Llama-3.1-8B-DALv0.1
/
venv
/lib
/python3.12
/site-packages
/transformers
/models
/fnet
/modeling_fnet.py
# coding=utf-8 | |
# Copyright 2021 Google 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. | |
"""PyTorch FNet model.""" | |
import warnings | |
from dataclasses import dataclass | |
from functools import partial | |
from typing import Optional, Tuple, Union | |
import torch | |
import torch.utils.checkpoint | |
from torch import nn | |
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss | |
from ...utils import is_scipy_available | |
if is_scipy_available(): | |
from scipy import linalg | |
from ...activations import ACT2FN | |
from ...modeling_outputs import ( | |
BaseModelOutput, | |
BaseModelOutputWithPooling, | |
MaskedLMOutput, | |
ModelOutput, | |
MultipleChoiceModelOutput, | |
NextSentencePredictorOutput, | |
QuestionAnsweringModelOutput, | |
SequenceClassifierOutput, | |
TokenClassifierOutput, | |
) | |
from ...modeling_utils import PreTrainedModel | |
from ...pytorch_utils import apply_chunking_to_forward | |
from ...utils import ( | |
add_code_sample_docstrings, | |
add_start_docstrings, | |
add_start_docstrings_to_model_forward, | |
logging, | |
replace_return_docstrings, | |
) | |
from .configuration_fnet import FNetConfig | |
logger = logging.get_logger(__name__) | |
_CHECKPOINT_FOR_DOC = "google/fnet-base" | |
_CONFIG_FOR_DOC = "FNetConfig" | |
# Adapted from https://github.com/google-research/google-research/blob/master/f_net/fourier.py | |
def _two_dim_matmul(x, matrix_dim_one, matrix_dim_two): | |
"""Applies 2D matrix multiplication to 3D input arrays.""" | |
seq_length = x.shape[1] | |
matrix_dim_one = matrix_dim_one[:seq_length, :seq_length] | |
x = x.type(torch.complex64) | |
return torch.einsum("bij,jk,ni->bnk", x, matrix_dim_two, matrix_dim_one) | |
# # Adapted from https://github.com/google-research/google-research/blob/master/f_net/fourier.py | |
def two_dim_matmul(x, matrix_dim_one, matrix_dim_two): | |
return _two_dim_matmul(x, matrix_dim_one, matrix_dim_two) | |
# Adapted from https://github.com/google-research/google-research/blob/master/f_net/fourier.py | |
def fftn(x): | |
""" | |
Applies n-dimensional Fast Fourier Transform (FFT) to input array. | |
Args: | |
x: Input n-dimensional array. | |
Returns: | |
n-dimensional Fourier transform of input n-dimensional array. | |
""" | |
out = x | |
for axis in reversed(range(x.ndim)[1:]): # We don't need to apply FFT to last axis | |
out = torch.fft.fft(out, axis=axis) | |
return out | |
class FNetEmbeddings(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) | |
# NOTE: This is the project layer and will be needed. The original code allows for different embedding and different model dimensions. | |
self.projection = nn.Linear(config.hidden_size, config.hidden_size) | |
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.register_buffer( | |
"token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), 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 | |
position_embeddings = self.position_embeddings(position_ids) | |
embeddings += position_embeddings | |
embeddings = self.LayerNorm(embeddings) | |
embeddings = self.projection(embeddings) | |
embeddings = self.dropout(embeddings) | |
return embeddings | |
class FNetBasicFourierTransform(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self._init_fourier_transform(config) | |
def _init_fourier_transform(self, config): | |
if not config.use_tpu_fourier_optimizations: | |
self.fourier_transform = partial(torch.fft.fftn, dim=(1, 2)) | |
elif config.max_position_embeddings <= 4096: | |
if is_scipy_available(): | |
self.register_buffer( | |
"dft_mat_hidden", torch.tensor(linalg.dft(config.hidden_size), dtype=torch.complex64) | |
) | |
self.register_buffer( | |
"dft_mat_seq", torch.tensor(linalg.dft(config.tpu_short_seq_length), dtype=torch.complex64) | |
) | |
self.fourier_transform = partial( | |
two_dim_matmul, matrix_dim_one=self.dft_mat_seq, matrix_dim_two=self.dft_mat_hidden | |
) | |
else: | |
logging.warning( | |
"SciPy is needed for DFT matrix calculation and is not found. Using TPU optimized fast fourier" | |
" transform instead." | |
) | |
self.fourier_transform = fftn | |
else: | |
self.fourier_transform = fftn | |
def forward(self, hidden_states): | |
# NOTE: We do not use torch.vmap as it is not integrated into PyTorch stable versions. | |
# Interested users can modify the code to use vmap from the nightly versions, getting the vmap from here: | |
# https://pytorch.org/docs/master/generated/torch.vmap.html. Note that fourier transform methods will need | |
# change accordingly. | |
outputs = self.fourier_transform(hidden_states).real | |
return (outputs,) | |
class FNetBasicOutput(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) | |
def forward(self, hidden_states, input_tensor): | |
hidden_states = self.LayerNorm(input_tensor + hidden_states) | |
return hidden_states | |
class FNetFourierTransform(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.self = FNetBasicFourierTransform(config) | |
self.output = FNetBasicOutput(config) | |
def forward(self, hidden_states): | |
self_outputs = self.self(hidden_states) | |
fourier_output = self.output(self_outputs[0], hidden_states) | |
outputs = (fourier_output,) | |
return outputs | |
# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->FNet | |
class FNetIntermediate(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->FNet | |
class FNetOutput(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 FNetLayer(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.chunk_size_feed_forward = config.chunk_size_feed_forward | |
self.seq_len_dim = 1 # The dimension which has the sequence length | |
self.fourier = FNetFourierTransform(config) | |
