm-roberta-xl
]
class XLMRobertaXLEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.register_buffer(
"token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False
)
# End copy
self.padding_idx = config.pad_token_id
self.position_embeddings = nn.Embedding(
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
)
def forward(
self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
):
if position_ids is None:
if input_ids is not None:
# Create the position ids from the input token ids. Any padded tokens remain padded.
position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
else:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.dropout(embeddings)
return embeddings
# Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings.create_position_ids_from_inputs_embeds
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
"""
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
"""
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
)
return position_ids.unsqueeze(0).expand(input_shape)
# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->XLMRobertaXL
class XLMRobertaXLSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(
config, "position_embedding_type", "absolute"
)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
use_cache = past_key_value is not None
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
query_length, key_length = query_layer.shape[2], key_layer.shape[2]
if use_cache:
position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
-1, 1
)
else:
position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in XLMRobertaXLModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
class XLMRobertaXLSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states + input_tensor
return hidden_states
class XLMRobertaXLAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self_attn_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.self = XLMRobertaXLSelfAttention(config, position_embedding_type=position_embedding_type)
self.output = XLMRobertaXLSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
intermediate = self.self_attn_layer_norm(hidden_states)
self_outputs = self.self(
intermediate,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
class XLMRobertaXLIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class XLMRobertaXLOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = hidden_states + input_tensor
return hidden_states
class XLMRobertaXLLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = XLMRobertaXLAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
self.crossattention = XLMRobertaXLAttention(config, position_embedding_type="absolute")
self.intermediate = XLMRobertaXLIntermediate(config)
self.output = XLMRobertaXLOutput(config)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and encoder_hidden_states is not None:
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
" by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
cross_attn_past_key_value,
output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.LayerNorm(attention_output)
intermediate_output = self.intermediate(intermediate_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class XLMRobertaXLEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([XLMRobertaXLLayer(config) for _ in range(config.num_hidden_layers)])
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_values[i] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
hidden_states = self.LayerNorm(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertPooler
class XLMRobertaXLPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
class XLMRobertaXLPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = XLMRobertaXLConfig
base_model_prefix = "roberta"
# Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
XLM_ROBERTA_XL_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
general usage and behavior.
Parameters:
config ([`XLMRobertaXLConfig`]): Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
XLM_ROBERTA_XL_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input
IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare XLM-RoBERTa-xlarge Model transformer outputting raw hidden-states without any specific head on top.",
XLM_ROBERTA_XL_START_DOCSTRING,
)
class XLMRobertaXLModel(XLMRobertaXLPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in *Attention is
all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz
Kaiser and Illia Polosukhin. To behave as an decoder the model needs to be initialized with the `is_decoder`
argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with
both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as
an input to the forward pass. .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762
"""
# Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->XLMRobertaXL
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = XLMRobertaXLEmbeddings(config)
self.encoder = XLMRobertaXLEncoder(config)
self.pooler = XLMRobertaXLPooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
# Copied from transformers.models.bert.modeling_bert.BertModel.forward
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
@add_start_docstrings(
"""XLM-RoBERTa-xlarge Model with a `language modeling` head on top for CLM fine-tuning.""",
XLM_ROBERTA_XL_START_DOCSTRING,
)
class XLMRobertaXLForCausalLM(XLMRobertaXLPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `RobertaLMHeadModel` as a standalone, add `is_decoder=True.`")
self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
self.lm_head = XLMRobertaXLLMHead(config)
self.init_weights()
def get_output_embeddings(self):
return self.lm_head.decoder
def set_output_embeddings(self, new_embeddings):
self.lm_head.decoder = new_embeddings
@add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, RobertaForCausalLM, RobertaConfig
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("roberta-base")
>>> config = RobertaConfig.from_pretrained("roberta-base")
>>> config.is_decoder = True
>>> model = RobertaForCausalLM.from_pretrained("roberta-base", config=config)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.logits
```
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
lm_loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((lm_loss,) + output) if lm_loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=lm_loss,
logits=prediction_scores,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs):
input_shape = input_ids.shape
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_shape)
# cut decoder_input_ids if past_key_values is used
if past_key_values is not None:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_length:]
return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values}
def _reorder_cache(self, past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
@add_start_docstrings(
"""XLM-RoBERTa-xlarge Model with a `language modeling` head on top.""", XLM_ROBERTA_XL_START_DOCSTRING
)
class XLMRobertaXLForMaskedLM(XLMRobertaXLPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
self.lm_head = XLMRobertaXLLMHead(config)
self.init_weights()
def get_output_embeddings(self):
return self.lm_head.decoder
def set_output_embeddings(self, new_embeddings):
self.lm_head.decoder = new_embeddings
@add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
mask="<mask>",
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, MaskedLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
kwargs (`Dict[str, any]`, optional, defaults to *{}*):
Used to hide legacy arguments that have been deprecated.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class XLMRobertaXLLMHead(nn.Module):
"""XLM-Roberta-xlarge Head for masked language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
self.decoder.bias = self.bias
def forward(self, features, **kwargs):
x = self.dense(features)
x = gelu(x)
x = self.layer_norm(x)
# project back to size of vocabulary with bias
x = self.decoder(x)
return x
def _tie_weights(self):
# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
self.bias = self.decoder.bias
@add_start_docstrings(
"""
XLM-RoBERTa-xlarge Model transformer with a sequence classification/regression head on top (a linear layer on top
of the pooled output) e.g. for GLUE tasks.
""",
XLM_ROBERTA_XL_START_DOCSTRING,
)
class XLMRobertaXLForSequenceClassification(XLMRobertaXLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
self.classifier = XLMRobertaXLClassificationHead(config)
self.init_weights()
@add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
XLM-Roberta-xlarge Model with a multiple choice classification head on top (a linear layer on top of the pooled
output and a softmax) e.g. for RocStories/SWAG tasks.
""",
XLM_ROBERTA_XL_START_DOCSTRING,
)
class XLMRobertaXLForMultipleChoice(XLMRobertaXLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.roberta = XLMRobertaXLModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
self.init_weights()
@add_start_docstrings_to_model_forward(
XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, MultipleChoiceModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
flat_inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.roberta(
flat_input_ids,
position_ids=flat_position_ids,
token_type_ids=flat_token_type_ids,
attention_mask=flat_attention_mask,
head_mask=head_mask,
inputs_embeds=flat_inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
XLM-Roberta-xlarge Model with a token classification head on top (a linear layer on top of the hidden-states
output) e.g. for Named-Entity-Recognition (NER) tasks.
""",
XLM_ROBERTA_XL_START_DOCSTRING,
)
class XLMRobertaXLForTokenClassification(XLMRobertaXLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
@add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
# Only keep active parts of the loss
if attention_mask is not None:
active_loss = attention_mask.view(-1) == 1
active_logits = logits.view(-1, self.num_labels)
active_labels = torch.where(
active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
)
loss = loss_fct(active_logits, active_labels)
else:
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class XLMRobertaXLClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, features, **kwargs):
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
@add_start_docstrings(
"""
XLM-Roberta-xlarge Model with a span classification head on top for extractive question-answering tasks like SQuAD
(a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
XLM_ROBERTA_XL_START_DOCSTRING,
)
class XLMRobertaXLForQuestionAnswering(XLMRobertaXLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
@add_start_docstrings_to_model_forward(XLM_ROBERTA_XL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`.
Args:
x: torch.Tensor x:
Returns: torch.Tensor
"""
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
mask = input_ids.ne(padding_idx).int()
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
return incremental_indices.long() + padding_idx
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/marian/modeling_flax_marian.py | # coding=utf-8
# Copyright 2021 The Marian Team Authors and The Google Flax Team Authors And The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Flax Marian model."""
import math
import random
from functools import partial
from typing import Callable, Optional, Tuple
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from jax.random import PRNGKey
from ...modeling_flax_outputs import (
FlaxBaseModelOutput,
FlaxBaseModelOutputWithPastAndCrossAttentions,
FlaxCausalLMOutputWithCrossAttentions,
FlaxSeq2SeqLMOutput,
FlaxSeq2SeqModelOutput,
)
from ...modeling_flax_utils import (
ACT2FN,
FlaxPreTrainedModel,
append_call_sample_docstring,
append_replace_return_docstrings,
overwrite_call_docstring,
)
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_marian import MarianConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "Helsinki-NLP/opus-mt-en-de"
_CONFIG_FOR_DOC = "MarianConfig"
MARIAN_START_DOCSTRING = r"""
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
Parameters:
config ([`MarianConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
`jax.numpy.bfloat16` (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.**
If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
[`~FlaxPreTrainedModel.to_bf16`].
"""
MARIAN_INPUTS_DOCSTRING = r"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
For translation and summarization training, `decoder_input_ids` should be provided. If no
`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
for denoising pre-training following the paper.
decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embeddings - 1]`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
MARIAN_ENCODE_INPUTS_DOCSTRING = r"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
MARIAN_DECODE_INPUTS_DOCSTRING = r"""
Args:
decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
For translation and summarization training, `decoder_input_ids` should be provided. If no
`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
for denoising pre-training following the paper.
encoder_outputs (`tuple(tuple(jnp.ndarray)`):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
encoder_attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embeddings - 1]`.
past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
def create_sinusoidal_positions(n_pos, dim):
position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)])
sentinel = dim // 2 + dim % 2
out = np.zeros_like(position_enc)
out[:, 0:sentinel] = np.sin(position_enc[:, 0::2])
out[:, sentinel:] = np.cos(position_enc[:, 1::2])
return jnp.array(out)
# Copied from transformers.models.bart.modeling_flax_bart.shift_tokens_right
def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int, decoder_start_token_id: int) -> jnp.ndarray:
"""
Shift input ids one token to the right.
"""
shifted_input_ids = jnp.zeros_like(input_ids)
shifted_input_ids = shifted_input_ids.at[:, 1:].set(input_ids[:, :-1])
shifted_input_ids = shifted_input_ids.at[:, 0].set(decoder_start_token_id)
shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids)
return shifted_input_ids
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention with Bart->Marian
class FlaxMarianAttention(nn.Module):
config: MarianConfig
embed_dim: int
num_heads: int
dropout: float = 0.0
causal: bool = False
bias: bool = True
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self) -> None:
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {self.num_heads})."
)
dense = partial(
nn.Dense,
self.embed_dim,
use_bias=self.bias,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense()
self.out_proj = dense()
self.dropout_layer = nn.Dropout(rate=self.dropout)
if self.causal:
self.causal_mask = make_causal_mask(
jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
)
def _split_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
def _merge_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
@nn.compact
def _concatenate_to_cache(self, key, value, query, attention_mask):
"""
This function takes projected key, value states from a single input token and concatenates the states to cached
states from previous steps. This function is slighly adapted from the official Flax repository:
https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
"""
# detect if we're initializing by absence of existing cache data.
is_initialized = self.has_variable("cache", "cached_key")
cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
if is_initialized:
*batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
# update key, value caches with our new 1d spatial slices
cur_index = cache_index.value
indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
key = lax.dynamic_update_slice(cached_key.value, key, indices)
value = lax.dynamic_update_slice(cached_value.value, value, indices)
cached_key.value = key
cached_value.value = value
num_updated_cache_vectors = query.shape[1]
cache_index.value = cache_index.value + num_updated_cache_vectors
# causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
pad_mask = jnp.broadcast_to(
jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
)
attention_mask = combine_masks(pad_mask, attention_mask)
return key, value, attention_mask
def __call__(
self,
hidden_states: jnp.ndarray,
key_value_states: Optional[jnp.ndarray] = None,
attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
batch_size = hidden_states.shape[0]
# get query proj
query_states = self.q_proj(hidden_states)
# get key, value proj
if is_cross_attention:
# cross_attentions
key_states = self.k_proj(key_value_states)
value_states = self.v_proj(key_value_states)
else:
# self_attention
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = self._split_heads(query_states)
key_states = self._split_heads(key_states)
value_states = self._split_heads(value_states)
# handle cache prepare causal attention mask
if self.causal:
query_length, key_length = query_states.shape[1], key_states.shape[1]
if self.has_variable("cache", "cached_key"):
mask_shift = self.variables["cache"]["cache_index"]
max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
causal_mask = lax.dynamic_slice(
self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
)
else:
causal_mask = self.causal_mask[:, :, :query_length, :key_length]
causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
# combine masks if needed
if attention_mask is not None and self.causal:
attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
attention_mask = combine_masks(attention_mask, causal_mask)
elif self.causal:
attention_mask = causal_mask
elif attention_mask is not None:
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
# During fast autoregressive decoding, we feed one position at a time,
# and cache the keys and values step by step.
if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
key_states, value_states, attention_mask = self._concatenate_to_cache(
key_states, value_states, query_states, attention_mask
)
# Convert the boolean attention mask to an attention bias.
if attention_mask is not None:
# attention mask in the form of attention bias
attention_bias = lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
)
else:
attention_bias = None
dropout_rng = None
if not deterministic and self.dropout > 0.0:
dropout_rng = self.make_rng("dropout")
attn_weights = dot_product_attention_weights(
query_states,
key_states,
bias=attention_bias,
dropout_rng=dropout_rng,
dropout_rate=self.dropout,
broadcast_dropout=True,
deterministic=deterministic,
dtype=self.dtype,
precision=None,
)
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
attn_output = self._merge_heads(attn_output)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartEncoderLayer with Bart->Marian
class FlaxMarianEncoderLayer(nn.Module):
config: MarianConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxMarianAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.encoder_attention_heads,
dropout=self.config.attention_dropout,
dtype=self.dtype,
)
self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
self.activation_fn = ACT2FN[self.config.activation_function]
self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
self.fc1 = nn.Dense(
self.config.encoder_ffn_dim,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
self.fc2 = nn.Dense(
self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
)
self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
def __call__(
self,
hidden_states: jnp.ndarray,
attention_mask: jnp.ndarray,
output_attentions: bool = True,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
residual = hidden_states
hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartEncoderLayerCollection with Bart->Marian
class FlaxMarianEncoderLayerCollection(nn.Module):
config: MarianConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxMarianEncoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.encoder_layers)
]
self.layerdrop = self.config.encoder_layerdrop
def __call__(
self,
hidden_states,
attention_mask,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for encoder_layer in self.layers:
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if not deterministic and (dropout_probability < self.layerdrop): # skip the layer
layer_outputs = (None, None)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
output_attentions,
deterministic,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = (hidden_states, all_hidden_states, all_attentions)
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderLayer with Bart->Marian
class FlaxMarianDecoderLayer(nn.Module):
config: MarianConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxMarianAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.decoder_attention_heads,
dropout=self.config.attention_dropout,
causal=True,
dtype=self.dtype,
)
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
self.activation_fn = ACT2FN[self.config.activation_function]
self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
self.encoder_attn = FlaxMarianAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.decoder_attention_heads,
dropout=self.config.attention_dropout,
dtype=self.dtype,
)
self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
self.fc1 = nn.Dense(
self.config.decoder_ffn_dim,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
self.fc2 = nn.Dense(
self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
)
self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
def __call__(
self,
hidden_states: jnp.ndarray,
attention_mask: jnp.ndarray,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
output_attentions: bool = True,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
residual = hidden_states
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache
)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Cross-Attention Block
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states, cross_attn_weights = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# Fully Connected
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
return outputs
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderLayerCollection with Bart->Marian
class FlaxMarianDecoderLayerCollection(nn.Module):
config: MarianConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxMarianDecoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.decoder_layers)
]
self.layerdrop = self.config.decoder_layerdrop
def __call__(
self,
hidden_states,
attention_mask,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if not deterministic and (dropout_probability < self.layerdrop):
layer_outputs = (None, None, None)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
init_cache=init_cache,
output_attentions=output_attentions,
deterministic=deterministic,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions]
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
class FlaxMarianEncoder(nn.Module):
config: MarianConfig
embed_tokens: nn.Embed
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
embed_dim = self.config.d_model
self.max_source_positions = self.config.max_position_embeddings
self.embed_scale = math.sqrt(embed_dim) if self.config.scale_embedding else 1.0
self.embed_positions = create_sinusoidal_positions(self.config.max_position_embeddings, embed_dim)
self.layers = FlaxMarianEncoderLayerCollection(self.config, self.dtype)
def __call__(
self,
input_ids,
attention_mask,
position_ids,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
input_shape = input_ids.shape
input_ids = input_ids.reshape(-1, input_shape[-1])
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
positions = jnp.take(self.embed_positions, position_ids, axis=0)
# explictly cast the positions here, since self.embed_positions are not registered as parameters
positions = positions.astype(inputs_embeds.dtype)
hidden_states = inputs_embeds + positions
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
outputs = self.layers(
hidden_states,
attention_mask,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return outputs
return FlaxBaseModelOutput(
last_hidden_state=outputs.last_hidden_state,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class FlaxMarianDecoder(nn.Module):
config: MarianConfig
embed_tokens: nn.Embed
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
embed_dim = self.config.d_model
self.max_target_positions = self.config.max_position_embeddings
self.embed_scale = math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0
self.embed_positions = create_sinusoidal_positions(self.config.max_position_embeddings, embed_dim)
self.layers = FlaxMarianDecoderLayerCollection(self.config, self.dtype)
def __call__(
self,
input_ids,
attention_mask,
position_ids,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
input_shape = input_ids.shape
input_ids = input_ids.reshape(-1, input_shape[-1])
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
# embed positions
positions = jnp.take(self.embed_positions, position_ids, axis=0)
# explictly cast the positions here, since self.embed_positions are not registered as parameters
positions = positions.astype(inputs_embeds.dtype)
hidden_states = inputs_embeds + positions
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
outputs = self.layers(
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return outputs
return FlaxBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=outputs.last_hidden_state,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
class FlaxMarianModule(nn.Module):
config: MarianConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.shared = nn.Embed(
self.config.vocab_size,
self.config.d_model,
embedding_init=jax.nn.initializers.normal(self.config.init_std),
)
self.encoder = FlaxMarianEncoder(self.config, dtype=self.dtype, embed_tokens=self.shared)
self.decoder = FlaxMarianDecoder(self.config, dtype=self.dtype, embed_tokens=self.shared)
def _get_encoder_module(self):
return self.encoder
def _get_decoder_module(self):
return self.decoder
def __call__(
self,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
position_ids,
decoder_position_ids,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return FlaxSeq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
class FlaxMarianPreTrainedModel(FlaxPreTrainedModel):
config_class = MarianConfig
base_model_prefix: str = "model"
module_class: nn.Module = None
def __init__(
self,
config: MarianConfig,
input_shape: Tuple[int] = (1, 1),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs,
):
module = self.module_class(config=config, dtype=dtype, **kwargs)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_ids = jnp.zeros(input_shape, dtype="i4")
# make sure initialization pass will work for FlaxMarianForSequenceClassificationModule
input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id)
attention_mask = jnp.ones_like(input_ids)
decoder_input_ids = input_ids
decoder_attention_mask = jnp.ones_like(input_ids)
batch_size, sequence_length = input_ids.shape
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
random_params = self.module.init(
rngs,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
position_ids,
decoder_position_ids,
)["params"]
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freeze(unflatten_dict(params))
else:
return random_params
def init_cache(self, batch_size, max_length, encoder_outputs):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
cache.
encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`):
`encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*:
`attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*)
is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
cross-attention of the decoder.
