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1300 lines
53 KiB
1300 lines
53 KiB
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/roberta/modular_roberta.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_roberta.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. 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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from collections.abc import Callable
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import torch
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import torch.nn as nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ... import initialization as init
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from ...activations import ACT2FN, gelu
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from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
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from ...generation import GenerationMixin
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from ...masking_utils import create_bidirectional_mask, create_causal_mask
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import (
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BaseModelOutputWithPastAndCrossAttentions,
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BaseModelOutputWithPoolingAndCrossAttentions,
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CausalLMOutputWithCrossAttentions,
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MaskedLMOutput,
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MultipleChoiceModelOutput,
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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 ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...pytorch_utils import apply_chunking_to_forward
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from ...utils import TransformersKwargs, auto_docstring, logging
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from ...utils.generic import can_return_tuple, check_model_inputs
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from .configuration_roberta import RobertaConfig
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logger = logging.get_logger(__name__)
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class RobertaEmbeddings(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.token_type_embeddings = nn.Embedding(config.type_vocab_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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# 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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self.padding_idx = config.pad_token_id
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self.position_embeddings = nn.Embedding(
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config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
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)
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def forward(
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self,
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input_ids: torch.LongTensor | None = None,
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token_type_ids: torch.LongTensor | None = None,
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position_ids: torch.LongTensor | None = None,
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inputs_embeds: torch.FloatTensor | None = None,
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past_key_values_length: int = 0,
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) -> torch.Tensor:
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if position_ids is None:
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if input_ids is not None:
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# Create the position ids from the input token ids. Any padded tokens remain padded.
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position_ids = self.create_position_ids_from_input_ids(
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input_ids, self.padding_idx, past_key_values_length
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)
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else:
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position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds, self.padding_idx)
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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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batch_size, seq_length = input_shape
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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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# NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0])
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buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1)
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buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids)
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token_type_ids = buffered_token_type_ids.expand(batch_size, seq_length)
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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 = embeddings + position_embeddings
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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@staticmethod
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def create_position_ids_from_inputs_embeds(inputs_embeds, padding_idx):
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"""
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We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
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Args:
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inputs_embeds: torch.Tensor
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Returns: torch.Tensor
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"""
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input_shape = inputs_embeds.size()[:-1]
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sequence_length = input_shape[1]
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position_ids = torch.arange(
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padding_idx + 1, sequence_length + padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
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)
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return position_ids.unsqueeze(0).expand(input_shape)
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@staticmethod
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def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
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"""
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Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
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are ignored. This is modified from fairseq's `utils.make_positions`.
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Args:
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x: torch.Tensor x:
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Returns: torch.Tensor
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"""
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# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
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mask = input_ids.ne(padding_idx).int()
