Llama-3.1-8B-DALv0.1
/
venv
/lib
/python3.12
/site-packages
/transformers
/models
/electra
/modeling_electra.py
# coding=utf-8 | |
# Copyright 2019 The Google AI Language 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 ELECTRA model.""" | |
import math | |
import os | |
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, get_activation | |
from ...modeling_outputs import ( | |
BaseModelOutputWithCrossAttentions, | |
BaseModelOutputWithPastAndCrossAttentions, | |
CausalLMOutputWithCrossAttentions, | |
MaskedLMOutput, | |
MultipleChoiceModelOutput, | |
QuestionAnsweringModelOutput, | |
SequenceClassifierOutput, | |
TokenClassifierOutput, | |
) | |
from ...modeling_utils import PreTrainedModel, SequenceSummary | |
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_electra import ElectraConfig | |
logger = logging.get_logger(__name__) | |
_CHECKPOINT_FOR_DOC = "google/electra-small-discriminator" | |
_CONFIG_FOR_DOC = "ElectraConfig" | |
def load_tf_weights_in_electra(model, config, tf_checkpoint_path, discriminator_or_generator="discriminator"): | |
"""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): | |
original_name: str = name | |
try: | |
if isinstance(model, ElectraForMaskedLM): | |
name = name.replace("electra/embeddings/", "generator/embeddings/") | |
if discriminator_or_generator == "generator": | |
name = name.replace("electra/", "discriminator/") | |
name = name.replace("generator/", "electra/") | |
name = name.replace("dense_1", "dense_prediction") | |
name = name.replace("generator_predictions/output_bias", "generator_lm_head/bias") | |
name = name.split("/") | |
# print(original_name, name) | |
# 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 ["global_step", "temperature"] for n in name): | |
logger.info(f"Skipping {original_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: | |
pointer = getattr(pointer, scope_names[0]) | |
if len(scope_names) >= 2: | |
num = int(scope_names[1]) | |
pointer = pointer[num] | |
if m_name.endswith("_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 ValueError as e: | |
e.args += (pointer.shape, array.shape) | |
raise | |
print(f"Initialize PyTorch weight {name}", original_name) | |
pointer.data = torch.from_numpy(array) | |
except AttributeError as e: | |
print(f"Skipping {original_name}", name, e) | |
continue | |
return model | |
class ElectraEmbeddings(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.embedding_size, padding_idx=config.pad_token_id) | |
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size) | |
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_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.embedding_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") | |
self.register_buffer( | |
"token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False | |
) | |
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.forward | |
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 | |
# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Electra | |
class ElectraSelfAttention(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 ElectraModel 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 ElectraSelfOutput(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 | |
ELECTRA_SELF_ATTENTION_CLASSES = { | |
"eager": ElectraSelfAttention, | |
} | |
# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Electra,BERT->ELECTRA | |
class ElectraAttention(nn.Module): | |
def __init__(self, config, position_embedding_type=None): | |
super().__init__() | |
self.self = ELECTRA_SELF_ATTENTION_CLASSES[config._attn_implementation]( | |
config, position_embedding_type=position_embedding_type | |
) | |
self.output = ElectraSelfOutput(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 ElectraIntermediate(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 ElectraOutput(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->Electra | |
class ElectraLayer(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 = ElectraAttention(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 = ElectraAttention(config, position_embedding_type="absolute") | |
self.intermediate = ElectraIntermediate(config) | |
self.output = ElectraOutput(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->Electra | |
class ElectraEncoder(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.config = config | |
self.layer = nn.ModuleList([ElectraLayer(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 ElectraDiscriminatorPredictions(nn.Module): | |
"""Prediction module for the discriminator, made up of two dense layers.""" | |
def __init__(self, config): | |
super().__init__() | |
self.dense = nn.Linear(config.hidden_size, config.hidden_size) | |
self.activation = get_activation(config.hidden_act) | |
self.dense_prediction = nn.Linear(config.hidden_size, 1) | |
self.config = config | |
def forward(self, discriminator_hidden_states): | |
hidden_states = self.dense(discriminator_hidden_states) | |
hidden_states = self.activation(hidden_states) | |
logits = self.dense_prediction(hidden_states).squeeze(-1) | |
return logits | |
class ElectraGeneratorPredictions(nn.Module): | |
"""Prediction module for the generator, made up of two dense layers.""" | |
def __init__(self, config): | |
super().__init__() | |
self.activation = get_activation("gelu") | |
self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps) | |
self.dense = nn.Linear(config.hidden_size, config.embedding_size) | |
def forward(self, generator_hidden_states): | |
hidden_states = self.dense(generator_hidden_states) | |
hidden_states = self.activation(hidden_states) | |
hidden_states = self.LayerNorm(hidden_states) | |
return hidden_states | |
class ElectraPreTrainedModel(PreTrainedModel): | |
""" | |
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained | |
models. | |
""" | |
config_class = ElectraConfig | |
load_tf_weights = load_tf_weights_in_electra | |
base_model_prefix = "electra" | |
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) | |
class ElectraForPreTrainingOutput(ModelOutput): | |
""" | |
Output type of [`ElectraForPreTraining`]. | |
Args: | |
