1183 lines
48 KiB
Python
1183 lines
48 KiB
Python
# coding=utf-8
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# Copyright 2021 Google Research and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" PyTorch FNet model."""
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import warnings
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from dataclasses import dataclass
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from functools import partial
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from typing import Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...utils import is_scipy_available
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if is_scipy_available():
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from scipy import linalg
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from ...activations import ACT2FN
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from ...modeling_outputs import (
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BaseModelOutput,
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BaseModelOutputWithPooling,
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MaskedLMOutput,
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ModelOutput,
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MultipleChoiceModelOutput,
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NextSentencePredictorOutput,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutput,
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TokenClassifierOutput,
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)
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import apply_chunking_to_forward
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from ...utils import (
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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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logging,
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replace_return_docstrings,
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)
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from .configuration_fnet import FNetConfig
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "google/fnet-base"
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_CONFIG_FOR_DOC = "FNetConfig"
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from ..deprecated._archive_maps import FNET_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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# Adapted from https://github.com/google-research/google-research/blob/master/f_net/fourier.py
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def _two_dim_matmul(x, matrix_dim_one, matrix_dim_two):
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"""Applies 2D matrix multiplication to 3D input arrays."""
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seq_length = x.shape[1]
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matrix_dim_one = matrix_dim_one[:seq_length, :seq_length]
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x = x.type(torch.complex64)
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return torch.einsum("bij,jk,ni->bnk", x, matrix_dim_two, matrix_dim_one)
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# # Adapted from https://github.com/google-research/google-research/blob/master/f_net/fourier.py
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def two_dim_matmul(x, matrix_dim_one, matrix_dim_two):
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return _two_dim_matmul(x, matrix_dim_one, matrix_dim_two)
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# Adapted from https://github.com/google-research/google-research/blob/master/f_net/fourier.py
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def fftn(x):
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"""
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Applies n-dimensional Fast Fourier Transform (FFT) to input array.
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Args:
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x: Input n-dimensional array.
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Returns:
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n-dimensional Fourier transform of input n-dimensional array.
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"""
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out = x
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for axis in reversed(range(x.ndim)[1:]): # We don't need to apply FFT to last axis
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out = torch.fft.fft(out, axis=axis)
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return out
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class FNetEmbeddings(nn.Module):
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"""Construct the embeddings from word, position and token_type embeddings."""
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def __init__(self, config):
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super().__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
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# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
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# any TensorFlow checkpoint file
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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# NOTE: This is the project layer and will be needed. The original code allows for different embedding and different model dimensions.
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self.projection = nn.Linear(config.hidden_size, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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# position_ids (1, len position emb) is contiguous in memory and exported when serialized
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self.register_buffer(
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"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
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)
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self.register_buffer(
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"token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False
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)
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def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
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if input_ids is not None:
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input_shape = input_ids.size()
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else:
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input_shape = inputs_embeds.size()[:-1]
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seq_length = input_shape[1]
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if position_ids is None:
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position_ids = self.position_ids[:, :seq_length]
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# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
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# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
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# issue #5664
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if token_type_ids is None:
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if hasattr(self, "token_type_ids"):
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buffered_token_type_ids = self.token_type_ids[:, :seq_length]
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buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
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token_type_ids = buffered_token_type_ids_expanded
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else:
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token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = inputs_embeds + token_type_embeddings
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position_embeddings = self.position_embeddings(position_ids)
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embeddings += position_embeddings
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.projection(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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class FNetBasicFourierTransform(nn.Module):
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def __init__(self, config):
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super().__init__()
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self._init_fourier_transform(config)
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def _init_fourier_transform(self, config):
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if not config.use_tpu_fourier_optimizations:
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self.fourier_transform = partial(torch.fft.fftn, dim=(1, 2))
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elif config.max_position_embeddings <= 4096:
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if is_scipy_available():
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self.register_buffer(
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"dft_mat_hidden", torch.tensor(linalg.dft(config.hidden_size), dtype=torch.complex64)
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)
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self.register_buffer(
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"dft_mat_seq", torch.tensor(linalg.dft(config.tpu_short_seq_length), dtype=torch.complex64)
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)
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self.fourier_transform = partial(
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two_dim_matmul, matrix_dim_one=self.dft_mat_seq, matrix_dim_two=self.dft_mat_hidden
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)
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else:
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logging.warning(
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"SciPy is needed for DFT matrix calculation and is not found. Using TPU optimized fast fourier"
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" transform instead."