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incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
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return incremental_indices.long() + padding_idx
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: torch.Tensor | None,
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scaling: float | None = None,
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dropout: float = 0.0,
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**kwargs: Unpack[TransformersKwargs],
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):
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if scaling is None:
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scaling = query.size(-1) ** -0.5
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
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if attention_mask is not None:
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attention_mask = attention_mask[:, :, :, : key.shape[-2]]
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attn_weights = attn_weights + attention_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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class RobertaSelfAttention(nn.Module):
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def __init__(self, config, is_causal=False, layer_idx=None):
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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raise ValueError(
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f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
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f"heads ({config.num_attention_heads})"
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)
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self.config = config
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.scaling = self.attention_head_size**-0.5
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self.query = nn.Linear(config.hidden_size, self.all_head_size)
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self.key = nn.Linear(config.hidden_size, self.all_head_size)
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self.value = nn.Linear(config.hidden_size, self.all_head_size)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.is_decoder = config.is_decoder
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self.is_causal = is_causal
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self.layer_idx = layer_idx
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: torch.FloatTensor | None = None,
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past_key_values: Cache | None = None,
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cache_position: torch.Tensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.attention_head_size)
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# get all proj
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query_layer = self.query(hidden_states).view(*hidden_shape).transpose(1, 2)
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key_layer = self.key(hidden_states).view(*hidden_shape).transpose(1, 2)
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value_layer = self.value(hidden_states).view(*hidden_shape).transpose(1, 2)
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if past_key_values is not None:
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# decoder-only roberta can have a simple dynamic cache for example
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current_past_key_values = past_key_values
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if isinstance(past_key_values, EncoderDecoderCache):
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current_past_key_values = past_key_values.self_attention_cache
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# save all key/value_layer to cache to be re-used for fast auto-regressive generation
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key_layer, value_layer = current_past_key_values.update(
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key_layer,
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value_layer,
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self.layer_idx,
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{"cache_position": cache_position},
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)
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attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
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self.config._attn_implementation, eager_attention_forward
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)
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attn_output, attn_weights = attention_interface(
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self,
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query_layer,
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key_layer,
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value_layer,
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attention_mask,
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dropout=0.0 if not self.training else self.dropout.p,
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scaling=self.scaling,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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return attn_output, attn_weights
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class RobertaCrossAttention(nn.Module):
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def __init__(self, config, is_causal=False, layer_idx=None):
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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raise ValueError(
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f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
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f"heads ({config.num_attention_heads})"
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)
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self.config = config
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.scaling = self.attention_head_size**-0.5
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self.query = nn.Linear(config.hidden_size, self.all_head_size)
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self.key = nn.Linear(config.hidden_size, self.all_head_size)
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self.value = nn.Linear(config.hidden_size, self.all_head_size)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.is_causal = is_causal
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self.layer_idx = layer_idx
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def forward(
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self,
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hidden_states: torch.Tensor,
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encoder_hidden_states: torch.FloatTensor | None = None,
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attention_mask: torch.FloatTensor | None = None,
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past_key_values: EncoderDecoderCache | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor]:
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# determine input shapes
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bsz, tgt_len = hidden_states.shape[:-1]
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src_len = encoder_hidden_states.shape[1]