self.intermediate = FNetIntermediate(config) | |
self.output = FNetOutput(config) | |
def forward(self, hidden_states): | |
self_fourier_outputs = self.fourier(hidden_states) | |
fourier_output = self_fourier_outputs[0] | |
layer_output = apply_chunking_to_forward( | |
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, fourier_output | |
) | |
outputs = (layer_output,) | |
return outputs | |
def feed_forward_chunk(self, fourier_output): | |
intermediate_output = self.intermediate(fourier_output) | |
layer_output = self.output(intermediate_output, fourier_output) | |
return layer_output | |
class FNetEncoder(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.config = config | |
self.layer = nn.ModuleList([FNetLayer(config) for _ in range(config.num_hidden_layers)]) | |
self.gradient_checkpointing = False | |
def forward(self, hidden_states, output_hidden_states=False, return_dict=True): | |
all_hidden_states = () if output_hidden_states 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) | |
else: | |
layer_outputs = layer_module(hidden_states) | |
hidden_states = layer_outputs[0] | |
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] if v is not None) | |
return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states) | |
# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->FNet | |
class FNetPooler(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->FNet | |
class FNetPredictionHeadTransform(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 | |
class FNetLMPredictionHead(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.transform = FNetPredictionHeadTransform(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) | |
self.bias = nn.Parameter(torch.zeros(config.vocab_size)) | |
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 | |
def _tie_weights(self) -> None: | |
# For accelerate compatibility and to not break backward compatibility | |
if self.decoder.bias.device.type == "meta": | |
self.decoder.bias = self.bias | |
else: | |
# To tie those two weights if they get disconnected (on TPU or when the bias is resized) | |
self.bias = self.decoder.bias | |
class FNetOnlyMLMHead(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.predictions = FNetLMPredictionHead(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->FNet | |
class FNetOnlyNSPHead(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->FNet | |
class FNetPreTrainingHeads(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.predictions = FNetLMPredictionHead(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 FNetPreTrainedModel(PreTrainedModel): | |
""" | |
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained | |
models. | |
""" | |
config_class = FNetConfig | |
base_model_prefix = "fnet" | |
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) | |
# NOTE: Original code uses same initialization as weights for biases as well. | |
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 FNetForPreTrainingOutput(ModelOutput): | |
""" | |
Output type of [`FNetForPreTraining`]. | |
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. | |
""" | |
loss: Optional[torch.FloatTensor] = None | |
prediction_logits: torch.FloatTensor = None | |
seq_relationship_logits: torch.FloatTensor = None | |
hidden_states: Optional[Tuple[torch.FloatTensor]] = None | |
FNET_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 ([`FNetConfig`]): 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. | |
""" | |
FNET_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) | |
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) | |
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_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 FNetModel(FNetPreTrainedModel): | |
""" | |
The model can behave as an encoder, following the architecture described in [FNet: Mixing Tokens with Fourier | |
Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon. | |
""" | |
def __init__(self, config, add_pooling_layer=True): | |
super().__init__(config) | |
self.config = config | |
self.embeddings = FNetEmbeddings(config) | |
self.encoder = FNetEncoder(config) | |
self.pooler = FNetPooler(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 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, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[tuple, BaseModelOutput]: | |
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: | |
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") | |
if ( | |
self.config.use_tpu_fourier_optimizations | |
and seq_length <= 4096 | |
and self.config.tpu_short_seq_length != seq_length | |
): | |
raise ValueError( | |
"The `tpu_short_seq_length` in FNetConfig should be set equal to the sequence length being passed to" | |
" the model when using TPU optimizations." | |
) | |
device = input_ids.device if input_ids is not None else inputs_embeds.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) | |
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, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
sequence_output = encoder_outputs[0] | |
pooler_output = self.pooler(sequence_output) if self.pooler is not None else None | |
if not return_dict: | |
return (sequence_output, pooler_output) + encoder_outputs[1:] | |
return BaseModelOutputWithPooling( | |
last_hidden_state=sequence_output, | |
pooler_output=pooler_output, | |
hidden_states=encoder_outputs.hidden_states, | |
) | |
class FNetForPreTraining(FNetPreTrainedModel): | |
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"] | |
def __init__(self, config): | |
super().__init__(config) | |
self.fnet = FNetModel(config) | |
self.cls = FNetPreTrainingHeads(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 | |
self.cls.predictions.bias = new_embeddings.bias | |
def forward( | |
self, | |
input_ids: Optional[torch.Tensor] = None, | |