"""
# init input variables to retrieve cache
decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape
)
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
return decoder_module(decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs)
init_variables = self.module.init(
jax.random.PRNGKey(0),
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
init_cache=True,
method=_decoder_forward, # we only need to call the decoder to init the cache
)
return unfreeze(init_variables["cache"])
@add_start_docstrings(MARIAN_ENCODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=MarianConfig)
def encode(
self,
input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
position_ids: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, FlaxMarianMTModel
>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=64, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
if position_ids is None:
batch_size, sequence_length = input_ids.shape
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
def _encoder_forward(module, input_ids, attention_mask, position_ids, **kwargs):
encode_module = module._get_encoder_module()
return encode_module(input_ids, attention_mask, position_ids, **kwargs)
return self.module.apply(
{"params": params or self.params},
input_ids=jnp.array(input_ids, dtype="i4"),
attention_mask=jnp.array(attention_mask, dtype="i4"),
position_ids=jnp.array(position_ids, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
method=_encoder_forward,
)
@add_start_docstrings(MARIAN_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=MarianConfig)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> import jax.numpy as jnp
>>> from transformers import AutoTokenizer, FlaxMarianMTModel
>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=64, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_hidden_states = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
encoder_hidden_states = encoder_outputs[0]
if encoder_attention_mask is None:
batch_size, sequence_length = encoder_hidden_states.shape[:2]
encoder_attention_mask = jnp.ones((batch_size, sequence_length))
batch_size, sequence_length = decoder_input_ids.shape
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones((batch_size, sequence_length))
if decoder_position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
inputs = {"params": params or self.params}
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be
# passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
# it can be changed by FlaxMarianAttention module
if past_key_values:
inputs["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
return decoder_module(
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
**kwargs,
)
outputs = self.module.apply(
inputs,
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
mutable=mutable,
method=_decoder_forward,
)
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs, past = outputs
outputs["past_key_values"] = unfreeze(past["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs, past = outputs
outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
return outputs
@add_start_docstrings_to_model_forward(MARIAN_INPUTS_DOCSTRING)
def __call__(
self,
input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
decoder_input_ids: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
position_ids: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
# prepare encoder inputs
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
if position_ids is None:
batch_size, sequence_length = input_ids.shape
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
# prepare decoder inputs
if decoder_input_ids is None:
decoder_input_ids = shift_tokens_right(
input_ids, self.config.pad_token_id, decoder_start_token_id=self.config.decoder_start_token_id
)
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
if decoder_position_ids is None:
batch_size, sequence_length = decoder_input_ids.shape
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
# Handle any PRNG if needed
rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
return self.module.apply(
{"params": params or self.params},
input_ids=jnp.array(input_ids, dtype="i4"),
attention_mask=jnp.array(attention_mask, dtype="i4"),
position_ids=jnp.array(position_ids, dtype="i4"),
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
)
@add_start_docstrings(
"The bare Marian Model transformer outputting raw hidden-states without any specific head on top.",
MARIAN_START_DOCSTRING,
)
class FlaxMarianModel(FlaxMarianPreTrainedModel):
config: MarianConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
module_class = FlaxMarianModule
append_call_sample_docstring(FlaxMarianModel, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC)
class FlaxMarianMTModule(nn.Module):
config: MarianConfig
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.model = FlaxMarianModule(config=self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.model.shared.num_embeddings,
use_bias=False,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
self.final_logits_bias = self.param("final_logits_bias", self.bias_init, (1, self.model.shared.num_embeddings))
def _get_encoder_module(self):
return self.model.encoder
def _get_decoder_module(self):
return self.model.decoder
def __call__(
self,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
position_ids,
decoder_position_ids,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
position_ids=position_ids,
decoder_position_ids=decoder_position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
shared_embedding = self.model.variables["params"]["shared"]["embedding"]
lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
else:
lm_logits = self.lm_head(hidden_states)
lm_logits += self.final_logits_bias.astype(self.dtype)
if not return_dict:
output = (lm_logits,) + outputs[1:]
return output
return FlaxSeq2SeqLMOutput(
logits=lm_logits,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
@add_start_docstrings(
"The MARIAN Model with a language modeling head. Can be used for translation.", MARIAN_START_DOCSTRING
)
class FlaxMarianMTModel(FlaxMarianPreTrainedModel):
module_class = FlaxMarianMTModule
dtype: jnp.dtype = jnp.float32
@add_start_docstrings(MARIAN_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=MarianConfig)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> import jax.numpy as jnp
>>> from transformers import AutoTokenizer, FlaxMarianMTModel
>>> model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=64, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> logits = outputs.logits
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
encoder_hidden_states = encoder_outputs[0]
if encoder_attention_mask is None:
batch_size, sequence_length = encoder_hidden_states.shape[:2]
encoder_attention_mask = jnp.ones((batch_size, sequence_length))
batch_size, sequence_length = decoder_input_ids.shape
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones((batch_size, sequence_length))
if decoder_position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
inputs = {"params": params or self.params}
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be
# passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
# it can be changed by FlaxMarianAttention module
if past_key_values:
inputs["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
outputs = decoder_module(
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
**kwargs,
)
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
shared_embedding = module.model.variables["params"]["shared"]["embedding"]
lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
else:
lm_logits = module.lm_head(hidden_states)
lm_logits += module.final_logits_bias.astype(self.dtype)
return lm_logits, outputs
outputs = self.module.apply(
inputs,
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
mutable=mutable,
method=_decoder_forward,
)
if past_key_values is None:
lm_logits, decoder_outputs = outputs
else:
(lm_logits, decoder_outputs), past = outputs
if return_dict:
outputs = FlaxCausalLMOutputWithCrossAttentions(
logits=lm_logits,
hidden_states=decoder_outputs.hidden_states,
attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
)
else:
outputs = (lm_logits,) + decoder_outputs[1:]
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs["past_key_values"] = unfreeze(past["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
return outputs
def _adapt_logits_for_beam_search(self, logits):
"""This function enforces the padding token never to be generated."""
logits = logits.at[:, :, self.config.pad_token_id].set(float("-inf"))
return logits
def prepare_inputs_for_generation(
self,
decoder_input_ids,
max_length,
attention_mask: Optional[jax.Array] = None,
decoder_attention_mask: Optional[jax.Array] = None,
encoder_outputs=None,
**kwargs,
):
# initializing the cache
batch_size, seq_length = decoder_input_ids.shape
past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
# Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
# But since the decoder uses a causal mask, those positions are masked anyways.
# Thus we can create a single static attention_mask here, which is more efficient for compilation
extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
if decoder_attention_mask is not None:
position_ids = decoder_attention_mask.cumsum(axis=-1) - 1
extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0))
else:
position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
return {
"past_key_values": past_key_values,
"encoder_outputs": encoder_outputs,
"encoder_attention_mask": attention_mask,
"decoder_attention_mask": extended_attention_mask,
"decoder_position_ids": position_ids,
}
def update_inputs_for_generation(self, model_outputs, model_kwargs):
model_kwargs["past_key_values"] = model_outputs.past_key_values
model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1
return model_kwargs
FLAX_MARIAN_MT_DOCSTRING = """
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, FlaxMarianMTModel
>>> model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> text = "My friends are cool but they eat too many carbs."
>>> input_ids = tokenizer(text, max_length=64, return_tensors="jax").input_ids
>>> sequences = model.generate(input_ids, max_length=64, num_beams=2).sequences
>>> outputs = tokenizer.batch_decode(sequences, skip_special_tokens=True)
>>> # should give *Meine Freunde sind cool, aber sie essen zu viele Kohlenhydrate.*
```
"""
overwrite_call_docstring(
FlaxMarianMTModel,
MARIAN_INPUTS_DOCSTRING + FLAX_MARIAN_MT_DOCSTRING,
)
append_replace_return_docstrings(FlaxMarianMTModel, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import json
import os
import socket
import time
import warnings
from pathlib import Path
from typing import Dict, List, Union
from zipfile import ZipFile
import numpy as np
import torch
from huggingface_hub.hf_api import list_models
from torch import nn
from tqdm import tqdm
from transformers import MarianConfig, MarianMTModel, MarianTokenizer
def remove_suffix(text: str, suffix: str):
if text.endswith(suffix):
return text[: -len(suffix)]
return text # or whatever
def remove_prefix(text: str, prefix: str):
if text.startswith(prefix):
return text[len(prefix) :]
return text # or whatever
def convert_encoder_layer(opus_dict, layer_prefix: str, converter: dict):
sd = {}
for k in opus_dict:
if not k.startswith(layer_prefix):
continue
stripped = remove_prefix(k, layer_prefix)
v = opus_dict[k].T # besides embeddings, everything must be transposed.
sd[converter[stripped]] = torch.tensor(v).squeeze()
return sd
def load_layers_(layer_lst: nn.ModuleList, opus_state: dict, converter, is_decoder=False):
for i, layer in enumerate(layer_lst):
layer_tag = f"decoder_l{i + 1}_" if is_decoder else f"encoder_l{i + 1}_"
sd = convert_encoder_layer(opus_state, layer_tag, converter)
layer.load_state_dict(sd, strict=False)
def find_pretrained_model(src_lang: str, tgt_lang: str) -> List[str]:
"""Find models that can accept src_lang as input and return tgt_lang as output."""
prefix = "Helsinki-NLP/opus-mt-"
model_list = list_models()
model_ids = [x.modelId for x in model_list if x.modelId.startswith("Helsinki-NLP")]
src_and_targ = [
remove_prefix(m, prefix).lower().split("-") for m in model_ids if "+" not in m
] # + cant be loaded.
matching = [f"{prefix}{a}-{b}" for (a, b) in src_and_targ if src_lang in a and tgt_lang in b]
return matching
def add_emb_entries(wemb, final_bias, n_special_tokens=1):
vsize, d_model = wemb.shape
embs_to_add = np.zeros((n_special_tokens, d_model))
new_embs = np.concatenate([wemb, embs_to_add])
bias_to_add = np.zeros((n_special_tokens, 1))
new_bias = np.concatenate((final_bias, bias_to_add), axis=1)
return new_embs, new_bias
def _cast_yaml_str(v):
bool_dct = {"true": True, "false": False}
if not isinstance(v, str):
return v
elif v in bool_dct:
return bool_dct[v]
try:
return int(v)
except (TypeError, ValueError):
return v
def cast_marian_config(raw_cfg: Dict[str, str]) -> Dict:
return {k: _cast_yaml_str(v) for k, v in raw_cfg.items()}
CONFIG_KEY = "special:model.yml"
def load_config_from_state_dict(opus_dict):
import yaml
cfg_str = "".join([chr(x) for x in opus_dict[CONFIG_KEY]])
yaml_cfg = yaml.load(cfg_str[:-1], Loader=yaml.BaseLoader)
return cast_marian_config(yaml_cfg)
def find_model_file(dest_dir): # this one better
model_files = list(Path(dest_dir).glob("*.npz"))
if len(model_files) != 1:
raise ValueError(f"Found more than one model file: {model_files}")
model_file = model_files[0]
return model_file
# Group Names Logic: change long opus model names to something shorter, like opus-mt-en-ROMANCE
ROM_GROUP = (
"fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT"
"+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co"
"+nap+scn+vec+sc+ro+la"
)
GROUPS = [
("cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh", "ZH"),
(ROM_GROUP, "ROMANCE"),
("de+nl+fy+af+da+fo+is+no+nb+nn+sv", "NORTH_EU"),
("da+fo+is+no+nb+nn+sv", "SCANDINAVIA"),
("se+sma+smj+smn+sms", "SAMI"),
("nb_NO+nb+nn_NO+nn+nog+no_nb+no", "NORWAY"),
("ga+cy+br+gd+kw+gv", "CELTIC"), # https://en.wikipedia.org/wiki/Insular_Celtic_languages
]
GROUP_TO_OPUS_NAME = {
"opus-mt-ZH-de": "cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-de",
"opus-mt-ZH-fi": "cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-fi",
"opus-mt-ZH-sv": "cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-sv",
"opus-mt-SCANDINAVIA-SCANDINAVIA": "da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv",
"opus-mt-NORTH_EU-NORTH_EU": "de+nl+fy+af+da+fo+is+no+nb+nn+sv-de+nl+fy+af+da+fo+is+no+nb+nn+sv",
"opus-mt-de-ZH": "de-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh",
"opus-mt-en_el_es_fi-en_el_es_fi": "en+el+es+fi-en+el+es+fi",
"opus-mt-en-ROMANCE": (
"en-fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO"
"+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR"
"+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la"
),
"opus-mt-en-CELTIC": "en-ga+cy+br+gd+kw+gv",
"opus-mt-es-NORWAY": "es-nb_NO+nb+nn_NO+nn+nog+no_nb+no",
"opus-mt-fi_nb_no_nn_ru_sv_en-SAMI": "fi+nb+no+nn+ru+sv+en-se+sma+smj+smn+sms",
"opus-mt-fi-ZH": "fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh",
"opus-mt-fi-NORWAY": "fi-nb_NO+nb+nn_NO+nn+nog+no_nb+no",
"opus-mt-ROMANCE-en": (
"fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO"
"+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR"
"+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la-en"
),
"opus-mt-CELTIC-en": "ga+cy+br+gd+kw+gv-en",
"opus-mt-sv-ZH": "sv-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh",
"opus-mt-sv-NORWAY": "sv-nb_NO+nb+nn_NO+nn+nog+no_nb+no",
}
OPUS_GITHUB_URL = "https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/"
ORG_NAME = "Helsinki-NLP/"
def convert_opus_name_to_hf_name(x):
"""For OPUS-MT-Train/ DEPRECATED"""
for substr, grp_name in GROUPS:
x = x.replace(substr, grp_name)
return x.replace("+", "_")
def convert_hf_name_to_opus_name(hf_model_name):
"""
Relies on the assumption that there are no language codes like pt_br in models that are not in GROUP_TO_OPUS_NAME.
"""
hf_model_name = remove_prefix(hf_model_name, ORG_NAME)
if hf_model_name in GROUP_TO_OPUS_NAME:
opus_w_prefix = GROUP_TO_OPUS_NAME[hf_model_name]
else:
opus_w_prefix = hf_model_name.replace("_", "+")
return remove_prefix(opus_w_prefix, "opus-mt-")
def get_system_metadata(repo_root):
import git
return {
"helsinki_git_sha": git.Repo(path=repo_root, search_parent_directories=True).head.object.hexsha,
"transformers_git_sha": git.Repo(path=".", search_parent_directories=True).head.object.hexsha,
"port_machine": socket.gethostname(),
"port_time": time.strftime("%Y-%m-%d-%H:%M"),
}
# docstyle-ignore
FRONT_MATTER_TEMPLATE = """---
language:
{}
tags:
- translation
license: apache-2.0
---
"""
DEFAULT_REPO = "Tatoeba-Challenge"
DEFAULT_MODEL_DIR = os.path.join(DEFAULT_REPO, "models")
def write_model_card(
hf_model_name: str,
repo_root=DEFAULT_REPO,
save_dir=Path("marian_converted"),
dry_run=False,
extra_metadata={},
) -> str:
"""
Copy the most recent model's readme section from opus, and add metadata. upload command: aws s3 sync model_card_dir
s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun
"""
import pandas as pd
hf_model_name = remove_prefix(hf_model_name, ORG_NAME)
opus_name: str = convert_hf_name_to_opus_name(hf_model_name)
if repo_root not in ("OPUS-MT-train", "Tatoeba-Challenge"):
raise ValueError(f"Repos root is {repo_root}. Expected either OPUS-MT-train or Tatoeba-Challenge")
opus_readme_path = Path(repo_root).joinpath("models", opus_name, "README.md")
if not (opus_readme_path.exists()):
raise ValueError(f"Readme file {opus_readme_path} not found")
opus_src, opus_tgt = [x.split("+") for x in opus_name.split("-")]
readme_url = f"https://github.com/Helsinki-NLP/{repo_root}/tree/master/models/{opus_name}/README.md"
s, t = ",".join(opus_src), ",".join(opus_tgt)
metadata = {
"hf_name": hf_model_name,
"source_languages": s,
"target_languages": t,
"opus_readme_url": readme_url,
"original_repo": repo_root,
"tags": ["translation"],
}
metadata.update(extra_metadata)
metadata.update(get_system_metadata(repo_root))
# combine with opus markdown
extra_markdown = (
f"### {hf_model_name}\n\n* source group: {metadata['src_name']} \n* target group: "
f"{metadata['tgt_name']} \n* OPUS readme: [{opus_name}]({readme_url})\n"
)
content = opus_readme_path.open().read()
content = content.split("\n# ")[-1] # Get the lowest level 1 header in the README -- the most recent model.
splat = content.split("*")[2:]
print(splat[3])
content = "*".join(splat)
content = (
FRONT_MATTER_TEMPLATE.format(metadata["src_alpha2"])
+ extra_markdown
+ "\n* "
+ content.replace("download", "download original weights")
)
items = "\n\n".join([f"- {k}: {v}" for k, v in metadata.items()])
sec3 = "\n### System Info: \n" + items
content += sec3
if dry_run:
return content, metadata
sub_dir = save_dir / f"opus-mt-{hf_model_name}"
sub_dir.mkdir(exist_ok=True)
dest = sub_dir / "README.md"
dest.open("w").write(content)
pd.Series(metadata).to_json(sub_dir / "metadata.json")
# if dry_run:
return content, metadata
def make_registry(repo_path="Opus-MT-train/models"):
if not (Path(repo_path) / "fr-en" / "README.md").exists():
raise ValueError(
f"repo_path:{repo_path} does not exist: "
"You must run: git clone [email protected]:Helsinki-NLP/Opus-MT-train.git before calling."
)
results = {}
for p in Path(repo_path).iterdir():
n_dash = p.name.count("-")
if n_dash == 0:
continue
else:
lns = list(open(p / "README.md").readlines())
results[p.name] = _parse_readme(lns)
return [(k, v["pre-processing"], v["download"], v["download"][:-4] + ".test.txt") for k, v in results.items()]
def convert_all_sentencepiece_models(model_list=None, repo_path=None, dest_dir=Path("marian_converted")):
"""Requires 300GB"""
save_dir = Path("marian_ckpt")
dest_dir = Path(dest_dir)
dest_dir.mkdir(exist_ok=True)
save_paths = []
if model_list is None:
model_list: list = make_registry(repo_path=repo_path)
for k, prepro, download, test_set_url in tqdm(model_list):
if "SentencePiece" not in prepro: # dont convert BPE models.
continue
if not os.path.exists(save_dir / k):
download_and_unzip(download, save_dir / k)
pair_name = convert_opus_name_to_hf_name(k)
convert(save_dir / k, dest_dir / f"opus-mt-{pair_name}")
save_paths.append(dest_dir / f"opus-mt-{pair_name}")
return save_paths
def lmap(f, x) -> List:
return list(map(f, x))
def fetch_test_set(test_set_url):
import wget
fname = wget.download(test_set_url, "opus_test.txt")
lns = Path(fname).open().readlines()
src = lmap(str.strip, lns[::4])
gold = lmap(str.strip, lns[1::4])
mar_model = lmap(str.strip, lns[2::4])
if not (len(gold) == len(mar_model) == len(src)):
raise ValueError(f"Gold, marian and source lengths {len(gold)}, {len(mar_model)}, {len(src)} mismatched")
os.remove(fname)
return src, mar_model, gold
def convert_whole_dir(path=Path("marian_ckpt/")):
for subdir in tqdm(list(path.ls())):
dest_dir = f"marian_converted/{subdir.name}"
if (dest_dir / "pytorch_model.bin").exists():
continue
convert(source_dir, dest_dir)
def _parse_readme(lns):
"""Get link and metadata from opus model card equivalent."""
subres = {}
for ln in [x.strip() for x in lns]:
if not ln.startswith("*"):
continue
ln = ln[1:].strip()
for k in ["download", "dataset", "models", "model", "pre-processing"]:
if ln.startswith(k):
break
else:
continue
if k in ["dataset", "model", "pre-processing"]:
splat = ln.split(":")
_, v = splat
subres[k] = v
elif k == "download":
v = ln.split("(")[-1][:-1]
subres[k] = v
return subres
def save_tokenizer_config(dest_dir: Path, separate_vocabs=False):
dname = dest_dir.name.split("-")
dct = {"target_lang": dname[-1], "source_lang": "-".join(dname[:-1]), "separate_vocabs": separate_vocabs}
save_json(dct, dest_dir / "tokenizer_config.json")
def add_to_vocab_(vocab: Dict[str, int], special_tokens: List[str]):
start = max(vocab.values()) + 1
added = 0
for tok in special_tokens:
if tok in vocab:
continue
vocab[tok] = start + added
added += 1
return added
def find_vocab_file(model_dir):
return list(model_dir.glob("*vocab.yml"))[0]
def find_src_vocab_file(model_dir):
return list(model_dir.glob("*src.vocab.yml"))[0]
def find_tgt_vocab_file(model_dir):
return list(model_dir.glob("*trg.vocab.yml"))[0]
def add_special_tokens_to_vocab(model_dir: Path, separate_vocab=False) -> None:
if separate_vocab:
vocab = load_yaml(find_src_vocab_file(model_dir))
vocab = {k: int(v) for k, v in vocab.items()}
num_added = add_to_vocab_(vocab, ["<pad>"])
save_json(vocab, model_dir / "vocab.json")
vocab = load_yaml(find_tgt_vocab_file(model_dir))
vocab = {k: int(v) for k, v in vocab.items()}
num_added = add_to_vocab_(vocab, ["<pad>"])
save_json(vocab, model_dir / "target_vocab.json")
save_tokenizer_config(model_dir, separate_vocabs=separate_vocab)
else:
vocab = load_yaml(find_vocab_file(model_dir))
vocab = {k: int(v) for k, v in vocab.items()}
num_added = add_to_vocab_(vocab, ["<pad>"])
print(f"added {num_added} tokens to vocab")
save_json(vocab, model_dir / "vocab.json")
save_tokenizer_config(model_dir)
def check_equal(marian_cfg, k1, k2):
v1, v2 = marian_cfg[k1], marian_cfg[k2]
if v1 != v2:
raise ValueError(f"hparams {k1},{k2} differ: {v1} != {v2}")
def check_marian_cfg_assumptions(marian_cfg):
assumed_settings = {
"layer-normalization": False,
"right-left": False,
"transformer-ffn-depth": 2,
"transformer-aan-depth": 2,
"transformer-no-projection": False,
"transformer-postprocess-emb": "d",
"transformer-postprocess": "dan", # Dropout, add, normalize
"transformer-preprocess": "",
"type": "transformer",
"ulr-dim-emb": 0,
"dec-cell-base-depth": 2,
"dec-cell-high-depth": 1,
"transformer-aan-nogate": False,
}
for k, v in assumed_settings.items():
actual = marian_cfg[k]
if actual != v:
raise ValueError(f"Unexpected config value for {k} expected {v} got {actual}")
BIAS_KEY = "decoder_ff_logit_out_b"
BART_CONVERTER = { # for each encoder and decoder layer
"self_Wq": "self_attn.q_proj.weight",
"self_Wk": "self_attn.k_proj.weight",
"self_Wv": "self_attn.v_proj.weight",
"self_Wo": "self_attn.out_proj.weight",
"self_bq": "self_attn.q_proj.bias",
"self_bk": "self_attn.k_proj.bias",
"self_bv": "self_attn.v_proj.bias",
"self_bo": "self_attn.out_proj.bias",
"self_Wo_ln_scale": "self_attn_layer_norm.weight",
"self_Wo_ln_bias": "self_attn_layer_norm.bias",
"ffn_W1": "fc1.weight",
"ffn_b1": "fc1.bias",
"ffn_W2": "fc2.weight",
"ffn_b2": "fc2.bias",
"ffn_ffn_ln_scale": "final_layer_norm.weight",
"ffn_ffn_ln_bias": "final_layer_norm.bias",
# Decoder Cross Attention
"context_Wk": "encoder_attn.k_proj.weight",
"context_Wo": "encoder_attn.out_proj.weight",
"context_Wq": "encoder_attn.q_proj.weight",
"context_Wv": "encoder_attn.v_proj.weight",
"context_bk": "encoder_attn.k_proj.bias",
"context_bo": "encoder_attn.out_proj.bias",
"context_bq": "encoder_attn.q_proj.bias",
"context_bv": "encoder_attn.v_proj.bias",
"context_Wo_ln_scale": "encoder_attn_layer_norm.weight",
"context_Wo_ln_bias": "encoder_attn_layer_norm.bias",
}
class OpusState:
def __init__(self, source_dir, eos_token_id=0):
npz_path = find_model_file(source_dir)
self.state_dict = np.load(npz_path)
cfg = load_config_from_state_dict(self.state_dict)
if cfg["dim-vocabs"][0] != cfg["dim-vocabs"][1]:
raise ValueError
if "Wpos" in self.state_dict:
raise ValueError("Wpos key in state dictionary")
self.state_dict = dict(self.state_dict)
if cfg["tied-embeddings-all"]:
cfg["tied-embeddings-src"] = True
cfg["tied-embeddings"] = True
self.share_encoder_decoder_embeddings = cfg["tied-embeddings-src"]
# create the tokenizer here because we need to know the eos_token_id
self.source_dir = source_dir
self.tokenizer = self.load_tokenizer()
# retrieve EOS token and set correctly
tokenizer_has_eos_token_id = (
hasattr(self.tokenizer, "eos_token_id") and self.tokenizer.eos_token_id is not None
)
eos_token_id = self.tokenizer.eos_token_id if tokenizer_has_eos_token_id else 0
if cfg["tied-embeddings-src"]:
self.wemb, self.final_bias = add_emb_entries(self.state_dict["Wemb"], self.state_dict[BIAS_KEY], 1)
self.pad_token_id = self.wemb.shape[0] - 1
cfg["vocab_size"] = self.pad_token_id + 1
else:
self.wemb, _ = add_emb_entries(self.state_dict["encoder_Wemb"], self.state_dict[BIAS_KEY], 1)
self.dec_wemb, self.final_bias = add_emb_entries(
self.state_dict["decoder_Wemb"], self.state_dict[BIAS_KEY], 1
)
# still assuming that vocab size is same for encoder and decoder
self.pad_token_id = self.wemb.shape[0] - 1
cfg["vocab_size"] = self.pad_token_id + 1
cfg["decoder_vocab_size"] = self.pad_token_id + 1
if cfg["vocab_size"] != self.tokenizer.vocab_size:
raise ValueError(
f"Original vocab size {cfg['vocab_size']} and new vocab size {len(self.tokenizer.encoder)} mismatched."