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q_input_shape = (bsz, tgt_len, -1, self.attention_head_size)
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kv_input_shape = (bsz, src_len, -1, self.attention_head_size)
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# get query proj
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query_layer = self.query(hidden_states).view(*q_input_shape).transpose(1, 2)
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is_updated = past_key_values.is_updated.get(self.layer_idx) if past_key_values is not None else False
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if past_key_values is not None and is_updated:
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# reuse k,v, cross_attentions
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key_layer = past_key_values.cross_attention_cache.layers[self.layer_idx].keys
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value_layer = past_key_values.cross_attention_cache.layers[self.layer_idx].values
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else:
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key_layer = self.key(encoder_hidden_states).view(*kv_input_shape).transpose(1, 2)
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value_layer = self.value(encoder_hidden_states).view(*kv_input_shape).transpose(1, 2)
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if past_key_values is not None:
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# save all states to the cache
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key_layer, value_layer = past_key_values.cross_attention_cache.update(
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key_layer, value_layer, self.layer_idx
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)
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# set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
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past_key_values.is_updated[self.layer_idx] = True
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attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
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self.config._attn_implementation, eager_attention_forward
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)
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attn_output, attn_weights = attention_interface(
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self,
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query_layer,
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key_layer,
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value_layer,
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attention_mask,
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dropout=0.0 if not self.training else self.dropout.p,
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scaling=self.scaling,
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**kwargs,
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)
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attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
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return attn_output, attn_weights
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class RobertaSelfOutput(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.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 RobertaAttention(nn.Module):
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def __init__(self, config, is_causal=False, layer_idx=None, is_cross_attention=False):
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super().__init__()
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self.is_cross_attention = is_cross_attention
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attention_class = RobertaCrossAttention if is_cross_attention else RobertaSelfAttention
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self.self = attention_class(config, is_causal=is_causal, layer_idx=layer_idx)
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self.output = RobertaSelfOutput(config)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: torch.FloatTensor | None = None,
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encoder_hidden_states: torch.FloatTensor | None = None,
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encoder_attention_mask: torch.FloatTensor | None = None,
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past_key_values: Cache | None = None,
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cache_position: torch.Tensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor]:
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attention_mask = attention_mask if not self.is_cross_attention else encoder_attention_mask
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attention_output, attn_weights = self.self(
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hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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attention_mask=attention_mask,
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past_key_values=past_key_values,
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cache_position=cache_position,
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**kwargs,
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)
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attention_output = self.output(attention_output, hidden_states)
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return attention_output, attn_weights
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|
|
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class RobertaIntermediate(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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|
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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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|
|
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class RobertaOutput(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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|
|
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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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|
|
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class RobertaLayer(GradientCheckpointingLayer):
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|
def __init__(self, config, layer_idx=None):
|
|
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
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self.attention = RobertaAttention(config, is_causal=config.is_decoder, layer_idx=layer_idx)
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self.is_decoder = config.is_decoder
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self.add_cross_attention = config.add_cross_attention
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|
if self.add_cross_attention:
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|
if not self.is_decoder:
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raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
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|
self.crossattention = RobertaAttention(
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config,
|
|
is_causal=False,
|
|
layer_idx=layer_idx,
|
|
is_cross_attention=True,
|
|
)
|
|
self.intermediate = RobertaIntermediate(config)
|
|