token_type_ids: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.Tensor] = None, | |
inputs_embeds: Optional[torch.Tensor] = None, | |
labels: Optional[torch.Tensor] = None, | |
next_sentence_label: Optional[torch.Tensor] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple, FNetForPreTrainingOutput]: | |
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, FNetForPreTraining | |
>>> import torch | |
>>> tokenizer = AutoTokenizer.from_pretrained("google/fnet-base") | |
>>> model = FNetForPreTraining.from_pretrained("google/fnet-base") | |
>>> 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.fnet( | |
input_ids, | |
token_type_ids=token_type_ids, | |
position_ids=position_ids, | |
inputs_embeds=inputs_embeds, | |
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 FNetForPreTrainingOutput( | |
loss=total_loss, | |
prediction_logits=prediction_scores, | |
seq_relationship_logits=seq_relationship_score, | |
hidden_states=outputs.hidden_states, | |
) | |
class FNetForMaskedLM(FNetPreTrainedModel): | |
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"] | |
def __init__(self, config): | |
super().__init__(config) | |
self.fnet = FNetModel(config) | |
self.cls = FNetOnlyMLMHead(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 | |
self.cls.predictions.bias = new_embeddings.bias | |
def forward( | |
self, | |
input_ids: Optional[torch.Tensor] = None, | |
token_type_ids: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.Tensor] = None, | |
inputs_embeds: Optional[torch.Tensor] = None, | |
labels: Optional[torch.Tensor] = 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.fnet( | |
input_ids, | |
token_type_ids=token_type_ids, | |
position_ids=position_ids, | |
inputs_embeds=inputs_embeds, | |
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) | |
class FNetForNextSentencePrediction(FNetPreTrainedModel): | |
def __init__(self, config): | |
super().__init__(config) | |
self.fnet = FNetModel(config) | |
self.cls = FNetOnlyNSPHead(config) | |
# Initialize weights and apply final processing | |
self.post_init() | |
def forward( | |
self, | |
input_ids: Optional[torch.Tensor] = None, | |
token_type_ids: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.Tensor] = None, | |
inputs_embeds: Optional[torch.Tensor] = None, | |
labels: Optional[torch.Tensor] = 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, FNetForNextSentencePrediction | |
>>> import torch | |
>>> tokenizer = AutoTokenizer.from_pretrained("google/fnet-base") | |
>>> model = FNetForNextSentencePrediction.from_pretrained("google/fnet-base") | |
>>> 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.fnet( | |
input_ids, | |
token_type_ids=token_type_ids, | |
position_ids=position_ids, | |
inputs_embeds=inputs_embeds, | |
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, | |
) | |
class FNetForSequenceClassification(FNetPreTrainedModel): | |
def __init__(self, config): | |
super().__init__(config) | |
self.num_labels = config.num_labels | |
self.fnet = FNetModel(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 forward( | |
self, | |
input_ids: Optional[torch.Tensor] = None, | |
token_type_ids: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.Tensor] = None, | |
inputs_embeds: Optional[torch.Tensor] = None, | |
labels: Optional[torch.Tensor] = 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.fnet( | |
input_ids, | |
token_type_ids=token_type_ids, | |
position_ids=position_ids, | |
inputs_embeds=inputs_embeds, | |
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) | |
class FNetForMultipleChoice(FNetPreTrainedModel): | |
def __init__(self, config): | |
super().__init__(config) | |
self.fnet = FNetModel(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() | |
def forward( | |
self, | |
input_ids: Optional[torch.Tensor] = None, | |
token_type_ids: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.Tensor] = None, | |
inputs_embeds: Optional[torch.Tensor] = None, | |
labels: Optional[torch.Tensor] = 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 | |
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.fnet( | |
input_ids, | |
token_type_ids=token_type_ids, | |
position_ids=position_ids, | |
inputs_embeds=inputs_embeds, | |
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) | |
class FNetForTokenClassification(FNetPreTrainedModel): | |
def __init__(self, config): | |
super().__init__(config) | |
self.num_labels = config.num_labels | |
self.fnet = FNetModel(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 forward( | |
self, | |
input_ids: Optional[torch.Tensor] = None, | |
token_type_ids: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.Tensor] = None, | |
inputs_embeds: Optional[torch.Tensor] = None, | |
labels: Optional[torch.Tensor] = 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.fnet( | |
input_ids, | |
token_type_ids=token_type_ids, | |
position_ids=position_ids, | |
inputs_embeds=inputs_embeds, | |
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 | |
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) | |
class FNetForQuestionAnswering(FNetPreTrainedModel): | |
def __init__(self, config): | |
super().__init__(config) | |
self.num_labels = config.num_labels | |
self.fnet = FNetModel(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, | |
token_type_ids: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.Tensor] = None, | |
inputs_embeds: Optional[torch.Tensor] = None, | |
start_positions: Optional[torch.Tensor] = None, | |
end_positions: Optional[torch.Tensor] = 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.fnet( | |
input_ids, | |
token_type_ids=token_type_ids, | |
position_ids=position_ids, | |
inputs_embeds=inputs_embeds, | |
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 | |
) | |