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): | |
Total loss of the ELECTRA objective. | |
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): | |
Prediction scores of the head (scores for each token 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 | |
logits: torch.FloatTensor = None | |
hidden_states: Optional[Tuple[torch.FloatTensor]] = None | |
attentions: Optional[Tuple[torch.FloatTensor]] = None | |
ELECTRA_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 ([`ElectraConfig`]): 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. | |
""" | |
ELECTRA_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. | |
encoder_hidden_states (`torch.FloatTensor` of shape `({0}, 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 `({0})`, *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 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 ElectraModel(ElectraPreTrainedModel): | |
def __init__(self, config): | |
super().__init__(config) | |
self.embeddings = ElectraEmbeddings(config) | |
if config.embedding_size != config.hidden_size: | |
self.embeddings_project = nn.Linear(config.embedding_size, config.hidden_size) | |
self.encoder = ElectraEncoder(config) | |
self.config = 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) | |
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], 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 | |
# 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(input_shape, 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) | |
extended_attention_mask = 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 | |
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) | |
hidden_states = 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, | |
) | |
if hasattr(self, "embeddings_project"): | |
hidden_states = self.embeddings_project(hidden_states) | |
hidden_states = self.encoder( | |
hidden_states, | |
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, | |
) | |
return hidden_states | |
class ElectraClassificationHead(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.activation = get_activation("gelu") | |
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 = self.activation(x) # although BERT uses tanh here, it seems Electra authors used gelu here | |
x = self.dropout(x) | |
x = self.out_proj(x) | |
return x | |
class ElectraForSequenceClassification(ElectraPreTrainedModel): | |
def __init__(self, config): | |
super().__init__(config) | |
self.num_labels = config.num_labels | |
self.config = config | |
self.electra = ElectraModel(config) | |
self.classifier = ElectraClassificationHead(config) | |
# Initialize weights and apply final processing | |
self.post_init() | |
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[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 | |
discriminator_hidden_states = self.electra( | |
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 = discriminator_hidden_states[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,) + discriminator_hidden_states[1:] | |
return ((loss,) + output) if loss is not None else output | |
return SequenceClassifierOutput( | |
loss=loss, | |
logits=logits, | |
hidden_states=discriminator_hidden_states.hidden_states, | |
attentions=discriminator_hidden_states.attentions, | |
) | |
class ElectraForPreTraining(ElectraPreTrainedModel): | |
def __init__(self, config): | |
super().__init__(config) | |
self.electra = ElectraModel(config) | |
self.discriminator_predictions = ElectraDiscriminatorPredictions(config) | |
# Initialize weights and apply final processing | |
self.post_init() | |
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[torch.Tensor], ElectraForPreTrainingOutput]: | |
r""" | |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Labels for computing the ELECTRA loss. Input should be a sequence of tokens (see `input_ids` docstring) | |
Indices should be in `[0, 1]`: | |
- 0 indicates the token is an original token, | |
- 1 indicates the token was replaced. | |
Returns: | |
Examples: | |
```python | |
>>> from transformers import ElectraForPreTraining, AutoTokenizer | |
>>> import torch | |
>>> discriminator = ElectraForPreTraining.from_pretrained("google/electra-base-discriminator") | |
>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-base-discriminator") | |
>>> sentence = "The quick brown fox jumps over the lazy dog" | |
>>> fake_sentence = "The quick brown fox fake over the lazy dog" | |
>>> fake_tokens = tokenizer.tokenize(fake_sentence, add_special_tokens=True) | |
>>> fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt") | |
>>> discriminator_outputs = discriminator(fake_inputs) | |
>>> predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2) | |
>>> fake_tokens | |
['[CLS]', 'the', 'quick', 'brown', 'fox', 'fake', 'over', 'the', 'lazy', 'dog', '[SEP]'] | |
>>> predictions.squeeze().tolist() | |
[0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0] | |
```""" | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
discriminator_hidden_states = self.electra( | |
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, | |
) | |
discriminator_sequence_output = discriminator_hidden_states[0] | |
logits = self.discriminator_predictions(discriminator_sequence_output) | |
loss = None | |
if labels is not None: | |
loss_fct = nn.BCEWithLogitsLoss() | |
if attention_mask is not None: | |
active_loss = attention_mask.view(-1, discriminator_sequence_output.shape[1]) == 1 | |
active_logits = logits.view(-1, discriminator_sequence_output.shape[1])[active_loss] | |
active_labels = labels[active_loss] | |
loss = loss_fct(active_logits, active_labels.float()) | |
else: | |
loss = loss_fct(logits.view(-1, discriminator_sequence_output.shape[1]), labels.float()) | |
if not return_dict: | |
output = (logits,) + discriminator_hidden_states[1:] | |
return ((loss,) + output) if loss is not None else output | |
return ElectraForPreTrainingOutput( | |
loss=loss, | |
logits=logits, | |
hidden_states=discriminator_hidden_states.hidden_states, | |
attentions=discriminator_hidden_states.attentions, | |
) | |
class ElectraForMaskedLM(ElectraPreTrainedModel): | |
_tied_weights_keys = ["generator_lm_head.weight"] | |