self.output = RobertaOutput(config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: torch.FloatTensor | None = None,
|
|
encoder_hidden_states: torch.FloatTensor | None = None,
|
|
encoder_attention_mask: torch.FloatTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
cache_position: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor]:
|
|
self_attention_output, _ = self.attention(
|
|
hidden_states,
|
|
attention_mask,
|
|
past_key_values=past_key_values,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
attention_output = self_attention_output
|
|
|
|
if self.is_decoder and encoder_hidden_states is not None:
|
|
if not hasattr(self, "crossattention"):
|
|
raise ValueError(
|
|
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
|
|
" by setting `config.add_cross_attention=True`"
|
|
)
|
|
|
|
cross_attention_output, _ = self.crossattention(
|
|
self_attention_output,
|
|
None, # attention_mask
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
past_key_values=past_key_values,
|
|
**kwargs,
|
|
)
|
|
attention_output = cross_attention_output
|
|
|
|
layer_output = apply_chunking_to_forward(
|
|
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
|
|
)
|
|
return layer_output
|
|
|
|
def feed_forward_chunk(self, attention_output):
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
return layer_output
|
|
|
|
|
|
@auto_docstring
|
|
class RobertaPreTrainedModel(PreTrainedModel):
|
|
config_class = RobertaConfig
|
|
base_model_prefix = "roberta"
|
|
supports_gradient_checkpointing = True
|
|
_supports_flash_attn = True
|
|
_supports_sdpa = True
|
|
_supports_flex_attn = True
|
|
_supports_attention_backend = True
|
|
_can_record_outputs = {
|
|
"hidden_states": RobertaLayer,
|
|
"attentions": RobertaSelfAttention,
|
|
"cross_attentions": RobertaCrossAttention,
|
|
}
|
|
|
|
@torch.no_grad()
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
super()._init_weights(module)
|
|
if isinstance(module, RobertaLMHead):
|
|
init.zeros_(module.bias)
|
|
elif isinstance(module, RobertaEmbeddings):
|
|
init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))
|
|
init.zeros_(module.token_type_ids)
|
|
|
|
|
|
class RobertaEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([RobertaLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)])
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: torch.FloatTensor | None = None,
|
|
encoder_hidden_states: torch.FloatTensor | None = None,
|
|
encoder_attention_mask: torch.FloatTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
use_cache: bool | None = None,
|
|
cache_position: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor] | BaseModelOutputWithPastAndCrossAttentions:
|
|
for i, layer_module in enumerate(self.layer):
|
|
hidden_states = layer_module(
|
|
hidden_states,
|
|
attention_mask,
|
|
encoder_hidden_states, # as a positional argument for gradient checkpointing
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
past_key_values=past_key_values,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values if use_cache else None,
|
|
)
|
|
|
|
|
|
class RobertaPooler(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
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
|
|
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
|
|
all you need](https://huggingface.co/papers/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
|
|
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
|
|
|
|
To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
|
|
to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
|
|
`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
|
|
"""
|
|
)
|
|
class RobertaModel(RobertaPreTrainedModel):
|
|
_no_split_modules = ["RobertaEmbeddings", "RobertaLayer"]
|
|
|
|
def __init__(self, config, add_pooling_layer=True):
|
|
r"""
|
|
add_pooling_layer (bool, *optional*, defaults to `True`):
|
|
Whether to add a pooling layer
|
|
"""
|
|
super().__init__(config)
|
|
self.config = config
|
|
self.gradient_checkpointing = False
|
|
|
|
self.embeddings = RobertaEmbeddings(config)
|
|
self.encoder = RobertaEncoder(config)
|
|
|
|
self.pooler = RobertaPooler(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
|
|
|
|
@check_model_inputs
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
token_type_ids: torch.Tensor | None = None,
|
|
position_ids: torch.Tensor | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
encoder_hidden_states: torch.Tensor | None = None,
|
|
encoder_attention_mask: torch.Tensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
use_cache: bool | None = None,
|
|
cache_position: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor] | BaseModelOutputWithPoolingAndCrossAttentions:
|
|
if self.config.is_decoder:
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
else:
|
|
use_cache = False
|
|
|
|
if use_cache and past_key_values is None:
|
|
past_key_values = (
|
|
EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config))
|
|
if encoder_hidden_states is not None or self.config.is_encoder_decoder
|
|
else DynamicCache(config=self.config)
|
|
)
|
|
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if input_ids is not None:
|
|
device = input_ids.device
|
|
seq_length = input_ids.shape[1]
|
|
else:
|
|
device = inputs_embeds.device
|
|
seq_length = inputs_embeds.shape[1]
|
|
|
|
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
if cache_position is None:
|
|
cache_position = torch.arange(past_key_values_length, past_key_values_length + seq_length, device=device)
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
past_key_values_length=past_key_values_length,
|
|
)
|
|
|
|
attention_mask, encoder_attention_mask = self._create_attention_masks(
|
|
attention_mask=attention_mask,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
embedding_output=embedding_output,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cache_position=cache_position,
|
|
past_key_values=past_key_values,
|
|
)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
attention_mask=attention_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
past_key_values=past_key_values,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_ids=position_ids,
|
|
**kwargs,
|
|
)
|
|
sequence_output = encoder_outputs.last_hidden_state
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
return BaseModelOutputWithPoolingAndCrossAttentions(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
past_key_values=encoder_outputs.past_key_values,
|
|
)
|
|
|
|
def _create_attention_masks(
|
|
self,
|
|
attention_mask,
|
|
encoder_attention_mask,
|
|
embedding_output,
|
|
encoder_hidden_states,
|
|
cache_position,
|
|
past_key_values,
|
|
):
|
|
if self.config.is_decoder:
|
|
attention_mask = create_causal_mask(
|
|
config=self.config,
|
|
input_embeds=embedding_output,
|
|
attention_mask=attention_mask,
|
|
cache_position=cache_position,
|
|
past_key_values=past_key_values,
|
|
)
|
|
else:
|
|
attention_mask = create_bidirectional_mask(
|
|
config=self.config,
|
|
input_embeds=embedding_output,
|
|
attention_mask=attention_mask,
|
|
)
|
|
|
|
if encoder_attention_mask is not None:
|
|
encoder_attention_mask = create_bidirectional_mask(
|
|
config=self.config,
|
|
input_embeds=embedding_output,
|
|
attention_mask=encoder_attention_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
)
|
|
|
|
return attention_mask, encoder_attention_mask
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
RoBERTa Model with a `language modeling` head on top for CLM fine-tuning.