)
# self.state_dict['Wemb'].sha
self.state_keys = list(self.state_dict.keys())
if "Wtype" in self.state_dict:
raise ValueError("Wtype key in state dictionary")
self._check_layer_entries()
self.cfg = cfg
hidden_size, intermediate_shape = self.state_dict["encoder_l1_ffn_W1"].shape
if hidden_size != cfg["dim-emb"]:
raise ValueError(f"Hidden size {hidden_size} and configured size {cfg['dim_emb']} mismatched")
# Process decoder.yml
decoder_yml = cast_marian_config(load_yaml(source_dir / "decoder.yml"))
check_marian_cfg_assumptions(cfg)
self.hf_config = MarianConfig(
vocab_size=cfg["vocab_size"],
decoder_vocab_size=cfg.get("decoder_vocab_size", cfg["vocab_size"]),
share_encoder_decoder_embeddings=cfg["tied-embeddings-src"],
decoder_layers=cfg["dec-depth"],
encoder_layers=cfg["enc-depth"],
decoder_attention_heads=cfg["transformer-heads"],
encoder_attention_heads=cfg["transformer-heads"],
decoder_ffn_dim=cfg["transformer-dim-ffn"],
encoder_ffn_dim=cfg["transformer-dim-ffn"],
d_model=cfg["dim-emb"],
activation_function=cfg["transformer-ffn-activation"],
pad_token_id=self.pad_token_id,
eos_token_id=eos_token_id,
forced_eos_token_id=eos_token_id,
bos_token_id=0,
max_position_embeddings=cfg["dim-emb"],
scale_embedding=True,
normalize_embedding="n" in cfg["transformer-preprocess"],
static_position_embeddings=not cfg["transformer-train-position-embeddings"],
tie_word_embeddings=cfg["tied-embeddings"],
dropout=0.1, # see opus-mt-train repo/transformer-dropout param.
# default: add_final_layer_norm=False,
num_beams=decoder_yml["beam-size"],
decoder_start_token_id=self.pad_token_id,
bad_words_ids=[[self.pad_token_id]],
max_length=512,
)
def _check_layer_entries(self):
self.encoder_l1 = self.sub_keys("encoder_l1")
self.decoder_l1 = self.sub_keys("decoder_l1")
self.decoder_l2 = self.sub_keys("decoder_l2")
if len(self.encoder_l1) != 16:
warnings.warn(f"Expected 16 keys for each encoder layer, got {len(self.encoder_l1)}")
if len(self.decoder_l1) != 26:
warnings.warn(f"Expected 26 keys for each decoder layer, got {len(self.decoder_l1)}")
if len(self.decoder_l2) != 26:
warnings.warn(f"Expected 26 keys for each decoder layer, got {len(self.decoder_l1)}")
@property
def extra_keys(self):
extra = []
for k in self.state_keys:
if (
k.startswith("encoder_l")
or k.startswith("decoder_l")
or k in [CONFIG_KEY, "Wemb", "encoder_Wemb", "decoder_Wemb", "Wpos", "decoder_ff_logit_out_b"]
):
continue
else:
extra.append(k)
return extra
def sub_keys(self, layer_prefix):
return [remove_prefix(k, layer_prefix) for k in self.state_dict if k.startswith(layer_prefix)]
def load_tokenizer(self):
# save tokenizer
add_special_tokens_to_vocab(self.source_dir, not self.share_encoder_decoder_embeddings)
return MarianTokenizer.from_pretrained(str(self.source_dir))
def load_marian_model(self) -> MarianMTModel:
state_dict, cfg = self.state_dict, self.hf_config
if not cfg.static_position_embeddings:
raise ValueError("config.static_position_embeddings should be True")
model = MarianMTModel(cfg)
if "hidden_size" in cfg.to_dict():
raise ValueError("hidden_size is in config")
load_layers_(
model.model.encoder.layers,
state_dict,
BART_CONVERTER,
)
load_layers_(model.model.decoder.layers, state_dict, BART_CONVERTER, is_decoder=True)
# handle tensors not associated with layers
if self.cfg["tied-embeddings-src"]:
wemb_tensor = nn.Parameter(torch.FloatTensor(self.wemb))
bias_tensor = nn.Parameter(torch.FloatTensor(self.final_bias))
model.model.shared.weight = wemb_tensor
model.model.encoder.embed_tokens = model.model.decoder.embed_tokens = model.model.shared
else:
wemb_tensor = nn.Parameter(torch.FloatTensor(self.wemb))
model.model.encoder.embed_tokens.weight = wemb_tensor
decoder_wemb_tensor = nn.Parameter(torch.FloatTensor(self.dec_wemb))
bias_tensor = nn.Parameter(torch.FloatTensor(self.final_bias))
model.model.decoder.embed_tokens.weight = decoder_wemb_tensor
model.final_logits_bias = bias_tensor
if "Wpos" in state_dict:
print("Unexpected: got Wpos")
wpos_tensor = torch.tensor(state_dict["Wpos"])
model.model.encoder.embed_positions.weight = wpos_tensor
model.model.decoder.embed_positions.weight = wpos_tensor
if cfg.normalize_embedding:
if "encoder_emb_ln_scale_pre" not in state_dict:
raise ValueError("encoder_emb_ln_scale_pre is not in state dictionary")
raise NotImplementedError("Need to convert layernorm_embedding")
if self.extra_keys:
raise ValueError(f"Failed to convert {self.extra_keys}")
if model.get_input_embeddings().padding_idx != self.pad_token_id:
raise ValueError(
f"Padding tokens {model.get_input_embeddings().padding_idx} and {self.pad_token_id} mismatched"
)
return model
def download_and_unzip(url, dest_dir):
try:
import wget
except ImportError:
raise ImportError("you must pip install wget")
filename = wget.download(url)
unzip(filename, dest_dir)
os.remove(filename)
def convert(source_dir: Path, dest_dir):
dest_dir = Path(dest_dir)
dest_dir.mkdir(exist_ok=True)
opus_state = OpusState(source_dir)
# save tokenizer
opus_state.tokenizer.save_pretrained(dest_dir)
# save_json(opus_state.cfg, dest_dir / "marian_original_config.json")
# ^^ Uncomment to save human readable marian config for debugging
model = opus_state.load_marian_model()
model = model.half()
model.save_pretrained(dest_dir)
model.from_pretrained(dest_dir) # sanity check
def load_yaml(path):
import yaml
with open(path) as f:
return yaml.load(f, Loader=yaml.BaseLoader)
def save_json(content: Union[Dict, List], path: str) -> None:
with open(path, "w") as f:
json.dump(content, f)
def unzip(zip_path: str, dest_dir: str) -> None:
with ZipFile(zip_path, "r") as zipObj:
zipObj.extractall(dest_dir)
if __name__ == "__main__":
"""
Tatoeba conversion instructions in scripts/tatoeba/README.md
"""
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--src", type=str, help="path to marian model sub dir", default="en-de")
parser.add_argument("--dest", type=str, default=None, help="Path to the output PyTorch model.")
args = parser.parse_args()
source_dir = Path(args.src)
if not source_dir.exists():
raise ValueError(f"Source directory {source_dir} not found")
dest_dir = f"converted-{source_dir.name}" if args.dest is None else args.dest
convert(source_dir, dest_dir)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/marian/modeling_marian.py | # coding=utf-8
# Copyright 2021 The Marian Team Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch MarianMTModel model, ported from the Marian C++ repo."""
import copy
import math
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
Seq2SeqLMOutput,
Seq2SeqModelOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_marian import MarianConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "MarianConfig"
_CHECKPOINT_FOR_DOC = "Helsinki-NLP/opus-mt-en-de"
MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST = [
"Helsinki-NLP/opus-mt-en-de",
# See all Marian models at https://huggingface.co/models?filter=marian
]
# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
"""
Shift input ids one token to the right.
"""
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
shifted_input_ids[:, 0] = decoder_start_token_id
if pad_token_id is None:
raise ValueError("self.model.config.pad_token_id has to be defined.")
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids
class MarianSinusoidalPositionalEmbedding(nn.Embedding):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None) -> None:
super().__init__(num_positions, embedding_dim)
self.weight = self._init_weight(self.weight)
@staticmethod
def _init_weight(out: nn.Parameter) -> nn.Parameter:
"""
Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in
the 2nd half of the vector. [dim // 2:]
"""
n_pos, dim = out.shape
position_enc = np.array(
[[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)]
)
out.requires_grad = False # set early to avoid an error in pytorch-1.8+
sentinel = dim // 2 if dim % 2 == 0 else (dim // 2) + 1
out[:, 0:sentinel] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
out[:, sentinel:] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
out.detach_()
return out
@torch.no_grad()
def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0) -> torch.Tensor:
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
bsz, seq_len = input_ids_shape[:2]
positions = torch.arange(
past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
)
return super().forward(positions)
# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->Marian
class MarianAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
config: Optional[MarianConfig] = None,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
self.config = config
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.is_causal = is_causal
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support prefix tuning
if (
is_cross_attention
and past_key_value is not None
and past_key_value[0].shape[2] == key_value_states.shape[1]
):
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.reshape(*proj_shape)
value_states = value_states.reshape(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
# Copied from transformers.models.bart.modeling_bart.BartEncoderLayer with Bart->Marian, BART->MARIAN
class MarianEncoderLayer(nn.Module):
def __init__(self, config: MarianConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = MARIAN_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
dropout=config.attention_dropout,
config=config,
)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: torch.FloatTensor,
layer_head_mask: torch.FloatTensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states, attn_weights, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
if hidden_states.dtype == torch.float16 and (
torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
):
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
MARIAN_ATTENTION_CLASSES = {"eager": MarianAttention}
# Copied from transformers.models.bart.modeling_bart.BartDecoderLayer with Bart->Marian, BART->MARIAN
class MarianDecoderLayer(nn.Module):
def __init__(self, config: MarianConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = MARIAN_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
is_causal=True,
config=config,
)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = MARIAN_ATTENTION_CLASSES[config._attn_implementation](
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
config=config,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = True,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
size `(decoder_attention_heads,)`.
past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
if use_cache:
outputs += (present_key_value,)
return outputs
class MarianPreTrainedModel(PreTrainedModel):
config_class = MarianConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
def _init_weights(self, module: Union[nn.Linear, nn.Embedding, MarianSinusoidalPositionalEmbedding]):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, MarianSinusoidalPositionalEmbedding):
pass
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
@property
def dummy_inputs(self):
pad_token = self.config.pad_token_id
input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
dummy_inputs = {
"attention_mask": input_ids.ne(pad_token),
"input_ids": input_ids,
"decoder_input_ids": input_ids,
}
return dummy_inputs
MARIAN_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`MarianConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
MARIAN_GENERATION_EXAMPLE = r"""
Pytorch version of marian-nmt's transformer.h (c++). Designed for the OPUS-NMT translation checkpoints. Available
models are listed [here](https://huggingface.co/models?search=Helsinki-NLP).
Examples:
```python
>>> from transformers import AutoTokenizer, MarianMTModel
>>> src = "fr" # source language
>>> trg = "en" # target language
>>> model_name = f"Helsinki-NLP/opus-mt-{src}-{trg}"
>>> model = MarianMTModel.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> sample_text = "où est l'arrêt de bus ?"
>>> batch = tokenizer([sample_text], return_tensors="pt")
>>> generated_ids = model.generate(**batch)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
"Where's the bus stop?"
```
"""
MARIAN_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
Marian uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
input (see `past_key_values`). This is useful if you want more control over how to convert
`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
of `inputs_embeds`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class MarianEncoder(MarianPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`MarianEncoderLayer`].
Args:
config: MarianConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: MarianConfig, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
embed_dim = config.d_model
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
if embed_tokens is not None:
self.embed_tokens = embed_tokens
else:
self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
self.embed_positions = MarianSinusoidalPositionalEmbedding(
config.max_position_embeddings, embed_dim, self.padding_idx
)
self.layers = nn.ModuleList([MarianEncoderLayer(config) for _ in range(config.encoder_layers)])
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], BaseModelOutput]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(input_shape)
hidden_states = inputs_embeds + embed_pos
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
assert head_mask.size()[0] == (
len(self.layers)
), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
to_drop = False
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop: # skip the layer
to_drop = True
if to_drop:
layer_outputs = (None, None)
else:
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
attention_mask,
(head_mask[idx] if head_mask is not None else None),
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
class MarianDecoder(MarianPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`MarianDecoderLayer`]
Args:
config: MarianConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: MarianConfig, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self.padding_idx = config.pad_token_id
self.max_target_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
if embed_tokens is not None:
self.embed_tokens = embed_tokens
else:
self.embed_tokens = nn.Embedding(config.decoder_vocab_size, config.d_model, self.padding_idx)
self.embed_positions = MarianSinusoidalPositionalEmbedding(
config.max_position_embeddings, config.d_model, self.padding_idx
)
self.layers = nn.ModuleList([MarianDecoderLayer(config) for _ in range(config.decoder_layers)])
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
cross-attention on hidden heads. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
attention_mask = _prepare_4d_causal_attention_mask(
attention_mask, input_shape, inputs_embeds, past_key_values_length
)
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _prepare_4d_attention_mask(
encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
)
# embed positions
positions = self.embed_positions(input_shape, past_key_values_length)
hidden_states = inputs_embeds + positions
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
next_decoder_cache = () if use_cache else None
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
assert attn_mask.size()[0] == (len(self.layers)), (
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop:
continue
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
head_mask[idx] if head_mask is not None else None,
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
None,
output_attentions,
use_cache,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
@add_start_docstrings(
"The bare Marian Model outputting raw hidden-states without any specific head on top.", MARIAN_START_DOCSTRING
)
class MarianModel(MarianPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: MarianConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
# We always use self.shared for token embeddings to ensure compatibility with all marian models
self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
if self.config.share_encoder_decoder_embeddings:
encoder_embed_tokens = decoder_embed_tokens = self.shared
else:
# Since the embeddings are not shared, deepcopy the embeddings here for encoder
# and decoder to make sure they are not tied.
encoder_embed_tokens = copy.deepcopy(self.shared)
decoder_embed_tokens = copy.deepcopy(self.shared)
self.shared = None
self.encoder = MarianEncoder(config, encoder_embed_tokens)
self.decoder = MarianDecoder(config, decoder_embed_tokens)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
# This will return shared embeddings if they are shared else specific to encoder.
return self.get_encoder().get_input_embeddings()
def set_input_embeddings(self, value):
if self.config.share_encoder_decoder_embeddings:
self.shared = value
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
else: # if not shared only set encoder embeedings
self.encoder.embed_tokens = value
def get_decoder_input_embeddings(self):
if self.config.share_encoder_decoder_embeddings:
raise ValueError(
"`get_decoder_input_embeddings` should not be called if `config.share_encoder_decoder_embeddings` "
"is `True`. Please use `get_input_embeddings` instead."
)
return self.get_decoder().get_input_embeddings()
def set_decoder_input_embeddings(self, value):
if self.config.share_encoder_decoder_embeddings:
raise ValueError(
"`config.share_encoder_decoder_embeddings` is set to `True` meaning the decoder input embeddings "
"are shared with the encoder. In order to set the decoder input embeddings, you should simply set "
"the encoder input embeddings by calling `set_input_embeddings` with the appropriate embeddings."
)
self.decoder.embed_tokens = value
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
def resize_decoder_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
if self.config.share_encoder_decoder_embeddings:
raise ValueError(
"`resize_decoder_token_embeddings` should not be called if `config.share_encoder_decoder_embeddings` "
"is `True`. Please use `resize_token_embeddings` instead."
)
old_embeddings = self.get_decoder_input_embeddings()
new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
self.set_decoder_input_embeddings(new_embeddings)
model_embeds = self.get_decoder_input_embeddings()
if new_num_tokens is None:
return model_embeds
# Update base model and current model config
self.config.decoder_vocab_size = new_num_tokens
# Tie weights again if needed
self.tie_weights()
return model_embeds
@add_start_docstrings_to_model_forward(MARIAN_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Union[Tuple[torch.Tensor], BaseModelOutput]] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Seq2SeqModelOutput:
r"""
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, MarianModel
>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> model = MarianModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt")
>>> decoder_inputs = tokenizer(
... "<pad> Studien haben gezeigt dass es hilfreich ist einen Hund zu besitzen",
... return_tensors="pt",
... add_special_tokens=False,
... )
>>> outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_inputs.input_ids)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 26, 512]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return Seq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"The Marian Model with a language modeling head. Can be used for summarization.", MARIAN_START_DOCSTRING
)
class MarianMTModel(MarianPreTrainedModel):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = [
"final_logits_bias",
"encoder.embed_positions.weight",
"decoder.embed_positions.weight",
]
_keys_to_ignore_on_save = ["model.encoder.embed_positions.weight", "model.decoder.embed_positions.weight"]
_tied_weights_keys = ["model.encoder.embed_tokens.weight", "model.decoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config: MarianConfig):
super().__init__(config)
self.model = MarianModel(config)
target_vocab_size = config.vocab_size if config.share_encoder_decoder_embeddings else config.decoder_vocab_size
self.register_buffer("final_logits_bias", torch.zeros((1, target_vocab_size)))
self.lm_head = nn.Linear(config.d_model, target_vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_encoder(self):
return self.model.get_encoder()
def get_decoder(self):
return self.model.get_decoder()
def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
if self.config.share_encoder_decoder_embeddings:
self._resize_final_logits_bias(new_num_tokens)
return new_embeddings
def _resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of=None) -> nn.Embedding:
old_embeddings = self.get_input_embeddings()
new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens, pad_to_multiple_of)
self.set_input_embeddings(new_embeddings)
new_num_tokens = new_embeddings.weight.shape[0]
# update config.decoder_vocab_size if embeddings are tied
if self.config.share_encoder_decoder_embeddings:
self.config.decoder_vocab_size = new_num_tokens
# if word embeddings are not tied, make sure that lm head is resized as well
if (
self.config.share_encoder_decoder_embeddings
and self.get_output_embeddings() is not None
and not self.config.tie_word_embeddings
):
old_lm_head = self.get_output_embeddings()
new_lm_head = self._get_resized_lm_head(old_lm_head, new_num_tokens)
self.set_output_embeddings(new_lm_head)
return self.get_input_embeddings()
def resize_decoder_token_embeddings(self, new_num_tokens):
if self.config.share_encoder_decoder_embeddings:
raise ValueError(
"`resize_decoder_token_embeddings` should not be called if `config.share_encoder_decoder_embeddings` "
"is `True`. Please use `resize_token_embeddings` instead."
)
old_embeddings = self.model.get_decoder_input_embeddings()
new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
self.model.set_decoder_input_embeddings(new_embeddings)
# if word embeddings are not tied, make sure that lm head is resized as well
if self.get_output_embeddings() is not None and not self.config.tie_word_embeddings:
old_lm_head = self.get_output_embeddings()
new_lm_head = self._get_resized_lm_head(old_lm_head, new_num_tokens)
self.set_output_embeddings(new_lm_head)
model_embeds = self.model.get_decoder_input_embeddings()
if new_num_tokens is None:
return model_embeds
# Update base model and current model config
self.config.decoder_vocab_size = new_num_tokens
# Tie weights again if needed
self.tie_weights()
self._resize_final_logits_bias(new_num_tokens)
return model_embeds
def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
old_num_tokens = self.final_logits_bias.shape[-1]
if new_num_tokens <= old_num_tokens:
new_bias = self.final_logits_bias[:, :new_num_tokens]
else:
extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
self.register_buffer("final_logits_bias", new_bias)
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings: nn.Embedding):
self.lm_head = new_embeddings
def tie_weights(self):
"""
Tie the weights between the input embeddings and the output embeddings.
If the `torchscript` flag is set in the configuration, can't handle parameter sharing so we are cloning the
weights instead.