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)
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self.fourier_transform = fftn
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else:
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self.fourier_transform = fftn
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def forward(self, hidden_states):
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# NOTE: We do not use torch.vmap as it is not integrated into PyTorch stable versions.
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# Interested users can modify the code to use vmap from the nightly versions, getting the vmap from here:
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# https://pytorch.org/docs/master/generated/torch.vmap.html. Note that fourier transform methods will need
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# change accordingly.
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outputs = self.fourier_transform(hidden_states).real
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return (outputs,)
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class FNetBasicOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.LayerNorm(input_tensor + hidden_states)
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return hidden_states
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class FNetFourierTransform(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.self = FNetBasicFourierTransform(config)
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self.output = FNetBasicOutput(config)
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def forward(self, hidden_states):
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self_outputs = self.self(hidden_states)
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fourier_output = self.output(self_outputs[0], hidden_states)
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outputs = (fourier_output,)
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return outputs
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# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->FNet
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class FNetIntermediate(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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if isinstance(config.hidden_act, str):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]
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else:
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self.intermediate_act_fn = config.hidden_act
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->FNet
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class FNetOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class FNetLayer(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.chunk_size_feed_forward = config.chunk_size_feed_forward
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self.seq_len_dim = 1 # The dimension which has the sequence length
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self.fourier = FNetFourierTransform(config)
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self.intermediate = FNetIntermediate(config)
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self.output = FNetOutput(config)
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def forward(self, hidden_states):
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self_fourier_outputs = self.fourier(hidden_states)
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fourier_output = self_fourier_outputs[0]
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layer_output = apply_chunking_to_forward(
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self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, fourier_output
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)
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outputs = (layer_output,)
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return outputs
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def feed_forward_chunk(self, fourier_output):
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intermediate_output = self.intermediate(fourier_output)
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layer_output = self.output(intermediate_output, fourier_output)
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return layer_output
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class FNetEncoder(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.layer = nn.ModuleList([FNetLayer(config) for _ in range(config.num_hidden_layers)])
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self.gradient_checkpointing = False
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def forward(self, hidden_states, output_hidden_states=False, return_dict=True):
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all_hidden_states = () if output_hidden_states else None
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for i, layer_module in enumerate(self.layer):
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if self.gradient_checkpointing and self.training:
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layer_outputs = self._gradient_checkpointing_func(layer_module.__call__, hidden_states)
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else:
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layer_outputs = layer_module(hidden_states)
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hidden_states = layer_outputs[0]
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if not return_dict:
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return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
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return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
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# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->FNet
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class FNetPooler(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.activation = nn.Tanh()
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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# We "pool" the model by simply taking the hidden state corresponding
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# to the first token.
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first_token_tensor = hidden_states[:, 0]
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pooled_output = self.dense(first_token_tensor)
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pooled_output = self.activation(pooled_output)
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return pooled_output
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# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->FNet
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class FNetPredictionHeadTransform(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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if isinstance(config.hidden_act, str):
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self.transform_act_fn = ACT2FN[config.hidden_act]
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else:
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self.transform_act_fn = config.hidden_act
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.transform_act_fn(hidden_states)
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hidden_states = self.LayerNorm(hidden_states)
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return hidden_states
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class FNetLMPredictionHead(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.transform = FNetPredictionHeadTransform(config)
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# The output weights are the same as the input embeddings, but there is
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# an output-only bias for each token.