def __init__(self, config): | |
super().__init__(config) | |
self.electra = ElectraModel(config) | |
self.generator_predictions = ElectraGeneratorPredictions(config) | |
self.generator_lm_head = nn.Linear(config.embedding_size, config.vocab_size) | |
# Initialize weights and apply final processing | |
self.post_init() | |
def get_output_embeddings(self): | |
return self.generator_lm_head | |
def set_output_embeddings(self, word_embeddings): | |
self.generator_lm_head = word_embeddings | |
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[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 | |
generator_hidden_states = self.electra( | |
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, | |
) | |
generator_sequence_output = generator_hidden_states[0] | |
prediction_scores = self.generator_predictions(generator_sequence_output) | |
prediction_scores = self.generator_lm_head(prediction_scores) | |
loss = None | |
# Masked language modeling softmax layer | |
if labels is not None: | |
loss_fct = nn.CrossEntropyLoss() # -100 index = padding token | |
loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) | |
if not return_dict: | |
output = (prediction_scores,) + generator_hidden_states[1:] | |
return ((loss,) + output) if loss is not None else output | |
return MaskedLMOutput( | |
loss=loss, | |
logits=prediction_scores, | |
hidden_states=generator_hidden_states.hidden_states, | |
attentions=generator_hidden_states.attentions, | |
) | |
class ElectraForTokenClassification(ElectraPreTrainedModel): | |
def __init__(self, config): | |
super().__init__(config) | |
self.num_labels = config.num_labels | |
self.electra = ElectraModel(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() | |
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[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 | |
discriminator_hidden_states = self.electra( | |
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, | |
) | |
discriminator_sequence_output = discriminator_hidden_states[0] | |
discriminator_sequence_output = self.dropout(discriminator_sequence_output) | |
logits = self.classifier(discriminator_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,) + discriminator_hidden_states[1:] | |
return ((loss,) + output) if loss is not None else output | |
return TokenClassifierOutput( | |
loss=loss, | |
logits=logits, | |
hidden_states=discriminator_hidden_states.hidden_states, | |
attentions=discriminator_hidden_states.attentions, | |
) | |
class ElectraForQuestionAnswering(ElectraPreTrainedModel): | |
config_class = ElectraConfig | |
base_model_prefix = "electra" | |
def __init__(self, config): | |
super().__init__(config) | |
self.num_labels = config.num_labels | |
self.electra = ElectraModel(config) | |
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) | |
# Initialize weights and apply final processing | |
self.post_init() | |
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[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 | |
discriminator_hidden_states = self.electra( | |
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, | |
) | |
sequence_output = discriminator_hidden_states[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, | |
) + discriminator_hidden_states[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=discriminator_hidden_states.hidden_states, | |
attentions=discriminator_hidden_states.attentions, | |
) | |
class ElectraForMultipleChoice(ElectraPreTrainedModel): | |
def __init__(self, config): | |
super().__init__(config) | |
self.electra = ElectraModel(config) | |
self.sequence_summary = SequenceSummary(config) | |
self.classifier = nn.Linear(config.hidden_size, 1) | |
# Initialize weights and apply final processing | |
self.post_init() | |
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[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 | |
) | |
discriminator_hidden_states = self.electra( | |
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 = discriminator_hidden_states[0] | |
pooled_output = self.sequence_summary(sequence_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,) + discriminator_hidden_states[1:] | |
return ((loss,) + output) if loss is not None else output | |
return MultipleChoiceModelOutput( | |
loss=loss, | |
logits=reshaped_logits, | |
hidden_states=discriminator_hidden_states.hidden_states, | |
attentions=discriminator_hidden_states.attentions, | |
) | |
class ElectraForCausalLM(ElectraPreTrainedModel): | |
_tied_weights_keys = ["generator_lm_head.weight"] | |
def __init__(self, config): | |
super().__init__(config) | |
if not config.is_decoder: | |
logger.warning("If you want to use `ElectraForCausalLM` as a standalone, add `is_decoder=True.`") | |
self.electra = ElectraModel(config) | |
self.generator_predictions = ElectraGeneratorPredictions(config) | |
self.generator_lm_head = nn.Linear(config.embedding_size, config.vocab_size) | |
self.init_weights() | |
def get_output_embeddings(self): | |
return self.generator_lm_head | |
def set_output_embeddings(self, new_embeddings): | |
self.generator_lm_head = new_embeddings | |
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, | |
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 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, ElectraForCausalLM, ElectraConfig | |
>>> import torch | |
>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-base-generator") | |
>>> config = ElectraConfig.from_pretrained("google/electra-base-generator") | |
>>> config.is_decoder = True | |
>>> model = ElectraForCausalLM.from_pretrained("google/electra-base-generator", 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.electra( | |
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.generator_lm_head(self.generator_predictions(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[1:] | |
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.roberta.modeling_roberta.RobertaForCausalLM.prepare_inputs_for_generation | |
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} | |
# Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM._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 | |