"""
output_embeddings = self.get_output_embeddings()
if output_embeddings is not None and getattr(self.config, "tie_word_embeddings", True):
# if embeddings are shared this will return shared embeddings otherwise decoder embed_tokens
word_embeddings = self.get_decoder().get_input_embeddings()
self._tie_or_clone_weights(output_embeddings, word_embeddings)
if getattr(self.config, "is_encoder_decoder", False) and getattr(self.config, "tie_encoder_decoder", False):
if hasattr(self, self.base_model_prefix):
self = getattr(self, self.base_model_prefix)
self._tie_encoder_decoder_weights(self.encoder, self.decoder, self.base_model_prefix)
for module in self.modules():
if hasattr(module, "_tie_weights"):
module._tie_weights()
@add_start_docstrings_to_model_forward(MARIAN_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(MARIAN_GENERATION_EXAMPLE)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Union[Tuple[torch.Tensor], BaseModelOutput]] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Seq2SeqLMOutput:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
if use_cache:
logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
use_cache = False
if decoder_input_ids is None and decoder_inputs_embeds is None:
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.decoder_vocab_size), labels.view(-1))
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return Seq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
def prepare_inputs_for_generation(
self,
decoder_input_ids: torch.LongTensor,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
encoder_outputs: Optional[Union[Tuple[torch.Tensor], BaseModelOutput]] = None,
**kwargs,
) -> Dict:
# cut decoder_input_ids if past is used
if past_key_values is not None:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if decoder_input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = decoder_input_ids.shape[1] - 1
decoder_input_ids = decoder_input_ids[:, remove_prefix_length:]
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past_key_values,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
}
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
# cached cross_attention states don't have to be reordered -> they are always the same
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2])
+ layer_past[2:],
)
return reordered_past
# Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->Marian
class MarianDecoderWrapper(MarianPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
self.decoder = MarianDecoder(config)
def forward(self, *args, **kwargs):
return self.decoder(*args, **kwargs)
# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->Marian, facebook/bart-base->Helsinki-NLP/opus-mt-fr-en
class MarianForCausalLM(MarianPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
config = copy.deepcopy(config)
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MarianDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.decoder.embed_tokens
def set_input_embeddings(self, value):
self.model.decoder.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model.decoder = decoder
def get_decoder(self):
return self.model.decoder
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
if the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, MarianForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-fr-en")
>>> model = MarianForCausalLM.from_pretrained("Helsinki-NLP/opus-mt-fr-en", add_cross_attention=False)
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size]
>>> list(logits.shape) == expected_shape
True
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
head_mask=head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = self.lm_head(outputs[0])
loss = None
if labels is not None:
labels = labels.to(logits.device)
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs
):
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_ids.shape)
if past_key_values:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_length:]
# first step, decoder_cached_states are empty
return {
"input_ids": input_ids, # encoder_outputs is defined. input_ids not needed
"attention_mask": attention_mask,
"past_key_values": past_key_values,
"use_cache": use_cache,
}
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/marian/__init__.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_import_structure = {
"configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_marian"] = ["MarianTokenizer"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_marian"] = [
"MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
"MarianForCausalLM",
"MarianModel",
"MarianMTModel",
"MarianPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_marian"] = ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_marian"] = ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]
if TYPE_CHECKING:
from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_marian import MarianTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_marian import (
MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
MarianForCausalLM,
MarianModel,
MarianMTModel,
MarianPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/marian/configuration_marian.py | # coding=utf-8
# Copyright 2021 The Marian Team Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Marian model configuration"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
logger = logging.get_logger(__name__)
MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
# See all Marian models at https://huggingface.co/models?filter=marian
}
class MarianConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MarianModel`]. It is used to instantiate an
Marian model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Marian
[Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 58101):
Vocabulary size of the Marian model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MarianModel`] or [`TFMarianModel`].
d_model (`int`, *optional*, defaults to 1024):
Dimensionality of the layers and the pooler layer.
encoder_layers (`int`, *optional*, defaults to 12):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 12):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
scale_embedding (`bool`, *optional*, defaults to `False`):
Scale embeddings by diving by sqrt(d_model).
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models)
forced_eos_token_id (`int`, *optional*, defaults to 0):
The id of the token to force as the last generated token when `max_length` is reached. Usually set to
`eos_token_id`.
Examples:
```python
>>> from transformers import MarianModel, MarianConfig
>>> # Initializing a Marian Helsinki-NLP/opus-mt-en-de style configuration
>>> configuration = MarianConfig()
>>> # Initializing a model from the Helsinki-NLP/opus-mt-en-de style configuration
>>> model = MarianModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "marian"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__(
self,
vocab_size=58101,
decoder_vocab_size=None,
max_position_embeddings=1024,
encoder_layers=12,
encoder_ffn_dim=4096,
encoder_attention_heads=16,
decoder_layers=12,
decoder_ffn_dim=4096,
decoder_attention_heads=16,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
use_cache=True,
is_encoder_decoder=True,
activation_function="gelu",
d_model=1024,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
init_std=0.02,
decoder_start_token_id=58100,
scale_embedding=False,
pad_token_id=58100,
eos_token_id=0,
forced_eos_token_id=0,
share_encoder_decoder_embeddings=True,
**kwargs,
):
self.vocab_size = vocab_size
self.decoder_vocab_size = decoder_vocab_size or vocab_size
self.max_position_embeddings = max_position_embeddings
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.init_std = init_std
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.use_cache = use_cache
self.num_hidden_layers = encoder_layers
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
self.share_encoder_decoder_embeddings = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
decoder_start_token_id=decoder_start_token_id,
forced_eos_token_id=forced_eos_token_id,
**kwargs,
)
class MarianOnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
]
)
if self.use_past:
common_inputs["decoder_input_ids"] = {0: "batch"}
common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(common_inputs, direction="inputs")
elif self.task == "causal-lm":
# TODO: figure this case out.
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
]
)
if self.use_past:
num_encoder_layers, _ = self.num_layers
for i in range(num_encoder_layers):
common_inputs[f"past_key_values.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
common_inputs[f"past_key_values.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}
else:
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
]
)
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def outputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
common_outputs = super().outputs
else:
common_outputs = super(OnnxConfigWithPast, self).outputs
if self.use_past:
num_encoder_layers, _ = self.num_layers
for i in range(num_encoder_layers):
common_outputs[f"present.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
common_outputs[f"present.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def _generate_dummy_inputs_for_default_and_seq2seq_lm(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
encoder_inputs = self._generate_dummy_inputs_for_encoder_and_decoder(
tokenizer, batch_size, seq_length, is_pair, framework
)
# Generate decoder inputs
decoder_seq_length = seq_length if not self.use_past else 1
decoder_inputs = self._generate_dummy_inputs_for_encoder_and_decoder(
tokenizer, batch_size, decoder_seq_length, is_pair, framework
)
decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
common_inputs = dict(**encoder_inputs, **decoder_inputs)
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
batch, encoder_seq_length = common_inputs["input_ids"].shape
decoder_seq_length = common_inputs["decoder_input_ids"].shape[1]
num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads
encoder_shape = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
decoder_past_length = decoder_seq_length + 3
decoder_shape = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
common_inputs["decoder_attention_mask"] = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1
)
common_inputs["past_key_values"] = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
num_encoder_layers, num_decoder_layers = self.num_layers
min_num_layers = min(num_encoder_layers, num_decoder_layers)
max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers
remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(min_num_layers):
common_inputs["past_key_values"].append(
(
torch.zeros(decoder_shape),
torch.zeros(decoder_shape),
torch.zeros(encoder_shape),
torch.zeros(encoder_shape),
)
)
# TODO: test this.
shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(min_num_layers, max_num_layers):
common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape)))
return common_inputs
def _generate_dummy_inputs_for_causal_lm(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
common_inputs = self._generate_dummy_inputs_for_encoder_and_decoder(
tokenizer, batch_size, seq_length, is_pair, framework
)
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
batch, seqlen = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
past_key_values_length = seqlen + 2
num_encoder_layers, _ = self.num_layers
num_encoder_attention_heads, _ = self.num_attention_heads
past_shape = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
mask_dtype = common_inputs["attention_mask"].dtype
common_inputs["attention_mask"] = torch.cat(
[common_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
)
common_inputs["past_key_values"] = [
(torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(num_encoder_layers)
]
return common_inputs
# Copied from BartOnnxConfig._generate_dummy_inputs_for_sequence_classification_and_question_answering
# We renamed this function because Marian models do not have a sequence classification or question answering head
def _generate_dummy_inputs_for_encoder_and_decoder(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
batch_size = compute_effective_axis_dimension(
batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0
)
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
token_to_add = tokenizer.num_special_tokens_to_add(is_pair)
seq_length = compute_effective_axis_dimension(
seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add
)
# Generate dummy inputs according to compute batch and sequence
dummy_input = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
common_inputs = dict(tokenizer(dummy_input, return_tensors=framework))
return common_inputs
def generate_dummy_inputs(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
common_inputs = self._generate_dummy_inputs_for_default_and_seq2seq_lm(
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
)
else:
common_inputs = self._generate_dummy_inputs_for_causal_lm(
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
)
return common_inputs
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig._flatten_past_key_values_
def _flatten_past_key_values_(self, flattened_output, name, idx, t):
if self.task in ["default", "seq2seq-lm"]:
flattened_output = super()._flatten_past_key_values_(flattened_output, name, idx, t)
else:
flattened_output = super(OnnxSeq2SeqConfigWithPast, self)._flatten_past_key_values_(
flattened_output, name, idx, t
)
@property
def atol_for_validation(self) -> float:
return 1e-4
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/marian/tokenization_marian.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"source_spm": "source.spm",
"target_spm": "target.spm",
"vocab": "vocab.json",
"target_vocab_file": "target_vocab.json",
"tokenizer_config_file": "tokenizer_config.json",
}
PRETRAINED_VOCAB_FILES_MAP = {
"source_spm": {
"Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"
},
"target_spm": {
"Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"
},
"vocab": {
"Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"
},
"tokenizer_config_file": {
"Helsinki-NLP/opus-mt-en-de": (
"https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"
)
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"Helsinki-NLP/opus-mt-en-de": 512}
PRETRAINED_INIT_CONFIGURATION = {}
SPIECE_UNDERLINE = "▁"
# Example URL https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json
class MarianTokenizer(PreTrainedTokenizer):
r"""
Construct a Marian tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
source_spm (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
contains the vocabulary for the source language.
target_spm (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
contains the vocabulary for the target language.
source_lang (`str`, *optional*):
A string representing the source language.
target_lang (`str`, *optional*):
A string representing the target language.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
model_max_length (`int`, *optional*, defaults to 512):
The maximum sentence length the model accepts.
additional_special_tokens (`List[str]`, *optional*, defaults to `["<eop>", "<eod>"]`):
Additional special tokens used by the tokenizer.
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
Examples:
```python
>>> from transformers import MarianForCausalLM, MarianTokenizer
>>> model = MarianForCausalLM.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> src_texts = ["I am a small frog.", "Tom asked his teacher for advice."]
>>> tgt_texts = ["Ich bin ein kleiner Frosch.", "Tom bat seinen Lehrer um Rat."] # optional
>>> inputs = tokenizer(src_texts, text_target=tgt_texts, return_tensors="pt", padding=True)
>>> outputs = model(**inputs) # should work
```"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
language_code_re = re.compile(">>.+<<") # type: re.Pattern
def __init__(
self,
source_spm,
target_spm,
vocab,
target_vocab_file=None,
source_lang=None,
target_lang=None,
unk_token="<unk>",
eos_token="</s>",
pad_token="<pad>",
model_max_length=512,
sp_model_kwargs: Optional[Dict[str, Any]] = None,
separate_vocabs=False,
**kwargs,
) -> None:
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
assert Path(source_spm).exists(), f"cannot find spm source {source_spm}"
self.separate_vocabs = separate_vocabs
self.encoder = load_json(vocab)
if str(unk_token) not in self.encoder:
raise KeyError("<unk> token must be in the vocab")
assert str(pad_token) in self.encoder
if separate_vocabs:
self.target_encoder = load_json(target_vocab_file)
self.decoder = {v: k for k, v in self.target_encoder.items()}
self.supported_language_codes = []
else:
self.decoder = {v: k for k, v in self.encoder.items()}
self.supported_language_codes: list = [k for k in self.encoder if k.startswith(">>") and k.endswith("<<")]
self.source_lang = source_lang
self.target_lang = target_lang
self.spm_files = [source_spm, target_spm]
# load SentencePiece model for pre-processing
self.spm_source = load_spm(source_spm, self.sp_model_kwargs)
self.spm_target = load_spm(target_spm, self.sp_model_kwargs)
self.current_spm = self.spm_source
self.current_encoder = self.encoder
# Multilingual target side: default to using first supported language code.
self._setup_normalizer()
super().__init__(
# bos_token=bos_token, unused. Start decoding with config.decoder_start_token_id
source_lang=source_lang,
target_lang=target_lang,
unk_token=unk_token,
eos_token=eos_token,
pad_token=pad_token,
model_max_length=model_max_length,
sp_model_kwargs=self.sp_model_kwargs,
target_vocab_file=target_vocab_file,
separate_vocabs=separate_vocabs,
**kwargs,
)
def _setup_normalizer(self):
try:
from sacremoses import MosesPunctNormalizer
self.punc_normalizer = MosesPunctNormalizer(self.source_lang).normalize
except (ImportError, FileNotFoundError):
warnings.warn("Recommended: pip install sacremoses.")
self.punc_normalizer = lambda x: x
def normalize(self, x: str) -> str:
"""Cover moses empty string edge case. They return empty list for '' input!"""
return self.punc_normalizer(x) if x else ""
def _convert_token_to_id(self, token):
return self.current_encoder.get(token, self.current_encoder[self.unk_token])
def remove_language_code(self, text: str):
"""Remove language codes like >>fr<< before sentencepiece"""
match = self.language_code_re.match(text)
code: list = [match.group(0)] if match else []
return code, self.language_code_re.sub("", text)
def _tokenize(self, text: str) -> List[str]:
code, text = self.remove_language_code(text)
pieces = self.current_spm.encode(text, out_type=str)
return code + pieces
def _convert_id_to_token(self, index: int) -> str:
"""Converts an index (integer) in a token (str) using the decoder."""
return self.decoder.get(index, self.unk_token)
def batch_decode(self, sequences, **kwargs):
"""
Convert a list of lists of token ids into a list of strings by calling decode.
Args:
sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (`bool`, *optional*):
Whether or not to clean up the tokenization spaces. If `None`, will default to
`self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
use_source_tokenizer (`bool`, *optional*, defaults to `False`):
Whether or not to use the source tokenizer to decode sequences (only applicable in sequence-to-sequence
problems).
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific decode method.
Returns:
`List[str]`: The list of decoded sentences.
"""
return super().batch_decode(sequences, **kwargs)
def decode(self, token_ids, **kwargs):
"""
Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces.
Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.
Args:
token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (`bool`, *optional*):
Whether or not to clean up the tokenization spaces. If `None`, will default to
`self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
use_source_tokenizer (`bool`, *optional*, defaults to `False`):
Whether or not to use the source tokenizer to decode sequences (only applicable in sequence-to-sequence
problems).
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific decode method.
Returns:
`str`: The decoded sentence.
"""
return super().decode(token_ids, **kwargs)
def convert_tokens_to_string(self, tokens: List[str]) -> str:
"""Uses source spm if _decode_use_source_tokenizer is True, and target spm otherwise"""
sp_model = self.spm_source if self._decode_use_source_tokenizer else self.spm_target
current_sub_tokens = []
out_string = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += sp_model.decode_pieces(current_sub_tokens) + token + " "
current_sub_tokens = []
else:
current_sub_tokens.append(token)
out_string += sp_model.decode_pieces(current_sub_tokens)
out_string = out_string.replace(SPIECE_UNDERLINE, " ")
return out_string.strip()
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
"""Build model inputs from a sequence by appending eos_token_id."""
if token_ids_1 is None:
return token_ids_0 + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_0 + token_ids_1 + [self.eos_token_id]
def _switch_to_input_mode(self):
self.current_spm = self.spm_source
self.current_encoder = self.encoder
def _switch_to_target_mode(self):
self.current_spm = self.spm_target
if self.separate_vocabs:
self.current_encoder = self.target_encoder
@property
def vocab_size(self) -> int:
return len(self.encoder)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
saved_files = []
if self.separate_vocabs:
out_src_vocab_file = os.path.join(
save_directory,
(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"],
)
out_tgt_vocab_file = os.path.join(
save_directory,
(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"],
)
save_json(self.encoder, out_src_vocab_file)
save_json(self.target_encoder, out_tgt_vocab_file)
saved_files.append(out_src_vocab_file)
saved_files.append(out_tgt_vocab_file)
else:
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]
)
save_json(self.encoder, out_vocab_file)
saved_files.append(out_vocab_file)
for spm_save_filename, spm_orig_path, spm_model in zip(
[VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]],
self.spm_files,
[self.spm_source, self.spm_target],
):
spm_save_path = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + spm_save_filename
)
if os.path.abspath(spm_orig_path) != os.path.abspath(spm_save_path) and os.path.isfile(spm_orig_path):
copyfile(spm_orig_path, spm_save_path)
saved_files.append(spm_save_path)
elif not os.path.isfile(spm_orig_path):
with open(spm_save_path, "wb") as fi:
content_spiece_model = spm_model.serialized_model_proto()
fi.write(content_spiece_model)
saved_files.append(spm_save_path)
return tuple(saved_files)
def get_vocab(self) -> Dict:
return self.get_src_vocab()
def get_src_vocab(self):
return dict(self.encoder, **self.added_tokens_encoder)
def get_tgt_vocab(self):
return dict(self.target_encoder, **self.added_tokens_decoder)
def __getstate__(self) -> Dict:
state = self.__dict__.copy()
state.update(
{k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]}
)
return state
def __setstate__(self, d: Dict) -> None:
self.__dict__ = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.spm_source, self.spm_target = (load_spm(f, self.sp_model_kwargs) for f in self.spm_files)
self.current_spm = self.spm_source
self._setup_normalizer()
def num_special_tokens_to_add(self, *args, **kwargs):
"""Just EOS"""
return 1
def _special_token_mask(self, seq):
all_special_ids = set(self.all_special_ids) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def get_special_tokens_mask(
self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""Get list where entries are [1] if a token is [eos] or [pad] else 0."""
if already_has_special_tokens:
return self._special_token_mask(token_ids_0)
elif token_ids_1 is None:
return self._special_token_mask(token_ids_0) + [1]
else:
return self._special_token_mask(token_ids_0 + token_ids_1) + [1]
def load_spm(path: str, sp_model_kwargs: Dict[str, Any]) -> sentencepiece.SentencePieceProcessor:
spm = sentencepiece.SentencePieceProcessor(**sp_model_kwargs)
spm.Load(path)
return spm
def save_json(data, path: str) -> None:
with open(path, "w") as f:
json.dump(data, f, indent=2)
def load_json(path: str) -> Union[Dict, List]:
with open(path, "r") as f:
return json.load(f)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/marian/modeling_tf_marian.py | # coding=utf-8
# Copyright 2021 The Marian Team Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 Marian model."""
from __future__ import annotations
import random
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
# Public API
from ...modeling_tf_utils import (
TFCausalLanguageModelingLoss,
TFPreTrainedModel,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_marian import MarianConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "Helsinki-NLP/opus-mt-en-de"
_CONFIG_FOR_DOC = "MarianConfig"
LARGE_NEGATIVE = -1e8
# Copied from transformers.models.bart.modeling_tf_bart.shift_tokens_right
def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int):
pad_token_id = tf.cast(pad_token_id, input_ids.dtype)
decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype)
start_tokens = tf.fill(
(shape_list(input_ids)[0], 1), tf.convert_to_tensor(decoder_start_token_id, input_ids.dtype)
)
shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1)
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids = tf.where(
shifted_input_ids == -100,
tf.fill(shape_list(shifted_input_ids), tf.convert_to_tensor(pad_token_id, input_ids.dtype)),
shifted_input_ids,
)
# "Verify that `labels` has only positive values and -100"
assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype))
# Make sure the assertion op is called by wrapping the result in an identity no-op
with tf.control_dependencies([assert_gte0]):
shifted_input_ids = tf.identity(shifted_input_ids)
return shifted_input_ids
# Copied from transformers.models.bart.modeling_tf_bart._make_causal_mask
def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz = input_ids_shape[0]
tgt_len = input_ids_shape[1]
mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE
mask_cond = tf.range(shape_list(mask)[-1])
mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask)
if past_key_values_length > 0:
mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1)
return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1))
# Copied from transformers.models.bart.modeling_tf_bart._expand_mask
def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
src_len = shape_list(mask)[1]
tgt_len = tgt_len if tgt_len is not None else src_len
one_cst = tf.constant(1.0)
mask = tf.cast(mask, dtype=one_cst.dtype)
expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
return (one_cst - expanded_mask) * LARGE_NEGATIVE
class TFMarianSinusoidalPositionalEmbedding(tf.keras.layers.Layer):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, **kwargs):
super().__init__(**kwargs)
if embedding_dim % 2 != 0:
raise NotImplementedError(f"odd embedding_dim {embedding_dim} not supported")
self.embedding_dim = embedding_dim
self.num_positions = num_positions
def build(self, input_shape: tf.TensorShape):
"""
Build shared token embedding layer Shared weights logic adapted from
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
"""
weight = self._init_weight(self.num_positions, self.embedding_dim)
self.weight = self.add_weight(
name="embeddings",
shape=[self.num_positions, self.embedding_dim],
)
weight = tf.cast(weight, dtype=self.weight.dtype)
self.weight.assign(weight)
super().build(input_shape)
@staticmethod
def _init_weight(n_pos: int, dim: int):
"""
Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in
the 2nd half of the vector. [dim // 2:]
"""
position_enc = np.array(
[[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)]
)
table = np.zeros_like(position_enc)
# index 0 is all zero
table[:, 0 : dim // 2] = np.sin(position_enc[:, 0::2])
table[:, dim // 2 :] = np.cos(position_enc[:, 1::2])
# convert to tensor
table = tf.convert_to_tensor(table)
tf.stop_gradient(table)
return table
def call(
self, input_shape: tf.TensorShape, past_key_values_length: int = 0, position_ids: tf.Tensor | None = None
):
"""Input is expected to be of size [bsz x seqlen]."""