|
|
"""
|
|
)
|
|
class RobertaForCausalLM(RobertaPreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = {
|
|
"lm_head.decoder.weight": "roberta.embeddings.word_embeddings.weight",
|
|
"lm_head.decoder.bias": "lm_head.bias",
|
|
}
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
if not config.is_decoder:
|
|
logger.warning("If you want to use `RobertaLMHeadModel` as a standalone, add `is_decoder=True.`")
|
|
|
|
self.roberta = RobertaModel(config, add_pooling_layer=False)
|
|
self.lm_head = RobertaLMHead(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head.decoder = new_embeddings
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.FloatTensor | None = None,
|
|
token_type_ids: torch.LongTensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
encoder_hidden_states: torch.FloatTensor | None = None,
|
|
encoder_attention_mask: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
past_key_values: tuple[tuple[torch.FloatTensor]] | None = None,
|
|
use_cache: bool | None = None,
|
|
cache_position: torch.Tensor | None = None,
|
|
logits_to_keep: int | torch.Tensor = 0,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor] | CausalLMOutputWithCrossAttentions:
|
|
r"""
|
|
token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value
|
|
>= 2. All the value in this tensor should be always < type_vocab_size.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
|
|
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
|
|
ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, RobertaForCausalLM, AutoConfig
|
|
>>> import torch
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base")
|
|
>>> config = AutoConfig.from_pretrained("FacebookAI/roberta-base")
|
|
>>> config.is_decoder = True
|
|
>>> model = RobertaForCausalLM.from_pretrained("FacebookAI/roberta-base", config=config)
|
|
|
|
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
|
|
>>> outputs = model(**inputs)
|
|
|
|
>>> prediction_logits = outputs.logits
|
|
```"""
|
|
if labels is not None:
|
|
use_cache = False
|
|
|
|
outputs: BaseModelOutputWithPoolingAndCrossAttentions = self.roberta(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
past_key_values=past_key_values,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = outputs.last_hidden_state
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
|
|
|
|
return CausalLMOutputWithCrossAttentions(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
cross_attentions=outputs.cross_attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class RobertaForMaskedLM(RobertaPreTrainedModel):
|
|
_tied_weights_keys = {
|
|
"lm_head.decoder.weight": "roberta.embeddings.word_embeddings.weight",
|
|
"lm_head.decoder.bias": "lm_head.bias",
|
|
}
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
if config.is_decoder:
|
|
logger.warning(
|
|
"If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for "
|
|
"bi-directional self-attention."
|
|
)
|
|
|
|
self.roberta = RobertaModel(config, add_pooling_layer=False)
|
|
self.lm_head = RobertaLMHead(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head.decoder = new_embeddings
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.FloatTensor | None = None,
|
|
token_type_ids: torch.LongTensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
encoder_hidden_states: torch.FloatTensor | None = None,
|
|
encoder_attention_mask: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor] | MaskedLMOutput:
|
|
r"""
|
|
token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value
|
|
>= 2. All the value in this tensor should be always < type_vocab_size.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
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]`
|
|
"""
|
|
outputs = self.roberta(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.lm_head(sequence_output)
|
|
|
|
masked_lm_loss = None
|
|
if labels is not None:
|
|
# move labels to correct device
|
|
labels = labels.to(prediction_scores.device)
|
|
loss_fct = CrossEntropyLoss()
|
|
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
|
|
|
|
return MaskedLMOutput(
|
|
loss=masked_lm_loss,
|
|
logits=prediction_scores,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class RobertaLMHead(nn.Module):
|
|
"""Roberta Head for masked language modeling."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
|
|
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
|
|
|
|
def forward(self, features, **kwargs):
|
|
x = self.dense(features)
|
|
x = gelu(x)
|
|
x = self.layer_norm(x)
|
|
|
|
# project back to size of vocabulary with bias
|
|
x = self.decoder(x)
|
|
|
|
return x
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
|
|
pooled output) e.g. for GLUE tasks.