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self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
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self.bias = nn.Parameter(torch.zeros(config.vocab_size))
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self.decoder.bias = self.bias
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def forward(self, hidden_states):
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hidden_states = self.transform(hidden_states)
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hidden_states = self.decoder(hidden_states)
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return hidden_states
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def _tie_weights(self):
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# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
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self.bias = self.decoder.bias
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class FNetOnlyMLMHead(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.predictions = FNetLMPredictionHead(config)
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def forward(self, sequence_output):
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prediction_scores = self.predictions(sequence_output)
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return prediction_scores
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# Copied from transformers.models.bert.modeling_bert.BertOnlyNSPHead with Bert->FNet
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class FNetOnlyNSPHead(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.seq_relationship = nn.Linear(config.hidden_size, 2)
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def forward(self, pooled_output):
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seq_relationship_score = self.seq_relationship(pooled_output)
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return seq_relationship_score
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# Copied from transformers.models.bert.modeling_bert.BertPreTrainingHeads with Bert->FNet
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class FNetPreTrainingHeads(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.predictions = FNetLMPredictionHead(config)
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self.seq_relationship = nn.Linear(config.hidden_size, 2)
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def forward(self, sequence_output, pooled_output):
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prediction_scores = self.predictions(sequence_output)
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seq_relationship_score = self.seq_relationship(pooled_output)
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return prediction_scores, seq_relationship_score
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class FNetPreTrainedModel(PreTrainedModel):
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"""
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
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models.
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"""
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config_class = FNetConfig
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base_model_prefix = "fnet"
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supports_gradient_checkpointing = True
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def _init_weights(self, module):
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"""Initialize the weights"""
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if isinstance(module, nn.Linear):
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# Slightly different from the TF version which uses truncated_normal for initialization
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# cf https://github.com/pytorch/pytorch/pull/5617
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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# NOTE: Original code uses same initialization as weights for biases as well.
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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elif isinstance(module, nn.LayerNorm):
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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@dataclass
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class FNetForPreTrainingOutput(ModelOutput):
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"""
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Output type of [`FNetForPreTraining`].
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Args:
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loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
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Total loss as the sum of the masked language modeling loss and the next sequence prediction
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(classification) loss.
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prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
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Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
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seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
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Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
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before SoftMax).
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
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plus the initial embedding outputs.
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"""
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loss: Optional[torch.FloatTensor] = None
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prediction_logits: torch.FloatTensor = None
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seq_relationship_logits: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor]] = None
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FNET_START_DOCSTRING = r"""
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This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
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it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
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behavior.
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Parameters:
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config ([`FNetConfig`]): Model configuration class with all the parameters of the model.
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Initializing with a config file does not load the weights associated with the model, only the
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configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
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"""
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FNET_INPUTS_DOCSTRING = r"""
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Args:
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|
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.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare FNet Model transformer outputting raw hidden-states without any specific head on top.",
|
|
FNET_START_DOCSTRING,
|
|
)
|
|
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
|
|
|
|
@add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=BaseModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
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,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
FNet Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next
|
|
sentence prediction (classification)` head.
|
|
""",
|
|
FNET_START_DOCSTRING,
|
|
)
|
|
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
|
|
|
|
@add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=FNetForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
|
|
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,
|
|
)
|
|
|
|
|
|
@add_start_docstrings("""FNet Model with a `language modeling` head on top.""", FNET_START_DOCSTRING)
|
|
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
|
|
|
|
@add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=MaskedLMOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
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)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""FNet Model with a `next sentence prediction (classification)` head on top.""",
|
|
FNET_START_DOCSTRING,
|
|
)
|
|
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()
|
|
|
|
@add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
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,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
FNet Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
|
|
output) e.g. for GLUE tasks.
|
|
""",
|
|
FNET_START_DOCSTRING,
|
|
)
|
|
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()
|
|
|
|
@add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=SequenceClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
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)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
FNet Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
|
|
softmax) e.g. for RocStories/SWAG tasks.
|
|
""",
|
|
FNET_START_DOCSTRING,
|
|
)
|
|
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()
|
|
|
|
@add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=MultipleChoiceModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
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)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
FNet Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
|
|
Named-Entity-Recognition (NER) tasks.
|
|
""",
|
|
FNET_START_DOCSTRING,
|
|
)
|
|
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()
|
|
|
|
@add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=TokenClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
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)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
FNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
|
|
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
|
|
""",
|
|
FNET_START_DOCSTRING,
|
|
)
|
|
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()
|
|
|
|
@add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=QuestionAnsweringModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
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
|
|
)
|