if position_ids is None:
seq_len = input_shape[1]
position_ids = tf.range(past_key_values_length, seq_len + past_key_values_length, delta=1, name="range")
return tf.gather(self.weight, position_ids)
# Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention with Bart->Marian
class TFMarianAttention(tf.keras.layers.Layer):
"""Multi-headed attention from "Attention Is All You Need"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
**kwargs,
):
super().__init__(**kwargs)
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = tf.keras.layers.Dropout(dropout)
self.head_dim = embed_dim // num_heads
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
self.q_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
self.v_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
self.out_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj")
def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3))
def call(
self,
hidden_states: tf.Tensor,
key_value_states: tf.Tensor | None = None,
past_key_value: Tuple[Tuple[tf.Tensor]] | None = None,
attention_mask: tf.Tensor | None = None,
layer_head_mask: tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Tuple[tf.Tensor, tf.Tensor | None]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, embed_dim = shape_list(hidden_states)
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = tf.concat([past_key_value[0], key_states], axis=2)
value_states = tf.concat([past_key_value[1], value_states], axis=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
key_states = tf.reshape(key_states, proj_shape)
value_states = tf.reshape(value_states, proj_shape)
src_len = shape_list(key_states)[1]
attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
tf.debugging.assert_equal(
shape_list(attn_weights),
[bsz * self.num_heads, tgt_len, src_len],
message=(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {shape_list(attn_weights)}"
),
)
if attention_mask is not None:
tf.debugging.assert_equal(
shape_list(attention_mask),
[bsz, 1, tgt_len, src_len],
message=(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
f" {shape_list(attention_mask)}"
),
)
attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype)
attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
attn_weights = stable_softmax(attn_weights, axis=-1)
if layer_head_mask is not None:
tf.debugging.assert_equal(
shape_list(layer_head_mask),
[self.num_heads],
message=(
f"Head mask for a single layer should be of size {(self.num_heads)}, but is"
f" {shape_list(layer_head_mask)}"
),
)
attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape(
attn_weights, (bsz, self.num_heads, tgt_len, src_len)
)
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
attn_probs = self.dropout(attn_weights, training=training)
attn_output = tf.matmul(attn_probs, value_states)
tf.debugging.assert_equal(
shape_list(attn_output),
[bsz * self.num_heads, tgt_len, self.head_dim],
message=(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {shape_list(attn_output)}"
),
)
attn_output = tf.transpose(
tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3)
)
attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
attn_output = self.out_proj(attn_output)
attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
return attn_output, attn_weights, past_key_value
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "k_proj", None) is not None:
with tf.name_scope(self.k_proj.name):
self.k_proj.build([None, None, self.embed_dim])
if getattr(self, "q_proj", None) is not None:
with tf.name_scope(self.q_proj.name):
self.q_proj.build([None, None, self.embed_dim])
if getattr(self, "v_proj", None) is not None:
with tf.name_scope(self.v_proj.name):
self.v_proj.build([None, None, self.embed_dim])
if getattr(self, "out_proj", None) is not None:
with tf.name_scope(self.out_proj.name):
self.out_proj.build([None, None, self.embed_dim])
# Copied from transformers.models.bart.modeling_tf_bart.TFBartEncoderLayer with Bart->Marian
class TFMarianEncoderLayer(tf.keras.layers.Layer):
def __init__(self, config: MarianConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFMarianAttention(
self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="self_attn"
)
self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.activation_fn = get_tf_activation(config.activation_function)
self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout)
self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
self.config = config
def call(
self,
hidden_states: tf.Tensor,
attention_mask: np.ndarray | tf.Tensor | None,
layer_head_mask: tf.Tensor | None,
training: Optional[bool] = False,
) -> tf.Tensor:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`tf.Tensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`
"""
residual = hidden_states
hidden_states, self_attn_weights, _ = self.self_attn(
hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask
)
tf.debugging.assert_equal(
shape_list(hidden_states),
shape_list(residual),
message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}",
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout(hidden_states, training=training)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
return hidden_states, self_attn_weights
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self_attn", None) is not None:
with tf.name_scope(self.self_attn.name):
self.self_attn.build(None)
if getattr(self, "self_attn_layer_norm", None) is not None:
with tf.name_scope(self.self_attn_layer_norm.name):
self.self_attn_layer_norm.build([None, None, self.embed_dim])
if getattr(self, "fc1", None) is not None:
with tf.name_scope(self.fc1.name):
self.fc1.build([None, None, self.embed_dim])
if getattr(self, "fc2", None) is not None:
with tf.name_scope(self.fc2.name):
self.fc2.build([None, None, self.config.encoder_ffn_dim])
if getattr(self, "final_layer_norm", None) is not None:
with tf.name_scope(self.final_layer_norm.name):
self.final_layer_norm.build([None, None, self.embed_dim])
# Copied from transformers.models.bart.modeling_tf_bart.TFBartDecoderLayer with Bart->Marian
class TFMarianDecoderLayer(tf.keras.layers.Layer):
def __init__(self, config: MarianConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFMarianAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
name="self_attn",
is_decoder=True,
)
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.activation_fn = get_tf_activation(config.activation_function)
self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout)
self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
self.encoder_attn = TFMarianAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
name="encoder_attn",
is_decoder=True,
)
self.encoder_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm")
self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
self.config = config
def call(
self,
hidden_states: tf.Tensor,
attention_mask: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
layer_head_mask: tf.Tensor | None = None,
cross_attn_layer_head_mask: tf.Tensor | None = None,
past_key_value: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
training: Optional[bool] = False,
) -> Tuple[tf.Tensor, tf.Tensor, Tuple[Tuple[tf.Tensor]]]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`tf.Tensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
encoder_hidden_states (`tf.Tensor`):
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`tf.Tensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
`(decoder_attention_heads,)`
cross_attn_layer_head_mask (`tf.Tensor`): mask for heads of the cross-attention module.
`(decoder_attention_heads,)`
past_key_value (`Tuple(tf.Tensor)`): cached past key and value projection states
"""
residual = hidden_states
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout(hidden_states, training=training)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
return (
hidden_states,
self_attn_weights,
cross_attn_weights,
present_key_value,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self_attn", None) is not None:
with tf.name_scope(self.self_attn.name):
self.self_attn.build(None)
if getattr(self, "self_attn_layer_norm", None) is not None:
with tf.name_scope(self.self_attn_layer_norm.name):
self.self_attn_layer_norm.build([None, None, self.embed_dim])
if getattr(self, "encoder_attn", None) is not None:
with tf.name_scope(self.encoder_attn.name):
self.encoder_attn.build(None)
if getattr(self, "encoder_attn_layer_norm", None) is not None:
with tf.name_scope(self.encoder_attn_layer_norm.name):
self.encoder_attn_layer_norm.build([None, None, self.embed_dim])
if getattr(self, "fc1", None) is not None:
with tf.name_scope(self.fc1.name):
self.fc1.build([None, None, self.embed_dim])
if getattr(self, "fc2", None) is not None:
with tf.name_scope(self.fc2.name):
self.fc2.build([None, None, self.config.decoder_ffn_dim])
if getattr(self, "final_layer_norm", None) is not None:
with tf.name_scope(self.final_layer_norm.name):
self.final_layer_norm.build([None, None, self.embed_dim])
class TFMarianPreTrainedModel(TFPreTrainedModel):
config_class = MarianConfig
base_model_prefix = "model"
MARIAN_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
<Tip>
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Note that when creating models and layers with
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
</Tip>
Args:
config ([`MarianConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""
MARIAN_GENERATION_EXAMPLE = r"""
TF version of marian-nmt's transformer.h (c++). Designed for the OPUS-NMT translation checkpoints. Available
models are listed [here](https://huggingface.co/models?search=Helsinki-NLP).
Examples:
```python
>>> from transformers import AutoTokenizer, TFMarianMTModel
>>> from typing import List
>>> src = "fr" # source language
>>> trg = "en" # target language
>>> sample_text = "où est l'arrêt de bus ?"
>>> model_name = f"Helsinki-NLP/opus-mt-{src}-{trg}"
>>> model = TFMarianMTModel.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> batch = tokenizer([sample_text], return_tensors="tf")
>>> gen = model.generate(**batch)
>>> tokenizer.batch_decode(gen, skip_special_tokens=True)
"Where is the bus stop ?"
```
"""
MARIAN_INPUTS_DOCSTRING = r"""
Args:
input_ids (`tf.Tensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
Marian uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
decoder_attention_mask (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
decoder_position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embeddings - 1]`.
head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
decoder_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
encoder_outputs (`tf.FloatTensor`, *optional*):
hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
of shape `(batch_size, sequence_length, hidden_size)` is a sequence of
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
use_cache (`bool`, *optional*, defaults to `True`):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`). Set to `False` during training, `True` during generation
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@keras_serializable
class TFMarianEncoder(tf.keras.layers.Layer):
config_class = MarianConfig
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TFMarianEncoderLayer`].
Args:
config: MarianConfig
"""
def __init__(self, config: MarianConfig, embed_tokens: Optional[tf.keras.layers.Embedding] = None, **kwargs):
super().__init__(**kwargs)
self.config = config
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.layerdrop = config.encoder_layerdrop
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
self.embed_tokens = embed_tokens
self.embed_positions = TFMarianSinusoidalPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
name="embed_positions",
)
self.layers = [TFMarianEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
def get_embed_tokens(self):
return self.embed_tokens
def set_embed_tokens(self, embed_tokens):
self.embed_tokens = embed_tokens
@unpack_inputs
def call(
self,
input_ids: tf.Tensor | None = None,
inputs_embeds: tf.Tensor | None = None,
attention_mask: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
):
"""
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, `optional):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value
in the config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail. This argument can be used only in eager mode, in graph mode the value in the config
will be used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
in eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(input_shape)
hidden_states = inputs_embeds + embed_pos
hidden_states = self.dropout(hidden_states, training=training)
# check attention mask and invert
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask)
else:
attention_mask = None
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
tf.debugging.assert_equal(
shape_list(head_mask)[0],
len(self.layers),
message=(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
f" {shape_list(head_mask)[0]}."
),
)
# encoder layers
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if training and (dropout_probability < self.layerdrop): # skip the layer
continue
hidden_states, attn = encoder_layer(
hidden_states,
attention_mask,
head_mask[idx] if head_mask is not None else None,
)
if output_attentions:
all_attentions += (attn,)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embed_positions", None) is not None:
with tf.name_scope(self.embed_positions.name):
self.embed_positions.build(None)
if getattr(self, "layers", None) is not None:
for layer in self.layers:
with tf.name_scope(layer.name):
layer.build(None)
@keras_serializable
class TFMarianDecoder(tf.keras.layers.Layer):
config_class = MarianConfig
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFMarianDecoderLayer`]
Args:
config: MarianConfig
embed_tokens: output embedding
"""
def __init__(self, config: MarianConfig, embed_tokens: Optional[tf.keras.layers.Embedding] = None, **kwargs):
super().__init__(**kwargs)
self.config = config
self.padding_idx = config.pad_token_id
self.embed_tokens = embed_tokens
self.layerdrop = config.decoder_layerdrop
self.embed_positions = TFMarianSinusoidalPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
name="embed_positions",
)
self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
self.layers = [TFMarianDecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)]
self.dropout = tf.keras.layers.Dropout(config.dropout)
def get_embed_tokens(self):
return self.embed_tokens
def set_embed_tokens(self, embed_tokens):
self.embed_tokens = embed_tokens
@unpack_inputs
def call(
self,
input_ids: tf.Tensor | None = None,
inputs_embeds: tf.Tensor | None = None,
attention_mask: tf.Tensor | None = None,
position_ids: tf.Tensor | None = None,
encoder_hidden_states: tf.Tensor | None = None,
encoder_attention_mask: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
cross_attn_head_mask: tf.Tensor | None = None,
past_key_values: Tuple[Tuple[tf.Tensor]] | None = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
):
r"""
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embeddings - 1]`.
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`tf.Tensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up
decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value
in the config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail. This argument can be used only in eager mode, in graph mode the value in the config
will be used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
in eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
past_key_values_length = shape_list(past_key_values[0][0])[2] if past_key_values is not None else 0
# embed positions
if position_ids is None:
positions = self.embed_positions(input_shape, past_key_values_length)
else:
positions = self.embed_positions(input_shape, position_ids=position_ids)
if inputs_embeds is None:
check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
hidden_states = inputs_embeds
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
else:
combined_attention_mask = _expand_mask(
tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1]
)
if attention_mask is not None:
combined_attention_mask = combined_attention_mask + _expand_mask(attention_mask, tgt_len=input_shape[-1])
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _expand_mask(encoder_attention_mask, tgt_len=input_shape[-1])
hidden_states = self.dropout(hidden_states + positions, training=training)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attns = () if (output_attentions and encoder_hidden_states is not None) else None
present_key_values = () if use_cache else None
# check if head_mask and cross_attn_head_mask have a correct number of layers specified if desired
for attn_name, attn_mask in [("head_mask", head_mask), ("cross_attn_head_mask", cross_attn_head_mask)]:
if attn_mask is not None:
tf.debugging.assert_equal(
shape_list(attn_mask)[0],
len(self.layers),
message=(
f"The {attn_name} should be specified for {len(self.layers)} layers, but it is for"
f" {shape_list(attn_mask)[0]}."
),
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
dropout_probability = random.uniform(0, 1)
if training and (dropout_probability < self.layerdrop):
continue
past_key_value = past_key_values[idx] if past_key_values is not None else None
hidden_states, layer_self_attn, layer_cross_attn, present_key_value = decoder_layer(
hidden_states,
attention_mask=combined_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
layer_head_mask=head_mask[idx] if head_mask is not None else None,
cross_attn_layer_head_mask=cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
past_key_value=past_key_value,
)
if use_cache:
present_key_values += (present_key_value,)
if output_attentions:
all_self_attns += (layer_self_attn,)
if encoder_hidden_states is not None:
all_cross_attns += (layer_cross_attn,)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if not return_dict:
return hidden_states, present_key_values, all_hidden_states, all_self_attns, all_cross_attns
else:
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=present_key_values,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attns,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embed_positions", None) is not None:
with tf.name_scope(self.embed_positions.name):
self.embed_positions.build(None)
if getattr(self, "layers", None) is not None:
for layer in self.layers:
with tf.name_scope(layer.name):
layer.build(None)
@keras_serializable
class TFMarianMainLayer(tf.keras.layers.Layer):
config_class = MarianConfig
def __init__(self, config: MarianConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.shared = tf.keras.layers.Embedding(
input_dim=config.vocab_size,
output_dim=config.d_model,
embeddings_initializer=tf.keras.initializers.TruncatedNormal(stddev=self.config.init_std),
name="model.shared",
)
# Additional attribute to specify the expected name scope of the layer (for loading/storing weights)
self.shared.load_weight_prefix = "model.shared"
self.encoder = TFMarianEncoder(config, self.shared, name="encoder")
self.decoder = TFMarianDecoder(config, self.shared, name="decoder")
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, new_embeddings):
self.shared = new_embeddings
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
@unpack_inputs
def call(
self,
input_ids: tf.Tensor | None = None,
attention_mask: tf.Tensor | None = None,
decoder_input_ids: tf.Tensor | None = None,
decoder_attention_mask: tf.Tensor | None = None,
decoder_position_ids: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
decoder_head_mask: tf.Tensor | None = None,
cross_attn_head_mask: tf.Tensor | None = None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values: Tuple[Tuple[tf.Tensor]] = None,
inputs_embeds: tf.Tensor | None = None,
decoder_inputs_embeds: tf.Tensor | None = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
**kwargs,
):
if decoder_input_ids is None and decoder_inputs_embeds is None:
use_cache = False
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, TFBaseModelOutput):
encoder_outputs = TFBaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
# If the user passed a TFBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False
elif not return_dict and not isinstance(encoder_outputs, tuple):
encoder_outputs = encoder_outputs.to_tuple()
decoder_outputs = self.decoder(
decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return TFSeq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
# The shared/tied weights expect to be in the model base namespace
# Adding "/" to the end (not the start!) of a tf.name_scope puts it in the root namespace rather than
# the current one.
with tf.name_scope(self.shared.load_weight_prefix + "/" + self.shared.name + "/"):
self.shared.build(None)
if getattr(self, "encoder", None) is not None:
with tf.name_scope(self.encoder.name):
self.encoder.build(None)
if getattr(self, "decoder", None) is not None:
with tf.name_scope(self.decoder.name):
self.decoder.build(None)
@add_start_docstrings(
"The bare MARIAN Model outputting raw hidden-states without any specific head on top.",
MARIAN_START_DOCSTRING,
)
class TFMarianModel(TFMarianPreTrainedModel):
def __init__(self, config: MarianConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFMarianMainLayer(config, name="model")
def get_encoder(self):
return self.model.encoder
def get_decoder(self):
return self.model.decoder
@unpack_inputs
@add_start_docstrings_to_model_forward(MARIAN_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSeq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: tf.Tensor | None = None,
attention_mask: tf.Tensor | None = None,
decoder_input_ids: tf.Tensor | None = None,
decoder_attention_mask: tf.Tensor | None = None,
decoder_position_ids: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
decoder_head_mask: tf.Tensor | None = None,
cross_attn_head_mask: tf.Tensor | None = None,
encoder_outputs: tf.Tensor | None = None,
past_key_values: Tuple[Tuple[tf.Tensor]] | None = None,
inputs_embeds: tf.Tensor | None = None,
decoder_inputs_embeds: tf.Tensor | None = None,
use_cache: bool | None = None,
output_attentions: bool | None = None,
output_hidden_states: bool | None = None,
return_dict: bool | None = None,
training: bool = False,
**kwargs,
) -> Tuple[tf.Tensor] | TFSeq2SeqModelOutput:
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
# Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output
def serving_output(self, output):
pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
return TFSeq2SeqModelOutput(
last_hidden_state=output.last_hidden_state,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
cross_attentions=cross_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
encoder_hidden_states=enc_hs,
encoder_attentions=enc_attns,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "model", None) is not None:
with tf.name_scope(self.model.name):
self.model.build(None)
# Copied from transformers.models.bart.modeling_tf_bart.BiasLayer
class BiasLayer(tf.keras.layers.Layer):
"""
Bias as a layer. It is used for serialization purposes: `tf.keras.Model.save_weights` stores on a per-layer basis,
so all weights have to be registered in a layer.
"""
def __init__(self, shape, initializer, trainable, name, **kwargs):
super().__init__(name=name, **kwargs)
# Note: the name of this variable will NOT be scoped when serialized, i.e. it will not be in the format of
# "outer_layer/inner_layer/.../name:0". Instead, it will be "name:0". For further details, see:
# https://github.com/huggingface/transformers/pull/18833#issuecomment-1233090214
self.bias = self.add_weight(name=name, shape=shape, initializer=initializer, trainable=trainable)
def call(self, x):
return x + self.bias
@add_start_docstrings(
"The MARIAN Model with a language modeling head. Can be used for summarization.",
MARIAN_START_DOCSTRING,
)
class TFMarianMTModel(TFMarianPreTrainedModel, TFCausalLanguageModelingLoss):
_keys_to_ignore_on_load_unexpected = [
r"model.encoder.embed_tokens.weight",
r"model.decoder.embed_tokens.weight",
]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFMarianMainLayer(config, name="model")
self.use_cache = config.use_cache
# final_bias_logits is registered as a buffer in pytorch, so not trainable for the sake of consistency.
self.bias_layer = BiasLayer(
name="final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False
)
def get_decoder(self):
return self.model.decoder
def get_encoder(self):
return self.model.encoder
def get_output_embeddings(self):
return self.get_input_embeddings()
def set_output_embeddings(self, value):
self.set_input_embeddings(value)
def get_bias(self):
return {"final_logits_bias": self.bias_layer.bias}
def set_bias(self, value):
# Replaces the existing layers containing bias for correct (de)serialization.
vocab_size = value["final_logits_bias"].shape[-1]
self.bias_layer = BiasLayer(
name="final_logits_bias", shape=[1, vocab_size], initializer="zeros", trainable=False
)
self.bias_layer.bias.assign(value["final_logits_bias"])
@unpack_inputs
@add_start_docstrings_to_model_forward(MARIAN_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(MARIAN_GENERATION_EXAMPLE)
def call(
self,
input_ids: tf.Tensor | None = None,
attention_mask: tf.Tensor | None = None,
decoder_input_ids: tf.Tensor | None = None,
decoder_attention_mask: tf.Tensor | None = None,
decoder_position_ids: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
decoder_head_mask: tf.Tensor | None = None,
cross_attn_head_mask: tf.Tensor | None = None,
encoder_outputs: TFBaseModelOutput | None = None,
past_key_values: Tuple[Tuple[tf.Tensor]] | None = None,
inputs_embeds: tf.Tensor | None = None,
decoder_inputs_embeds: tf.Tensor | None = None,
use_cache: bool | None = None,
output_attentions: bool | None = None,
output_hidden_states: bool | None = None,
return_dict: bool | None = None,
labels: tf.Tensor | None = None,
training: bool = False,
) -> Tuple[tf.Tensor] | TFSeq2SeqLMOutput:
r"""
labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
"""
if labels is not None:
labels = tf.where(
labels == self.config.pad_token_id,
tf.fill(shape_list(labels), tf.cast(-100, labels.dtype)),
labels,
)
use_cache = False
if decoder_input_ids is None and decoder_inputs_embeds is None:
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
lm_logits = tf.matmul(outputs[0], self.model.shared.weights, transpose_b=True)
lm_logits = self.bias_layer(lm_logits)
masked_lm_loss = None if labels is None else self.hf_compute_loss(labels, lm_logits)
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return TFSeq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values, # index 1 of d outputs
decoder_hidden_states=outputs.decoder_hidden_states, # index 2 of d outputs
decoder_attentions=outputs.decoder_attentions, # index 3 of d outputs
cross_attentions=outputs.cross_attentions, # index 4 of d outputs
encoder_last_hidden_state=outputs.encoder_last_hidden_state, # index 0 of encoder outputs
encoder_hidden_states=outputs.encoder_hidden_states, # 1 of e out
encoder_attentions=outputs.encoder_attentions, # 2 of e out
)
# Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
def serving_output(self, output):
pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
return TFSeq2SeqLMOutput(
logits=output.logits,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
cross_attentions=cross_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
encoder_hidden_states=enc_hs,
encoder_attentions=enc_attns,
)
# Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.prepare_inputs_for_generation
def prepare_inputs_for_generation(
self,
decoder_input_ids,
past_key_values=None,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
use_cache=None,
encoder_outputs=None,
**kwargs,
):
# cut decoder_input_ids if past_key_values is used
if past_key_values is not None:
decoder_input_ids = decoder_input_ids[:, -1:]
if decoder_attention_mask is not None: # xla
decoder_position_ids = tf.math.cumsum(decoder_attention_mask, axis=-1, exclusive=True)[:, -1:]
elif past_key_values is not None: # no xla + past_key_values
decoder_position_ids = past_key_values[0][0].shape[2]
else: # no xla + no past_key_values
decoder_position_ids = tf.range(decoder_input_ids.shape[1])
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past_key_values,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"decoder_position_ids": decoder_position_ids,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
}
def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor):
return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "model", None) is not None:
with tf.name_scope(self.model.name):
self.model.build(None)
if getattr(self, "bias_layer", None) is not None:
with tf.name_scope(self.bias_layer.name):
self.bias_layer.build(None)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import datetime
import json
import os
import re
from pathlib import Path
from typing import Tuple
import yaml
from tqdm import tqdm
from transformers.models.marian.convert_marian_to_pytorch import (
FRONT_MATTER_TEMPLATE,
convert,
convert_opus_name_to_hf_name,
download_and_unzip,
get_system_metadata,
)
DEFAULT_REPO = "Tatoeba-Challenge"
DEFAULT_MODEL_DIR = os.path.join(DEFAULT_REPO, "models")
LANG_CODE_URL = "https://datahub.io/core/language-codes/r/language-codes-3b2.csv"
ISO_URL = "https://cdn-datasets.huggingface.co/language_codes/iso-639-3.csv"
ISO_PATH = "lang_code_data/iso-639-3.csv"
LANG_CODE_PATH = "lang_code_data/language-codes-3b2.csv"
TATOEBA_MODELS_URL = "https://object.pouta.csc.fi/Tatoeba-MT-models"
class TatoebaConverter:
"""
Convert Tatoeba-Challenge models to huggingface format.