|
|
"""
|
|
)
|
|
class RobertaForSequenceClassification(RobertaPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.config = config
|
|
|
|
self.roberta = RobertaModel(config, add_pooling_layer=False)
|
|
self.classifier = RobertaClassificationHead(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.FloatTensor | None = None,
|
|
token_type_ids: torch.LongTensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor] | SequenceClassifierOutput:
|
|
r"""
|
|
token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value
|
|
>= 2. All the value in this tensor should be always < type_vocab_size.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
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).
|
|
"""
|
|
outputs = self.roberta(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
sequence_output = outputs[0]
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device
|
|
labels = labels.to(logits.device)
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class RobertaForMultipleChoice(RobertaPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.roberta = RobertaModel(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()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
token_type_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor] | MultipleChoiceModelOutput:
|
|
r"""
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value
|
|
>= 2. All the value in this tensor should be always < type_vocab_size.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
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)
|
|
position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
|
model's internal embedding lookup matrix.
|
|
"""
|
|
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
|
|
|
|
flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
|
|
flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
|
|
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
|
|
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
|
|
flat_inputs_embeds = (
|
|
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
|
|
if inputs_embeds is not None
|
|
else None
|
|
)
|
|
|
|
outputs = self.roberta(
|
|
flat_input_ids,
|
|
position_ids=flat_position_ids,
|
|
token_type_ids=flat_token_type_ids,
|
|
attention_mask=flat_attention_mask,
|
|
inputs_embeds=flat_inputs_embeds,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
pooled_output = outputs[1]
|
|
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
reshaped_logits = logits.view(-1, num_choices)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device
|
|
labels = labels.to(reshaped_logits.device)
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(reshaped_logits, labels)
|
|
|
|
return MultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class RobertaForTokenClassification(RobertaPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.roberta = RobertaModel(config, add_pooling_layer=False)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.FloatTensor | None = None,
|
|
token_type_ids: torch.LongTensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor] | TokenClassifierOutput:
|
|
r"""
|
|
token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value
|
|
>= 2. All the value in this tensor should be always < type_vocab_size.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
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]`.
|
|
"""
|
|
outputs = self.roberta(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device
|
|
labels = labels.to(logits.device)
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class RobertaClassificationHead(nn.Module):
|
|
"""Head for sentence-level classification tasks."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
def forward(self, features, **kwargs):
|
|
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
|
|
x = self.dropout(x)
|
|
x = self.dense(x)
|
|
x = torch.tanh(x)
|
|
x = self.dropout(x)
|
|
x = self.out_proj(x)
|
|
return x
|
|
|
|
|
|
@auto_docstring
|
|
class RobertaForQuestionAnswering(RobertaPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.roberta = RobertaModel(config, add_pooling_layer=False)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.FloatTensor | None = None,
|
|
token_type_ids: torch.LongTensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
start_positions: torch.LongTensor | None = None,
|
|
end_positions: torch.LongTensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor] | QuestionAnsweringModelOutput:
|
|
r"""
|
|
token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value
|
|
>= 2. All the value in this tensor should be always < type_vocab_size.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
"""
|
|
outputs = self.roberta(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
|
|
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
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"RobertaForCausalLM",
|
|
"RobertaForMaskedLM",
|
|
"RobertaForMultipleChoice",
|
|
"RobertaForQuestionAnswering",
|
|
"RobertaForSequenceClassification",
|
|
"RobertaForTokenClassification",
|
|
"RobertaModel",
|
|
"RobertaPreTrainedModel",
|
|
]
|