Steps:
1. Convert numpy state dict to hf format (same code as OPUS-MT-Train conversion).
2. Rename opus model to huggingface format. This means replace each alpha3 code with an alpha2 code if a unique
one exists. e.g. aav-eng -> aav-en, heb-eng -> he-en
3. Select the best model for a particular pair, parse the yml for it and write a model card. By default the
best model is the one listed first in released-model-results, but it's also possible to specify the most
recent one.
"""
def __init__(self, save_dir="marian_converted"):
assert Path(DEFAULT_REPO).exists(), "need git clone [email protected]:Helsinki-NLP/Tatoeba-Challenge.git"
self.download_lang_info()
self.model_results = json.load(open("Tatoeba-Challenge/models/released-model-results.json"))
self.alpha3_to_alpha2 = {}
for line in open(ISO_PATH):
parts = line.split("\t")
if len(parts[0]) == 3 and len(parts[3]) == 2:
self.alpha3_to_alpha2[parts[0]] = parts[3]
for line in LANG_CODE_PATH:
parts = line.split(",")
if len(parts[0]) == 3 and len(parts[1]) == 2:
self.alpha3_to_alpha2[parts[0]] = parts[1]
self.model_card_dir = Path(save_dir)
self.tag2name = {}
for key, value in GROUP_MEMBERS.items():
self.tag2name[key] = value[0]
def convert_models(self, tatoeba_ids, dry_run=False):
models_to_convert = [self.parse_metadata(x) for x in tatoeba_ids]
save_dir = Path("marian_ckpt")
dest_dir = Path(self.model_card_dir)
dest_dir.mkdir(exist_ok=True)
for model in tqdm(models_to_convert): # k, prepro, download, test_set_url in tqdm(model_list):
if "SentencePiece" not in model["pre-processing"]:
print(f"Skipping {model['release']} because it doesn't appear to use SentencePiece")
continue
if not os.path.exists(save_dir / model["_name"]):
download_and_unzip(f"{TATOEBA_MODELS_URL}/{model['release']}", save_dir / model["_name"])
# from convert_marian_to_pytorch
opus_language_groups_to_hf = convert_opus_name_to_hf_name
pair_name = opus_language_groups_to_hf(model["_name"])
convert(save_dir / model["_name"], dest_dir / f"opus-mt-{pair_name}")
self.write_model_card(model, dry_run=dry_run)
def expand_group_to_two_letter_codes(self, grp_name):
return [self.alpha3_to_alpha2.get(x, x) for x in GROUP_MEMBERS[grp_name][1]]
def is_group(self, code, name):
return "languages" in name or len(GROUP_MEMBERS.get(code, [])) > 1
def get_tags(self, code, name):
if len(code) == 2:
assert "languages" not in name, f"{code}: {name}"
return [code]
elif self.is_group(code, name):
group = self.expand_group_to_two_letter_codes(code)
group.append(code)
return group
else: # zho-> zh
print(f"Three letter monolingual code: {code}")
return [code]
def resolve_lang_code(self, src, tgt) -> Tuple[str, str]:
src_tags = self.get_tags(src, self.tag2name[src])
tgt_tags = self.get_tags(tgt, self.tag2name[tgt])
return src_tags, tgt_tags
@staticmethod
def model_type_info_from_model_name(name):
info = {"_has_backtranslated_data": False}
if "1m" in name:
info["_data_per_pair"] = str(1e6)
if "2m" in name:
info["_data_per_pair"] = str(2e6)
if "4m" in name:
info["_data_per_pair"] = str(4e6)
if "+bt" in name:
info["_has_backtranslated_data"] = True
if "tuned4" in name:
info["_tuned"] = re.search(r"tuned4[^-]+", name).group()
return info
def write_model_card(self, model_dict, dry_run=False) -> str:
"""
Construct card from data parsed from YAML and the model's name. upload command: aws s3 sync model_card_dir
s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun
"""
model_dir_url = f"{TATOEBA_MODELS_URL}/{model_dict['release']}"
long_pair = model_dict["_name"].split("-")
assert len(long_pair) == 2, f"got a translation pair {model_dict['_name']} that doesn't appear to be a pair"
short_src = self.alpha3_to_alpha2.get(long_pair[0], long_pair[0])
short_tgt = self.alpha3_to_alpha2.get(long_pair[1], long_pair[1])
model_dict["_hf_model_id"] = f"opus-mt-{short_src}-{short_tgt}"
a3_src, a3_tgt = model_dict["_name"].split("-")
# opus_src_tags, opus_tgt_tags = a3_src.split("+"), a3_tgt.split("+")
# This messy part tries to deal with language tags in multilingual models, possibly
# not all having three-letter codes
resolved_src_tags, resolved_tgt_tags = self.resolve_lang_code(a3_src, a3_tgt)
a2_src_tags, a2_tgt_tags = [], []
for tag in resolved_src_tags:
if tag not in self.alpha3_to_alpha2:
a2_src_tags.append(tag)
for tag in resolved_tgt_tags:
if tag not in self.alpha3_to_alpha2:
a2_tgt_tags.append(tag)
lang_tags = dedup(a2_src_tags + a2_tgt_tags)
src_multilingual, tgt_multilingual = (len(a2_src_tags) > 1), (len(a2_tgt_tags) > 1)
s, t = ",".join(a2_src_tags), ",".join(a2_tgt_tags)
metadata = {
"hf_name": model_dict["_name"],
"source_languages": s,
"target_languages": t,
"opus_readme_url": f"{model_dir_url}/README.md",
"original_repo": "Tatoeba-Challenge",
"tags": ["translation"],
"languages": lang_tags,
}
lang_tags = l2front_matter(lang_tags)
metadata["src_constituents"] = list(GROUP_MEMBERS[a3_src][1])
metadata["tgt_constituents"] = list(GROUP_MEMBERS[a3_tgt][1])
metadata["src_multilingual"] = src_multilingual
metadata["tgt_multilingual"] = tgt_multilingual
backtranslated_data = ""
if model_dict["_has_backtranslated_data"]:
backtranslated_data = " with backtranslations"
multilingual_data = ""
if "_data_per_pair" in model_dict:
multilingual_data = f"* data per pair in multilingual model: {model_dict['_data_per_pair']}\n"
tuned = ""
if "_tuned" in model_dict:
tuned = f"* multilingual model tuned for: {model_dict['_tuned']}\n"
model_base_filename = model_dict["release"].split("/")[-1]
download = f"* download original weights: [{model_base_filename}]({model_dir_url}/{model_dict['release']})\n"
langtoken = ""
if tgt_multilingual:
langtoken = (
"* a sentence-initial language token is required in the form of >>id<<"
"(id = valid, usually three-letter target language ID)\n"
)
metadata.update(get_system_metadata(DEFAULT_REPO))
scorestable = ""
for k, v in model_dict.items():
if "scores" in k:
this_score_table = f"* {k}\n|Test set|score|\n|---|---|\n"
pairs = sorted(v.items(), key=lambda x: x[1], reverse=True)
for pair in pairs:
this_score_table += f"|{pair[0]}|{pair[1]}|\n"
scorestable += this_score_table
datainfo = ""
if "training-data" in model_dict:
datainfo += "* Training data: \n"
for k, v in model_dict["training-data"].items():
datainfo += f" * {str(k)}: {str(v)}\n"
if "validation-data" in model_dict:
datainfo += "* Validation data: \n"
for k, v in model_dict["validation-data"].items():
datainfo += f" * {str(k)}: {str(v)}\n"
if "test-data" in model_dict:
datainfo += "* Test data: \n"
for k, v in model_dict["test-data"].items():
datainfo += f" * {str(k)}: {str(v)}\n"
testsetfilename = model_dict["release"].replace(".zip", ".test.txt")
testscoresfilename = model_dict["release"].replace(".zip", ".eval.txt")
testset = f"* test set translations file: [test.txt]({model_dir_url}/{testsetfilename})\n"
testscores = f"* test set scores file: [eval.txt]({model_dir_url}/{testscoresfilename})\n"
# combine with Tatoeba markdown
readme_url = f"{TATOEBA_MODELS_URL}/{model_dict['_name']}/README.md"
extra_markdown = f"""
### {model_dict['_name']}
* source language name: {self.tag2name[a3_src]}
* target language name: {self.tag2name[a3_tgt]}
* OPUS readme: [README.md]({readme_url})
"""
content = (
f"""
* model: {model_dict['modeltype']}
* source language code{src_multilingual*'s'}: {', '.join(a2_src_tags)}
* target language code{tgt_multilingual*'s'}: {', '.join(a2_tgt_tags)}
* dataset: opus {backtranslated_data}
* release date: {model_dict['release-date']}
* pre-processing: {model_dict['pre-processing']}
"""
+ multilingual_data
+ tuned
+ download
+ langtoken
+ datainfo
+ testset
+ testscores
+ scorestable
)
content = FRONT_MATTER_TEMPLATE.format(lang_tags) + extra_markdown + content
items = "\n".join([f"* {k}: {v}" for k, v in metadata.items()])
sec3 = "\n### System Info: \n" + items
content += sec3
if dry_run:
print("CONTENT:")
print(content)
print("METADATA:")
print(metadata)
return
sub_dir = self.model_card_dir / model_dict["_hf_model_id"]
sub_dir.mkdir(exist_ok=True)
dest = sub_dir / "README.md"
dest.open("w").write(content)
for k, v in metadata.items():
if isinstance(v, datetime.date):
metadata[k] = datetime.datetime.strftime(v, "%Y-%m-%d")
with open(sub_dir / "metadata.json", "w", encoding="utf-8") as writeobj:
json.dump(metadata, writeobj)
def download_lang_info(self):
Path(LANG_CODE_PATH).parent.mkdir(exist_ok=True)
import wget
if not os.path.exists(ISO_PATH):
wget.download(ISO_URL, ISO_PATH)
if not os.path.exists(LANG_CODE_PATH):
wget.download(LANG_CODE_URL, LANG_CODE_PATH)
def parse_metadata(self, model_name, repo_path=DEFAULT_MODEL_DIR, method="best"):
p = Path(repo_path) / model_name
def url_to_name(url):
return url.split("/")[-1].split(".")[0]
if model_name not in self.model_results:
# This is not a language pair, so model results are ambiguous, go by newest
method = "newest"
if method == "best":
# Sort by how early they appear in released-models-results
results = [url_to_name(model["download"]) for model in self.model_results[model_name]]
ymls = [f for f in os.listdir(p) if f.endswith(".yml") and f[:-4] in results]
ymls.sort(key=lambda x: results.index(x[:-4]))
metadata = yaml.safe_load(open(p / ymls[0]))
metadata.update(self.model_type_info_from_model_name(ymls[0][:-4]))
elif method == "newest":
ymls = [f for f in os.listdir(p) if f.endswith(".yml")]
# Sort by date
ymls.sort(
key=lambda x: datetime.datetime.strptime(re.search(r"\d\d\d\d-\d\d?-\d\d?", x).group(), "%Y-%m-%d")
)
metadata = yaml.safe_load(open(p / ymls[-1]))
metadata.update(self.model_type_info_from_model_name(ymls[-1][:-4]))
else:
raise NotImplementedError(f"Don't know argument method='{method}' to parse_metadata()")
metadata["_name"] = model_name
return metadata
GROUP_MEMBERS = {
# three letter code -> (group/language name, {constituents...}
# if this language is on the target side the constituents can be used as target language codes.
# if the language is on the source side they are supported natively without special codes.
"aav": ("Austro-Asiatic languages", {"hoc", "hoc_Latn", "kha", "khm", "khm_Latn", "mnw", "vie", "vie_Hani"}),
"afa": (
"Afro-Asiatic languages",
{
"acm",
"afb",
"amh",
"apc",
"ara",
"arq",
"ary",
"arz",
"hau_Latn",
"heb",
"kab",
"mlt",
"rif_Latn",
"shy_Latn",
"som",
"thv",
"tir",
},
),
"afr": ("Afrikaans", {"afr"}),
"alv": (
"Atlantic-Congo languages",
{
"ewe",
"fuc",
"fuv",
"ibo",
"kin",
"lin",
"lug",
"nya",
"run",
"sag",
"sna",
"swh",
"toi_Latn",
"tso",
"umb",
"wol",
"xho",
"yor",
"zul",
},
),
"ara": ("Arabic", {"afb", "apc", "apc_Latn", "ara", "ara_Latn", "arq", "arq_Latn", "arz"}),
"art": (
"Artificial languages",
{
"afh_Latn",
"avk_Latn",
"dws_Latn",
"epo",
"ido",
"ido_Latn",
"ile_Latn",
"ina_Latn",
"jbo",
"jbo_Cyrl",
"jbo_Latn",
"ldn_Latn",
"lfn_Cyrl",
"lfn_Latn",
"nov_Latn",
"qya",
"qya_Latn",
"sjn_Latn",
"tlh_Latn",
"tzl",
"tzl_Latn",
"vol_Latn",
},
),
"aze": ("Azerbaijani", {"aze_Latn"}),
"bat": ("Baltic languages", {"lit", "lav", "prg_Latn", "ltg", "sgs"}),
"bel": ("Belarusian", {"bel", "bel_Latn"}),
"ben": ("Bengali", {"ben"}),
"bnt": (
"Bantu languages",
{"kin", "lin", "lug", "nya", "run", "sna", "swh", "toi_Latn", "tso", "umb", "xho", "zul"},
),
"bul": ("Bulgarian", {"bul", "bul_Latn"}),
"cat": ("Catalan", {"cat"}),
"cau": ("Caucasian languages", {"abk", "kat", "che", "ady"}),
"ccs": ("South Caucasian languages", {"kat"}),
"ceb": ("Cebuano", {"ceb"}),
"cel": ("Celtic languages", {"gla", "gle", "bre", "cor", "glv", "cym"}),
"ces": ("Czech", {"ces"}),
"cpf": ("Creoles and pidgins, French‑based", {"gcf_Latn", "hat", "mfe"}),
"cpp": (
"Creoles and pidgins, Portuguese-based",
{"zsm_Latn", "ind", "pap", "min", "tmw_Latn", "max_Latn", "zlm_Latn"},
),
"cus": ("Cushitic languages", {"som"}),
"dan": ("Danish", {"dan"}),
"deu": ("German", {"deu"}),
"dra": ("Dravidian languages", {"tam", "kan", "mal", "tel"}),
"ell": ("Modern Greek (1453-)", {"ell"}),
"eng": ("English", {"eng"}),
"epo": ("Esperanto", {"epo"}),
"est": ("Estonian", {"est"}),
"euq": ("Basque (family)", {"eus"}),
"eus": ("Basque", {"eus"}),
"fin": ("Finnish", {"fin"}),
"fiu": (
"Finno-Ugrian languages",
{
"est",
"fin",
"fkv_Latn",
"hun",
"izh",
"kpv",
"krl",
"liv_Latn",
"mdf",
"mhr",
"myv",
"sma",
"sme",
"udm",
"vep",
"vro",
},
),
"fra": ("French", {"fra"}),
"gem": (
"Germanic languages",
{
"afr",
"ang_Latn",
"dan",
"deu",
"eng",
"enm_Latn",
"fao",
"frr",
"fry",
"gos",
"got_Goth",
"gsw",
"isl",
"ksh",
"ltz",
"nds",
"nld",
"nno",
"nob",
"nob_Hebr",
"non_Latn",
"pdc",
"sco",
"stq",
"swe",
"swg",
"yid",
},
),
"gle": ("Irish", {"gle"}),
"glg": ("Galician", {"glg"}),
"gmq": ("North Germanic languages", {"dan", "nob", "nob_Hebr", "swe", "isl", "nno", "non_Latn", "fao"}),
"gmw": (
"West Germanic languages",
{
"afr",
"ang_Latn",
"deu",
"eng",
"enm_Latn",
"frr",
"fry",
"gos",
"gsw",
"ksh",
"ltz",
"nds",
"nld",
"pdc",
"sco",
"stq",
"swg",
"yid",
},
),
"grk": ("Greek languages", {"grc_Grek", "ell"}),
"hbs": ("Serbo-Croatian", {"hrv", "srp_Cyrl", "bos_Latn", "srp_Latn"}),
"heb": ("Hebrew", {"heb"}),
"hin": ("Hindi", {"hin"}),
"hun": ("Hungarian", {"hun"}),
"hye": ("Armenian", {"hye", "hye_Latn"}),
"iir": (
"Indo-Iranian languages",
{
"asm",
"awa",
"ben",
"bho",
"gom",
"guj",
"hif_Latn",
"hin",
"jdt_Cyrl",
"kur_Arab",
"kur_Latn",
"mai",
"mar",
"npi",
"ori",
"oss",
"pan_Guru",
"pes",
"pes_Latn",
"pes_Thaa",
"pnb",
"pus",
"rom",
"san_Deva",
"sin",
"snd_Arab",
"tgk_Cyrl",
"tly_Latn",
"urd",
"zza",
},
),
"ilo": ("Iloko", {"ilo"}),
"inc": (
"Indic languages",
{
"asm",
"awa",
"ben",
"bho",
"gom",
"guj",
"hif_Latn",
"hin",
"mai",
"mar",
"npi",
"ori",
"pan_Guru",
"pnb",
"rom",
"san_Deva",
"sin",
"snd_Arab",
"urd",
},
),
"ine": (
"Indo-European languages",
{
"afr",
"afr_Arab",
"aln",
"ang_Latn",
"arg",
"asm",
"ast",
"awa",
"bel",
"bel_Latn",
"ben",
"bho",
"bjn",
"bos_Latn",
"bre",
"bul",
"bul_Latn",
"cat",
"ces",
"cor",
"cos",
"csb_Latn",
"cym",
"dan",
"deu",
"dsb",
"egl",
"ell",
"eng",
"enm_Latn",
"ext",
"fao",
"fra",
"frm_Latn",
"frr",
"fry",
"gcf_Latn",
"gla",
"gle",
"glg",
"glv",
"gom",
"gos",
"got_Goth",
"grc_Grek",
"gsw",
"guj",
"hat",
"hif_Latn",
"hin",
"hrv",
"hsb",
"hye",
"hye_Latn",
"ind",
"isl",
"ita",
"jdt_Cyrl",
"ksh",
"kur_Arab",
"kur_Latn",
"lad",
"lad_Latn",
"lat_Grek",
"lat_Latn",
"lav",
"lij",
"lit",
"lld_Latn",
"lmo",
"ltg",
"ltz",
"mai",
"mar",
"max_Latn",
"mfe",
"min",
"mkd",
"mwl",
"nds",
"nld",
"nno",
"nob",
"nob_Hebr",
"non_Latn",
"npi",
"oci",
"ori",
"orv_Cyrl",
"oss",
"pan_Guru",
"pap",
"pcd",
"pdc",
"pes",
"pes_Latn",
"pes_Thaa",
"pms",
"pnb",
"pol",
"por",
"prg_Latn",
"pus",
"roh",
"rom",
"ron",
"rue",
"rus",
"rus_Latn",
"san_Deva",
"scn",
"sco",
"sgs",
"sin",
"slv",
"snd_Arab",
"spa",
"sqi",
"srd",
"srp_Cyrl",
"srp_Latn",
"stq",
"swe",
"swg",
"tgk_Cyrl",
"tly_Latn",
"tmw_Latn",
"ukr",
"urd",
"vec",
"wln",
"yid",
"zlm_Latn",
"zsm_Latn",
"zza",
},
),
"isl": ("Icelandic", {"isl"}),
"ita": ("Italian", {"ita"}),
"itc": (
"Italic languages",
{
"arg",
"ast",
"bjn",
"cat",
"cos",
"egl",
"ext",
"fra",
"frm_Latn",
"gcf_Latn",
"glg",
"hat",
"ind",
"ita",
"lad",
"lad_Latn",
"lat_Grek",
"lat_Latn",
"lij",
"lld_Latn",
"lmo",
"max_Latn",
"mfe",
"min",
"mwl",
"oci",
"pap",
"pcd",
"pms",
"por",
"roh",
"ron",
"scn",
"spa",
"srd",
"tmw_Latn",
"vec",
"wln",
"zlm_Latn",
"zsm_Latn",
},
),
"jpn": ("Japanese", {"jpn", "jpn_Bopo", "jpn_Hang", "jpn_Hani", "jpn_Hira", "jpn_Kana", "jpn_Latn", "jpn_Yiii"}),
"jpx": ("Japanese (family)", {"jpn"}),
"kat": ("Georgian", {"kat"}),
"kor": ("Korean", {"kor_Hani", "kor_Hang", "kor_Latn", "kor"}),
"lav": ("Latvian", {"lav"}),
"lit": ("Lithuanian", {"lit"}),
"mkd": ("Macedonian", {"mkd"}),
"mkh": ("Mon-Khmer languages", {"vie_Hani", "mnw", "vie", "kha", "khm_Latn", "khm"}),
"msa": ("Malay (macrolanguage)", {"zsm_Latn", "ind", "max_Latn", "zlm_Latn", "min"}),
"mul": (
"Multiple languages",
{
"abk",
"acm",
"ady",
"afb",
"afh_Latn",
"afr",
"akl_Latn",
"aln",
"amh",
"ang_Latn",
"apc",
"ara",
"arg",
"arq",
"ary",
"arz",
"asm",
"ast",
"avk_Latn",
"awa",
"aze_Latn",
"bak",
"bam_Latn",
"bel",
"bel_Latn",
"ben",
"bho",
"bod",
"bos_Latn",
"bre",
"brx",
"brx_Latn",
"bul",
"bul_Latn",
"cat",
"ceb",
"ces",
"cha",
"che",
"chr",
"chv",
"cjy_Hans",
"cjy_Hant",
"cmn",
"cmn_Hans",
"cmn_Hant",
"cor",
"cos",
"crh",
"crh_Latn",
"csb_Latn",
"cym",
"dan",
"deu",
"dsb",
"dtp",
"dws_Latn",
"egl",
"ell",
"enm_Latn",
"epo",
"est",
"eus",
"ewe",
"ext",
"fao",
"fij",
"fin",
"fkv_Latn",
"fra",
"frm_Latn",
"frr",
"fry",
"fuc",
"fuv",
"gan",
"gcf_Latn",
"gil",
"gla",
"gle",
"glg",
"glv",
"gom",
"gos",
"got_Goth",
"grc_Grek",
"grn",
"gsw",
"guj",
"hat",
"hau_Latn",
"haw",
"heb",
"hif_Latn",
"hil",
"hin",
"hnj_Latn",
"hoc",
"hoc_Latn",
"hrv",
"hsb",
"hun",
"hye",
"iba",
"ibo",
"ido",
"ido_Latn",
"ike_Latn",
"ile_Latn",
"ilo",
"ina_Latn",
"ind",
"isl",
"ita",
"izh",
"jav",
"jav_Java",
"jbo",
"jbo_Cyrl",
"jbo_Latn",
"jdt_Cyrl",
"jpn",
"kab",
"kal",
"kan",
"kat",
"kaz_Cyrl",
"kaz_Latn",
"kek_Latn",
"kha",
"khm",
"khm_Latn",
"kin",
"kir_Cyrl",
"kjh",
"kpv",
"krl",
"ksh",
"kum",
"kur_Arab",
"kur_Latn",
"lad",
"lad_Latn",
"lao",
"lat_Latn",
"lav",
"ldn_Latn",
"lfn_Cyrl",
"lfn_Latn",
"lij",
"lin",
"lit",
"liv_Latn",
"lkt",
"lld_Latn",
"lmo",
"ltg",
"ltz",
"lug",
"lzh",
"lzh_Hans",
"mad",
"mah",
"mai",
"mal",
"mar",
"max_Latn",
"mdf",
"mfe",
"mhr",
"mic",
"min",
"mkd",
"mlg",
"mlt",
"mnw",
"moh",
"mon",
"mri",
"mwl",
"mww",
"mya",
"myv",
"nan",
"nau",
"nav",
"nds",
"niu",
"nld",
"nno",
"nob",
"nob_Hebr",
"nog",
"non_Latn",
"nov_Latn",
"npi",
"nya",
"oci",
"ori",
"orv_Cyrl",
"oss",
"ota_Arab",
"ota_Latn",
"pag",
"pan_Guru",
"pap",
"pau",
"pdc",
"pes",
"pes_Latn",
"pes_Thaa",
"pms",
"pnb",
"pol",
"por",
"ppl_Latn",
"prg_Latn",
"pus",
"quc",
"qya",
"qya_Latn",
"rap",
"rif_Latn",
"roh",
"rom",
"ron",
"rue",
"run",
"rus",
"sag",
"sah",
"san_Deva",
"scn",
"sco",
"sgs",
"shs_Latn",
"shy_Latn",
"sin",
"sjn_Latn",
"slv",
"sma",
"sme",
"smo",
"sna",
"snd_Arab",
"som",
"spa",
"sqi",
"srp_Cyrl",
"srp_Latn",
"stq",
"sun",
"swe",
"swg",
"swh",
"tah",
"tam",
"tat",
"tat_Arab",
"tat_Latn",
"tel",
"tet",
"tgk_Cyrl",
"tha",
"tir",
"tlh_Latn",
"tly_Latn",
"tmw_Latn",
"toi_Latn",
"ton",
"tpw_Latn",
"tso",
"tuk",
"tuk_Latn",
"tur",
"tvl",
"tyv",
"tzl",
"tzl_Latn",
"udm",
"uig_Arab",
"uig_Cyrl",
"ukr",
"umb",
"urd",
"uzb_Cyrl",
"uzb_Latn",
"vec",
"vie",
"vie_Hani",
"vol_Latn",
"vro",
"war",
"wln",
"wol",
"wuu",
"xal",
"xho",
"yid",
"yor",
"yue",
"yue_Hans",
"yue_Hant",
"zho",
"zho_Hans",
"zho_Hant",
"zlm_Latn",
"zsm_Latn",
"zul",
"zza",
},
),
"nic": (
"Niger-Kordofanian languages",
{
"bam_Latn",
"ewe",
"fuc",
"fuv",
"ibo",
"kin",
"lin",
"lug",
"nya",
"run",
"sag",
"sna",
"swh",
"toi_Latn",
"tso",
"umb",
"wol",
"xho",
"yor",
"zul",
},
),
"nld": ("Dutch", {"nld"}),
"nor": ("Norwegian", {"nob", "nno"}),
"phi": ("Philippine languages", {"ilo", "akl_Latn", "war", "hil", "pag", "ceb"}),
"pol": ("Polish", {"pol"}),
"por": ("Portuguese", {"por"}),
"pqe": (
"Eastern Malayo-Polynesian languages",
{"fij", "gil", "haw", "mah", "mri", "nau", "niu", "rap", "smo", "tah", "ton", "tvl"},
),
"roa": (
"Romance languages",
{
"arg",
"ast",
"cat",
"cos",
"egl",
"ext",
"fra",
"frm_Latn",
"gcf_Latn",
"glg",
"hat",
"ind",
"ita",
"lad",
"lad_Latn",
"lij",
"lld_Latn",
"lmo",
"max_Latn",
"mfe",
"min",
"mwl",
"oci",
"pap",
"pms",
"por",
"roh",
"ron",
"scn",
"spa",
"tmw_Latn",
"vec",
"wln",
"zlm_Latn",
"zsm_Latn",
},
),
"ron": ("Romanian", {"ron"}),
"run": ("Rundi", {"run"}),
"rus": ("Russian", {"rus"}),
"sal": ("Salishan languages", {"shs_Latn"}),
"sem": ("Semitic languages", {"acm", "afb", "amh", "apc", "ara", "arq", "ary", "arz", "heb", "mlt", "tir"}),
"sla": (
"Slavic languages",
{
"bel",
"bel_Latn",
"bos_Latn",
"bul",
"bul_Latn",
"ces",
"csb_Latn",
"dsb",
"hrv",
"hsb",
"mkd",
"orv_Cyrl",
"pol",
"rue",
"rus",
"slv",
"srp_Cyrl",
"srp_Latn",
"ukr",
},
),
"slv": ("Slovenian", {"slv"}),
"spa": ("Spanish", {"spa"}),
"swe": ("Swedish", {"swe"}),
"taw": ("Tai", {"lao", "tha"}),
"tgl": ("Tagalog", {"tgl_Latn"}),
"tha": ("Thai", {"tha"}),
"trk": (
"Turkic languages",
{
"aze_Latn",
"bak",
"chv",
"crh",
"crh_Latn",
"kaz_Cyrl",
"kaz_Latn",
"kir_Cyrl",
"kjh",
"kum",
"ota_Arab",
"ota_Latn",
"sah",
"tat",
"tat_Arab",
"tat_Latn",
"tuk",
"tuk_Latn",
"tur",
"tyv",
"uig_Arab",
"uig_Cyrl",
"uzb_Cyrl",
"uzb_Latn",
},
),
"tur": ("Turkish", {"tur"}),
"ukr": ("Ukrainian", {"ukr"}),
"urd": ("Urdu", {"urd"}),
"urj": (
"Uralic languages",
{
"est",
"fin",
"fkv_Latn",
"hun",
"izh",
"kpv",
"krl",
"liv_Latn",
"mdf",
"mhr",
"myv",
"sma",
"sme",
"udm",
"vep",
"vro",
},
),
"vie": ("Vietnamese", {"vie", "vie_Hani"}),
"war": ("Waray (Philippines)", {"war"}),
"zho": (
"Chinese",
{
"cjy_Hans",
"cjy_Hant",
"cmn",
"cmn_Bopo",
"cmn_Hang",
"cmn_Hani",
"cmn_Hans",
"cmn_Hant",
"cmn_Hira",
"cmn_Kana",
"cmn_Latn",
"cmn_Yiii",
"gan",
"hak_Hani",
"lzh",
"lzh_Bopo",
"lzh_Hang",
"lzh_Hani",
"lzh_Hans",
"lzh_Hira",
"lzh_Kana",
"lzh_Yiii",
"nan",
"nan_Hani",
"wuu",
"wuu_Bopo",
"wuu_Hani",
"wuu_Latn",
"yue",
"yue_Bopo",
"yue_Hang",
"yue_Hani",
"yue_Hans",
"yue_Hant",
"yue_Hira",
"yue_Kana",
"zho",
"zho_Hans",
"zho_Hant",
},
),
"zle": ("East Slavic languages", {"bel", "orv_Cyrl", "bel_Latn", "rus", "ukr", "rue"}),
"zls": ("South Slavic languages", {"bos_Latn", "bul", "bul_Latn", "hrv", "mkd", "slv", "srp_Cyrl", "srp_Latn"}),
"zlw": ("West Slavic languages", {"csb_Latn", "dsb", "hsb", "pol", "ces"}),
}
def l2front_matter(langs):
return "".join(f"- {l}\n" for l in langs)
def dedup(lst):
"""Preservers order"""
new_lst = []
for item in lst:
if not item or item in new_lst:
continue
else:
new_lst.append(item)
return new_lst
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"-m", "--models", action="append", help="<Required> Set flag", required=True, nargs="+", dest="models"
)
parser.add_argument("-save_dir", "--save_dir", default="marian_converted", help="where to save converted models")
args = parser.parse_args()
resolver = TatoebaConverter(save_dir=args.save_dir)
resolver.convert_models(args.models[0])
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/vipllava/convert_vipllava_weights_to_hf.py | # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import (
AddedToken,
AutoConfig,
AutoTokenizer,
CLIPImageProcessor,
LlavaProcessor,
VipLlavaConfig,
VipLlavaForConditionalGeneration,
)
KEYS_TO_MODIFY_MAPPING = {
"model.vision_tower.": "",
"model.mm_projector": "multi_modal_projector",
"model": "model.model",
"vision_model.model": "vision_model",
"lm_head": "language_model.lm_head",
"model.model": "language_model.model",
"multi_modal_projector.0": "multi_modal_projector.linear_1",
"multi_modal_projector.2": "multi_modal_projector.linear_2",
"final_linear.0": "linear_1",
"final_linear.2": "linear_2",
"multi_modal_projector.clip_layernorm": "multi_modal_projector.projector_layernorm",
}
# Copied from transformers.models.llava.convert_llava_weights_to_hf.convert_state_dict_to_hf
def convert_state_dict_to_hf(state_dict):
new_state_dict = {}
for key, value in state_dict.items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
key = key.replace(key_to_modify, new_key)
new_state_dict[key] = value
return new_state_dict
def convert_vipllava_llama_to_hf(text_model_id, vision_model_id, output_hub_path, old_state_dict_id):
torch.set_default_dtype(torch.float16)
text_config = AutoConfig.from_pretrained(text_model_id)
tokenizer = AutoTokenizer.from_pretrained(text_model_id)
tokenizer.add_tokens(AddedToken("<image>", special=True, normalized=False))
tokenizer.add_special_tokens({"pad_token": "<pad>"})
image_processor = CLIPImageProcessor.from_pretrained(vision_model_id)
processor = LlavaProcessor(tokenizer=tokenizer, image_processor=image_processor)
config = VipLlavaConfig(text_config=text_config)
config.pad_token_id = 32001
with torch.device("meta"):
model = VipLlavaForConditionalGeneration(config)
# Pad to 64 for performance reasons
pad_shape = 64
state_dict_path = hf_hub_download(old_state_dict_id, "model_state_dict_7b.bin")
state_dict = torch.load(state_dict_path, map_location="cpu")
state_dict = convert_state_dict_to_hf(state_dict)
model.load_state_dict(state_dict, strict=True, assign=True)
pre_expansion_embeddings = model.language_model.model.embed_tokens.weight.data
mu = torch.mean(pre_expansion_embeddings, dim=0).float()
n = pre_expansion_embeddings.size()[0]
sigma = ((pre_expansion_embeddings - mu).T @ (pre_expansion_embeddings - mu)) / n
dist = torch.distributions.multivariate_normal.MultivariateNormal(mu, covariance_matrix=1e-5 * sigma)
# We add an image token so we resize the model
model.resize_token_embeddings(config.text_config.vocab_size + 2, pad_shape)
model.language_model.model.embed_tokens.weight.data[32000:] = torch.stack(
tuple((dist.sample() for _ in range(model.language_model.model.embed_tokens.weight.data[32000:].shape[0]))),
dim=0,
)
model.language_model.lm_head.weight.data[32000:] = torch.stack(
tuple((dist.sample() for _ in range(model.language_model.lm_head.weight.data[32000:].shape[0]))),
dim=0,
)
model.config.vocab_size = model.config.vocab_size + pad_shape
model.config.text_config.vocab_size = model.config.text_config.vocab_size + pad_shape
model.push_to_hub(output_hub_path)
processor.push_to_hub(output_hub_path)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--text_model_id",
help="Hub location of the text model",
)
parser.add_argument(
"--vision_model_id",
help="Hub location of the vision model",
)
parser.add_argument(
"--output_hub_path",
help="Location on the hub of the converted model",
)
parser.add_argument(
"--old_state_dict_id",
help="Location on the hub of the raw state dict of the original model. The filename needs to be `model_state_dict.bin`",
)
args = parser.parse_args()
convert_vipllava_llama_to_hf(
args.text_model_id, args.vision_model_id, args.output_hub_path, args.old_state_dict_id
)
if __name__ == "__main__":
main()
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/vipllava/modeling_vipllava.py | # coding=utf-8
# Copyright 2023 the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch VipLlava model."""
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ... import PreTrainedModel
from ...activations import ACT2FN
from ...cache_utils import Cache
from ...modeling_outputs import ModelOutput
from ...utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ..auto import AutoModel, AutoModelForCausalLM
from .configuration_vipllava import VipLlavaConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "VipLlavaConfig"
VIPLLAVA_PRETRAINED_MODEL_ARCHIVE_LIST = [
"llava-hf/vip-llava-7b-hf",
# See all VipLlava models at https://huggingface.co/models?filter=vipllava
]
@dataclass
# Copied from transformers.models.idefics.modeling_idefics.IdeficsCausalLMOutputWithPast with Idefics->VipLlava
class VipLlavaCausalLMOutputWithPast(ModelOutput):
"""
Base class for VipLlava causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
sequence_length, hidden_size)`.
image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[List[torch.FloatTensor]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
class VipLlavaMultiModalProjector(nn.Module):
def __init__(self, config: VipLlavaConfig):
super().__init__()
self.projector_layernorm = nn.LayerNorm(
len(config.vision_feature_layers) * config.vision_config.hidden_size, eps=config.projector_layernorm_eps
)
self.linear_1 = nn.Linear(
len(config.vision_feature_layers) * config.vision_config.hidden_size,
config.text_config.hidden_size,
bias=True,
)
self.act = ACT2FN[config.projector_hidden_act]
self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=True)
def forward(self, hidden_states):
hidden_states = self.projector_layernorm(hidden_states)
hidden_states = self.linear_1(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.linear_2(hidden_states)
return hidden_states
VIPLLAVA_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`VipLlavaConfig`] or [`VipLlavaVisionConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare VipLlava Model outputting raw hidden-states without any specific head on top.",
VIPLLAVA_START_DOCSTRING,
)
# Copied from transformers.models.llava.modeling_llava.LlavaPreTrainedModel with Llava->VipLlava,llava->vipllava
class VipLlavaPreTrainedModel(PreTrainedModel):
config_class = VipLlavaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["VipLlavaVisionAttention"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
def _init_weights(self, module):
# important: this ported version of VipLlava isn't meant for training from scratch - only
# inference and fine-tuning - so the proper init weights code has been removed - the original codebase
# https://github.com/haotian-liu/LLaVA/tree/main/vipllava should serve for that purpose
std = (
self.config.initializer_range
if hasattr(self.config, "initializer_range")
else self.config.text_config.initializer_range
)
if hasattr(module, "class_embedding"):
module.class_embedding.data.normal_(mean=0.0, std=std)
if isinstance(module, (nn.Linear, nn.Conv2d)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
@property
def _supports_sdpa(self):
"""
Retrieve language_model's attribute to check whether the model supports
SDPA or not.
"""
return self.language_model._supports_sdpa
VIPLLAVA_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
The tensors corresponding to the input images. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details ([]`LlavaProcessor`] uses
[`CLIPImageProcessor`] for processing images).
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"""The VIPLLAVA model which consists of a vision backbone and a language model.""",
VIPLLAVA_START_DOCSTRING,
)
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration with LLAVA->VIPLLAVA,Llava->VipLlava
class VipLlavaForConditionalGeneration(VipLlavaPreTrainedModel):
def __init__(self, config: VipLlavaConfig):
super().__init__(config)
self.vision_tower = AutoModel.from_config(config.vision_config)
self.multi_modal_projector = VipLlavaMultiModalProjector(config)
self.vocab_size = config.vocab_size
self.language_model = AutoModelForCausalLM.from_config(
config.text_config, attn_implementation=config._attn_implementation
)
self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
self.post_init()
def get_input_embeddings(self):
return self.language_model.get_input_embeddings()
def set_input_embeddings(self, value):
self.language_model.set_input_embeddings(value)
def get_output_embeddings(self):
return self.language_model.get_output_embeddings()
def set_output_embeddings(self, new_embeddings):
self.language_model.set_output_embeddings(new_embeddings)
def set_decoder(self, decoder):
self.language_model.set_decoder(decoder)
def get_decoder(self):
return self.language_model.get_decoder()
def tie_weights(self):
return self.language_model.tie_weights()
def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
# update vocab size
self.config.text_config.vocab_size = model_embeds.num_embeddings
self.config.vocab_size = model_embeds.num_embeddings
self.vocab_size = model_embeds.num_embeddings
return model_embeds
def _merge_input_ids_with_image_features(self, image_features, inputs_embeds, input_ids, attention_mask, labels):
num_images, num_image_patches, embed_dim = image_features.shape
batch_size, sequence_length = input_ids.shape
left_padding = not torch.sum(input_ids[:, -1] == torch.tensor(self.pad_token_id))
# 1. Create a mask to know where special image tokens are
special_image_token_mask = input_ids == self.config.image_token_index
num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1)
# Compute the maximum embed dimension
max_embed_dim = (num_special_image_tokens.max() * (num_image_patches - 1)) + sequence_length
batch_indices, non_image_indices = torch.where(input_ids != self.config.image_token_index)
# 2. Compute the positions where text should be written
# Calculate new positions for text tokens in merged image-text sequence.
# `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images - 1` text tokens.
# `torch.cumsum` computes how each image token shifts subsequent text token positions.
# - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one.
new_token_positions = torch.cumsum((special_image_token_mask * (num_image_patches - 1) + 1), -1) - 1
nb_image_pad = max_embed_dim - 1 - new_token_positions[:, -1]
if left_padding:
new_token_positions += nb_image_pad[:, None] # offset for left padding
text_to_overwrite = new_token_positions[batch_indices, non_image_indices]
# 3. Create the full embedding, already padded to the maximum position
final_embedding = torch.zeros(
batch_size, max_embed_dim, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device
)
final_attention_mask = torch.zeros(
batch_size, max_embed_dim, dtype=attention_mask.dtype, device=inputs_embeds.device
)
if labels is not None:
final_labels = torch.full(
(batch_size, max_embed_dim), self.config.ignore_index, dtype=input_ids.dtype, device=input_ids.device
)
# In case the Vision model or the Language model has been offloaded to CPU, we need to manually
# set the corresponding tensors into their correct target device.
target_device = inputs_embeds.device
batch_indices, non_image_indices, text_to_overwrite = (
batch_indices.to(target_device),
non_image_indices.to(target_device),
text_to_overwrite.to(target_device),
)
attention_mask = attention_mask.to(target_device)
# 4. Fill the embeddings based on the mask. If we have ["hey" "<image>", "how", "are"]
# we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features
final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices]
final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices]
if labels is not None:
final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices]
# 5. Fill the embeddings corresponding to the images. Anything that is still zeros needs filling
image_to_overwrite = torch.all(final_embedding == 0, dim=-1)
image_to_overwrite &= image_to_overwrite.cumsum(-1) - 1 >= nb_image_pad[:, None].to(target_device)
if image_to_overwrite.sum() != image_features.shape[:-1].numel():
raise ValueError(
f"The input provided to the model are wrong. The number of image tokens is {torch.sum(special_image_token_mask)} while"
f" the number of image given to the model is {num_images}. This prevents correct indexing and breaks batch generation."
)
final_embedding[image_to_overwrite] = image_features.contiguous().reshape(-1, embed_dim).to(target_device)
final_attention_mask |= image_to_overwrite
position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
if labels is None:
final_labels = None
return final_embedding, final_attention_mask, final_labels, position_ids
@add_start_docstrings_to_model_forward(VIPLLAVA_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=VipLlavaCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
# Ignore copy
def forward(
self,
input_ids: torch.LongTensor = None,
pixel_values: torch.FloatTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
vision_feature_layers: Optional[List[int]] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, VipLlavaCausalLMOutputWithPast]:
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>>> import torch
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, VipLlavaForConditionalGeneration
>>> model = VipLlavaForConditionalGeneration.from_pretrained("llava-hf/vip-llava-7b-hf", device_map="auto", torch_dtype=torch.float16)
>>> processor = AutoProcessor.from_pretrained("llava-hf/vip-llava-7b-hf")
>>> prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.###Human: <image>\n{}###Assistant:"
>>> question = "Can you please describe this image?"
>>> prompt = prompt.format(question)
>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/compel-neg.png"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(text=text, images=image, return_tensors="pt").to(0, torch.float16)
>>> # Generate
>>> generate_ids = model.generate(**inputs, max_new_tokens=20)
>>> processor.decode(generate_ids[0][len(inputs["input_ids"][0]):], skip_special_tokens=True)
The image features a brown and white cat sitting on a green surface, with a red ball in its
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
vision_feature_layers = (
vision_feature_layers if vision_feature_layers is not None else self.config.vision_feature_layers
)
if inputs_embeds is None:
# 1. Extra the input embeddings
inputs_embeds = self.get_input_embeddings()(input_ids)
# 2. Merge text and images
if pixel_values is not None and input_ids.shape[1] != 1:
image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
# For VIP-llava, the image features are computed this way
# We select the features from index 1: for the layers -2, -5, -8, -11 and 6
image_features = [image_outputs.hidden_states[index][:, 1:] for index in vision_feature_layers]
image_features = torch.cat(image_features, dim=-1)
image_features = self.multi_modal_projector(image_features)
inputs_embeds, attention_mask, labels, position_ids = self._merge_input_ids_with_image_features(
image_features, inputs_embeds, input_ids, attention_mask, labels
)
if labels is None:
labels = torch.full_like(attention_mask, self.config.ignore_index).to(torch.long)
else:
# In case input_ids.shape[1] == 1 & pixel_values==None & past_key_values != None, we are in the case of
# generation with cache
if past_key_values is not None and pixel_values is not None and input_ids.shape[1] == 1:
# Retrieve the first layer to inspect the logits and mask out the hidden states
# that are set to 0
first_layer_past_key_value = past_key_values[0][0][:, 0, :, :]
# Sum all dimensions of head_dim (-1) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-1) == 0)
# Get the target length
target_seqlen = first_layer_past_key_value.shape[-2] + 1
extended_attention_mask = torch.ones(
(attention_mask.shape[0], target_seqlen - attention_mask.shape[1]),
dtype=attention_mask.dtype,
device=attention_mask.device,
)
# Filter out only the tokens that can be un-attended, this can happen
# in the case one uses Llava + Fused modules where the cache on the
# first iteration is already big enough, or if one passes custom cache
valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
new_batch_index = batch_index[valid_indices]
new_non_attended_tokens = non_attended_tokens[valid_indices]
# Zero-out the places where we don't need to attend
extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
attention_mask = torch.cat((attention_mask, extended_attention_mask), dim=1)
position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
outputs = self.language_model(
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = outputs[0]
loss = None
if labels is not None:
# Shift so that tokens < n predict n
if attention_mask is not None:
shift_attention_mask = attention_mask[..., 1:]
shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous()
shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous()
else:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(
shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device)
)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return VipLlavaCausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, inputs_embeds=None, pixel_values=None, attention_mask=None, **kwargs
):
if past_key_values is not None:
if isinstance(past_key_values, Cache):
cache_length = past_key_values.get_seq_length()
past_length = past_key_values.seen_tokens
else:
cache_length = past_length = past_key_values[0][0].shape[2]
# Keep only the unprocessed tokens:
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
# input)
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
# input_ids based on the past_length.
elif past_length < input_ids.shape[1]:
input_ids = input_ids[:, past_length:]
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
elif self.config.image_token_index in input_ids:
input_ids = input_ids[:, input_ids.shape[1] - 1 :]
# If the cache has seen more tokens than it can hold, then the cache has a size limit. Let's discard the
# older attention values, as their corresponding values are not part of the input.
if cache_length < past_length and attention_mask is not None:
attention_mask = attention_mask[:, -(cache_length + input_ids.shape[1]) :]
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_ids[:, -input_ids.shape[1] :]
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
"pixel_values": pixel_values,
}
)
return model_inputs
def _reorder_cache(self, *args, **kwargs):
return self.language_model._reorder_cache(*args, **kwargs)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/vipllava/__init__.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {"configuration_vipllava": ["VIPLLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP", "VipLlavaConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_vipllava"] = [
"VIPLLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
"VipLlavaForConditionalGeneration",
"VipLlavaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_vipllava import VIPLLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP, VipLlavaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vipllava import (
VIPLLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
VipLlavaForConditionalGeneration,
VipLlavaPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/vipllava/configuration_vipllava.py | # coding=utf-8
# Copyright 2023 Microsoft Research & University of Wisconsin-Madison and the HuggingFace Inc. team. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" VipLlava model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
logger = logging.get_logger(__name__)
VIPLLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"ybelkada/vip-llava-7b-hf": "https://huggingface.co/llava-hf/vip-llava-7b-hf/resolve/main/config.json",
}
class VipLlavaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VipLlavaForConditionalGeneration`]. It is used to instantiate an
VipLlava model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the VipLlava-9B.
e.g. [ybelkada/vip-llava-7b-hf](https://huggingface.co/ybelkada/vip-llava-7b-hf)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vision_config (`VipLlavaVisionConfig`, *optional*):
Custom vision config or dict
text_config (`Union[AutoConfig, dict]`, *optional*):
The config object of the text backbone. Can be any of `LlamaConfig` or `MistralConfig`.
ignore_index (`int`, *optional*, defaults to -100):
The ignore index for the loss function.
image_token_index (`int`, *optional*, defaults to 32000):
The image token index to encode the image prompt.
projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
The activation function used by the multimodal projector.
projector_layernorm_eps (`float`, *optional*, defaults to 1e-05):
The layer norm epsilon of the projector layernorm
vision_feature_layers (`List[int]`, *optional*, defaults to `[-2, -5, -8, -11, 6]`):
The list of layers to select the vision features from.
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the VipLlava model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`~VipLlavaForConditionalGeneration`]
Example:
```python
>>> from transformers import VipLlavaForConditionalGeneration, VipLlavaConfig, CLIPVisionConfig, LlamaConfig
>>> # Initializing a CLIP-vision config
>>> vision_config = CLIPVisionConfig()
>>> # Initializing a Llama config
>>> text_config = LlamaConfig()
>>> # Initializing a VipLlava vipllava-7b style configuration
>>> configuration = VipLlavaConfig(vision_config, text_config)
>>> # Initializing a model from the vipllava-7b style configuration
>>> model = VipLlavaForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "vipllava"
is_composition = False
def __init__(
self,
vision_config=None,
text_config=None,
ignore_index=-100,
image_token_index=32000,
projector_hidden_act="gelu",
projector_layernorm_eps=1e-5,
vision_feature_layers=[-2, -5, -8, -11, 6],
vocab_size=32000,
**kwargs,
):
self.ignore_index = ignore_index
self.image_token_index = image_token_index
self.projector_hidden_act = projector_hidden_act
self.projector_layernorm_eps = projector_layernorm_eps
self.vision_feature_layers = vision_feature_layers
self.vocab_size = vocab_size
self.vision_config = vision_config
if isinstance(self.vision_config, dict):
vision_config["model_type"] = (
vision_config["model_type"] if "model_type" in vision_config else "clip_vision_model"
)
self.vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
elif vision_config is None:
self.vision_config = CONFIG_MAPPING["clip_vision_model"](
intermediate_size=4096,
hidden_size=1024,
patch_size=14,
image_size=336,
num_hidden_layers=24,
num_attention_heads=16,
vocab_size=32000,
projection_dim=768,
)
self.vocab_size = self.vocab_size
self.text_config = text_config
if isinstance(self.text_config, dict):
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama"
self.text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
self.vocab_size = self.text_config.vocab_size
elif text_config is None:
self.text_config = CONFIG_MAPPING["llama"]()
super().__init__(**kwargs)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/lilt/configuration_lilt.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" LiLT configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
LILT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
),
}
class LiltConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LiltModel`]. It is used to instantiate a LiLT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the LiLT
[SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the LiLT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`LiltModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer. Should be a multiple of 24.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`LiltModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
classifier_dropout (`float`, *optional*):
The dropout ratio for the classification head.
channel_shrink_ratio (`int`, *optional*, defaults to 4):
The shrink ratio compared to the `hidden_size` for the channel dimension of the layout embeddings.
max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum value that the 2D position embedding might ever be used with. Typically set this to something
large just in case (e.g., 1024).
Examples:
```python
>>> from transformers import LiltConfig, LiltModel
>>> # Initializing a LiLT SCUT-DLVCLab/lilt-roberta-en-base style configuration
>>> configuration = LiltConfig()
>>> # Randomly initializing a model from the SCUT-DLVCLab/lilt-roberta-en-base style configuration
>>> model = LiltModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "lilt"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
position_embedding_type="absolute",
classifier_dropout=None,
channel_shrink_ratio=4,
max_2d_position_embeddings=1024,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.classifier_dropout = classifier_dropout
self.channel_shrink_ratio = channel_shrink_ratio
self.max_2d_position_embeddings = max_2d_position_embeddings
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/lilt/modeling_lilt.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch LiLT model."""
import math
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPooling,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_lilt import LiltConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "LiltConfig"
LILT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"SCUT-DLVCLab/lilt-roberta-en-base",
# See all LiLT models at https://huggingface.co/models?filter=lilt
]
class LiltTextEmbeddings(nn.Module):
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
# End copy
self.padding_idx = config.pad_token_id
self.position_embeddings = nn.Embedding(
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
)
def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
):
if position_ids is None:
if input_ids is not None:
# Create the position ids from the input token ids. Any padded tokens remain padded.
position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx).to(
input_ids.device
)
else:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings, position_ids
def create_position_ids_from_input_ids(self, input_ids, padding_idx):
"""
Args:
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
symbols are ignored. This is modified from fairseq's `utils.make_positions`.
x: torch.Tensor x:
Returns: torch.Tensor
"""
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
mask = input_ids.ne(padding_idx).int()
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask)) * mask
return incremental_indices.long() + padding_idx
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
"""
Args:
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
"""
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
)
return position_ids.unsqueeze(0).expand(input_shape)
class LiltLayoutEmbeddings(nn.Module):
def __init__(self, config):
super().__init__()
# we divide the hidden_size by 6 here as there are 6 different layout embeddings,
# namely left_position, upper_position, right_position, lower_position, height, width
self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size // 6)
self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size // 6)
self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size // 6)
self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size // 6)
self.padding_idx = config.pad_token_id
self.box_position_embeddings = nn.Embedding(
config.max_position_embeddings,
config.hidden_size // config.channel_shrink_ratio,
padding_idx=self.padding_idx,
)
self.box_linear_embeddings = nn.Linear(
in_features=config.hidden_size, out_features=config.hidden_size // config.channel_shrink_ratio
)
self.LayerNorm = nn.LayerNorm(config.hidden_size // config.channel_shrink_ratio, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, bbox=None, position_ids=None):
try:
left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2])
lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3])
except IndexError as e:
raise IndexError("The `bbox` coordinate values should be within 0-1000 range.") from e
h_position_embeddings = self.h_position_embeddings(bbox[:, :, 3] - bbox[:, :, 1])
w_position_embeddings = self.w_position_embeddings(bbox[:, :, 2] - bbox[:, :, 0])
spatial_position_embeddings = torch.cat(
[
left_position_embeddings,
upper_position_embeddings,
right_position_embeddings,
lower_position_embeddings,
h_position_embeddings,
w_position_embeddings,
],
dim=-1,
)
spatial_position_embeddings = self.box_linear_embeddings(spatial_position_embeddings)
box_position_embeddings = self.box_position_embeddings(position_ids)
spatial_position_embeddings = spatial_position_embeddings + box_position_embeddings
spatial_position_embeddings = self.LayerNorm(spatial_position_embeddings)
spatial_position_embeddings = self.dropout(spatial_position_embeddings)
return spatial_position_embeddings
class LiltSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.layout_query = nn.Linear(
config.hidden_size // config.channel_shrink_ratio, self.all_head_size // config.channel_shrink_ratio
)
self.layout_key = nn.Linear(
config.hidden_size // config.channel_shrink_ratio, self.all_head_size // config.channel_shrink_ratio
)
self.layout_value = nn.Linear(
config.hidden_size // config.channel_shrink_ratio, self.all_head_size // config.channel_shrink_ratio
)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(
config, "position_embedding_type", "absolute"
)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
self.channel_shrink_ratio = config.channel_shrink_ratio
def transpose_for_scores(self, x, r=1):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size // r)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
layout_inputs,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
layout_value_layer = self.transpose_for_scores(self.layout_value(layout_inputs), r=self.channel_shrink_ratio)
layout_key_layer = self.transpose_for_scores(self.layout_key(layout_inputs), r=self.channel_shrink_ratio)
layout_query_layer = self.transpose_for_scores(self.layout_query(layout_inputs), r=self.channel_shrink_ratio)
mixed_query_layer = self.query(hidden_states)
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
layout_attention_scores = torch.matmul(layout_query_layer, layout_key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
seq_length = hidden_states.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
tmp_attention_scores = attention_scores / math.sqrt(self.attention_head_size)
tmp_layout_attention_scores = layout_attention_scores / math.sqrt(
self.attention_head_size // self.channel_shrink_ratio
)
attention_scores = tmp_attention_scores + tmp_layout_attention_scores
layout_attention_scores = tmp_layout_attention_scores + tmp_attention_scores
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
layout_attention_scores = layout_attention_scores + attention_mask
# Normalize the attention scores to probabilities.
layout_attention_probs = nn.Softmax(dim=-1)(layout_attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
layout_attention_probs = self.dropout(layout_attention_probs)
# Mask heads if we want to
if head_mask is not None:
layout_attention_probs = layout_attention_probs * head_mask
layout_context_layer = torch.matmul(layout_attention_probs, layout_value_layer)
layout_context_layer = layout_context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = layout_context_layer.size()[:-2] + (self.all_head_size // self.channel_shrink_ratio,)
layout_context_layer = layout_context_layer.view(*new_context_layer_shape)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in RobertaModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.Softmax(dim=-1)(attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (
((context_layer, layout_context_layer), attention_probs)
if output_attentions
else ((context_layer, layout_context_layer),)
)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
class LiltSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class LiltAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = LiltSelfAttention(config, position_embedding_type=position_embedding_type)
self.output = LiltSelfOutput(config)
self.pruned_heads = set()
ori_hidden_size = config.hidden_size
config.hidden_size = config.hidden_size // config.channel_shrink_ratio
self.layout_output = LiltSelfOutput(config)
config.hidden_size = ori_hidden_size
# Copied from transformers.models.bert.modeling_bert.BertAttention.prune_heads
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.Tensor,
layout_inputs: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = self.self(
hidden_states,
layout_inputs,
attention_mask,
head_mask,
output_attentions,
)
attention_output = self.output(self_outputs[0][0], hidden_states)
layout_attention_output = self.layout_output(self_outputs[0][1], layout_inputs)
outputs = ((attention_output, layout_attention_output),) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
class LiltIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOutput
class LiltOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class LiltLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = LiltAttention(config)
self.intermediate = LiltIntermediate(config)
self.output = LiltOutput(config)
ori_hidden_size = config.hidden_size
ori_intermediate_size = config.intermediate_size
config.hidden_size = config.hidden_size // config.channel_shrink_ratio
config.intermediate_size = config.intermediate_size // config.channel_shrink_ratio
self.layout_intermediate = LiltIntermediate(config)
self.layout_output = LiltOutput(config)
config.hidden_size = ori_hidden_size
config.intermediate_size = ori_intermediate_size
def forward(
self,
hidden_states: torch.Tensor,
layout_inputs: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_attention_outputs = self.attention(
hidden_states,
layout_inputs,
attention_mask,
head_mask,
output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0][0]
layout_attention_output = self_attention_outputs[0][1]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
layout_layer_output = apply_chunking_to_forward(
self.layout_feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, layout_attention_output
)
outputs = ((layer_output, layout_layer_output),) + outputs
return outputs
# Copied from transformers.models.bert.modeling_bert.BertLayer.feed_forward_chunk
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
def layout_feed_forward_chunk(self, attention_output):
intermediate_output = self.layout_intermediate(attention_output)
layer_output = self.layout_output(intermediate_output, attention_output)
return layer_output
class LiltEncoder(nn.Module):
# Copied from transformers.models.bert.modeling_bert.BertEncoder.__init__ with Bert->Lilt
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([LiltLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
layout_inputs: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple[torch.Tensor], BaseModelOutput]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
layout_inputs,
attention_mask,
layer_head_mask,
output_attentions,
)
else:
layer_outputs = layer_module(
hidden_states,
layout_inputs,
attention_mask,
layer_head_mask,
output_attentions,
)
hidden_states = layer_outputs[0][0]
layout_inputs = layer_outputs[0][1]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
all_hidden_states,
all_self_attentions,
]
if v is not None
)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertPooler
class LiltPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
class LiltPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LiltConfig
base_model_prefix = "lilt"
supports_gradient_checkpointing = True
_no_split_modules = []
# Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
LILT_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`LiltConfig`]): Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
LILT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
bbox (`torch.LongTensor` of shape `({0}, 4)`, *optional*):
Bounding boxes of each input sequence tokens. Selected in the range `[0,
config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
y1) represents the position of the lower right corner. See [Overview](#Overview) for normalization.
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare LiLT Model transformer outputting raw hidden-states without any specific head on top.",
LILT_START_DOCSTRING,
)
class LiltModel(LiltPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = LiltTextEmbeddings(config)
self.layout_embeddings = LiltLayoutEmbeddings(config)
self.encoder = LiltEncoder(config)
self.pooler = LiltPooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(LILT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
bbox: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPooling]:
r"""
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, AutoModel
>>> from datasets import load_dataset
>>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
>>> model = AutoModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
>>> example = dataset[0]
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> encoding = tokenizer(words, boxes=boxes, return_tensors="pt")
>>> outputs = model(**encoding)
>>> last_hidden_states = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
if bbox is None:
bbox = torch.zeros(input_shape + (4,), dtype=torch.long, device=device)
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length)), device=device)
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output, position_ids = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
layout_embedding_output = self.layout_embeddings(bbox=bbox, position_ids=position_ids)
encoder_outputs = self.encoder(
embedding_output,
layout_embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"""
LiLT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
""",
LILT_START_DOCSTRING,
)
class LiltForSequenceClassification(LiltPreTrainedModel):
# Copied from transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification.__init__ with Roberta->Lilt, roberta->lilt
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.lilt = LiltModel(config, add_pooling_layer=False)
self.classifier = LiltClassificationHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(LILT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
>>> from datasets import load_dataset
>>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
>>> model = AutoModelForSequenceClassification.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
>>> example = dataset[0]
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> encoding = tokenizer(words, boxes=boxes, return_tensors="pt")
>>> outputs = model(**encoding)
>>> predicted_class_idx = outputs.logits.argmax(-1).item()
>>> predicted_class = model.config.id2label[predicted_class_idx]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.lilt(
input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
Lilt Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
LILT_START_DOCSTRING,
)
class LiltForTokenClassification(LiltPreTrainedModel):
# Copied from transformers.models.roberta.modeling_roberta.RobertaForTokenClassification.__init__ with Roberta->Lilt, roberta->lilt
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.lilt = LiltModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(LILT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, AutoModelForTokenClassification
>>> from datasets import load_dataset
>>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
>>> model = AutoModelForTokenClassification.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
>>> example = dataset[0]
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> encoding = tokenizer(words, boxes=boxes, return_tensors="pt")
>>> outputs = model(**encoding)
>>> predicted_class_indices = outputs.logits.argmax(-1)
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.lilt(
input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# Copied from transformers.models.roberta.modeling_roberta.RobertaClassificationHead with Roberta->Lilt
class LiltClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, features, **kwargs):
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
@add_start_docstrings(
"""
Lilt Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
LILT_START_DOCSTRING,
)
class LiltForQuestionAnswering(LiltPreTrainedModel):
# Copied from transformers.models.roberta.modeling_roberta.RobertaForQuestionAnswering.__init__ with Roberta->Lilt, roberta->lilt
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.lilt = LiltModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(LILT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, AutoModelForQuestionAnswering
>>> from datasets import load_dataset
>>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
>>> model = AutoModelForQuestionAnswering.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
>>> example = dataset[0]
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> encoding = tokenizer(words, boxes=boxes, return_tensors="pt")
>>> outputs = model(**encoding)
>>> answer_start_index = outputs.start_logits.argmax()
>>> answer_end_index = outputs.end_logits.argmax()
>>> predict_answer_tokens = encoding.input_ids[0, answer_start_index : answer_end_index + 1]
>>> predicted_answer = tokenizer.decode(predict_answer_tokens)
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.lilt(
input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/lilt/__init__.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_import_structure = {
"configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_lilt"] = [
"LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
"LiltForQuestionAnswering",
"LiltForSequenceClassification",
"LiltForTokenClassification",
"LiltModel",
"LiltPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lilt import (
LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
LiltPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 |
hf_public_repos/transformers/src/transformers/models | hf_public_repos/transformers/src/transformers/models/pix2struct/processing_pix2struct.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for Pix2Struct.
"""
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class Pix2StructProcessor(ProcessorMixin):
r"""
Constructs a PIX2STRUCT processor which wraps a BERT tokenizer and PIX2STRUCT image processor into a single
processor.
[`Pix2StructProcessor`] offers all the functionalities of [`Pix2StructImageProcessor`] and [`T5TokenizerFast`]. See
the docstring of [`~Pix2StructProcessor.__call__`] and [`~Pix2StructProcessor.decode`] for more information.
Args:
image_processor (`Pix2StructImageProcessor`):
An instance of [`Pix2StructImageProcessor`]. The image processor is a required input.
tokenizer (Union[`T5TokenizerFast`, `T5Tokenizer`]):
An instance of ['T5TokenizerFast`] or ['T5Tokenizer`]. The tokenizer is a required input.
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "Pix2StructImageProcessor"
tokenizer_class = ("T5Tokenizer", "T5TokenizerFast")
def __init__(self, image_processor, tokenizer):
tokenizer.return_token_type_ids = False
super().__init__(image_processor, tokenizer)
def __call__(
self,
images=None,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
max_patches: Optional[int] = 2048,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_token_type_ids: bool = False,
return_length: bool = False,
verbose: bool = True,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs,
) -> BatchEncoding:
"""
This method uses [`Pix2StructImageProcessor.preprocess`] method to prepare image(s) for the model, and
[`T5TokenizerFast.__call__`] to prepare text for the model.
Please refer to the docstring of the above two methods for more information.
"""
if images is None and text is None:
raise ValueError("You have to specify either images or text.")
# Get only text
if images is None and not self.image_processor.is_vqa:
self.current_processor = self.tokenizer
text_encoding = self.tokenizer(
text=text,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_token_type_ids=return_token_type_ids,
return_length=return_length,
verbose=verbose,
return_tensors=return_tensors,
**kwargs,
)
return text_encoding
if not self.image_processor.is_vqa:
# add pixel_values
encoding_image_processor = self.image_processor(
images, return_tensors=return_tensors, max_patches=max_patches, **kwargs
)
else:
# add pixel_values and bbox
encoding_image_processor = self.image_processor(
images, return_tensors=return_tensors, max_patches=max_patches, header_text=text, **kwargs
)
if text is not None and not self.image_processor.is_vqa:
text_encoding = self.tokenizer(
text=text,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_token_type_ids=return_token_type_ids,
return_length=return_length,
verbose=verbose,
return_tensors=return_tensors,
**kwargs,
)
if "attention_mask" in text_encoding:
text_encoding["decoder_attention_mask"] = text_encoding.pop("attention_mask")
if "input_ids" in text_encoding:
text_encoding["decoder_input_ids"] = text_encoding.pop("input_ids")
else:
text_encoding = None
if text_encoding is not None:
encoding_image_processor.update(text_encoding)
return encoding_image_processor
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to Pix2StructTokenizerFast's [`~PreTrainedTokenizer.batch_decode`].
Please refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to Pix2StructTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
| 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.