898 lines
39 KiB
Python
898 lines
39 KiB
Python
# coding=utf-8
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# Copyright 2021 Tel AViv University, AllenAI 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 Splinter model."""
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from dataclasses import dataclass
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from typing import Callable, Optional, 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 CrossEntropyLoss
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from ...activations import ACT2FN
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutput, ModelOutput, QuestionAnsweringModelOutput
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import (
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auto_docstring,
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can_return_tuple,
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logging,
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)
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from ...utils.deprecation import deprecate_kwarg
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from .configuration_splinter import SplinterConfig
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logger = logging.get_logger(__name__)
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class SplinterEmbeddings(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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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.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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token_type_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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) -> tuple:
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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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if token_type_ids is None:
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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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if self.position_embedding_type == "absolute":
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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.dropout(embeddings)
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return embeddings
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# Copied from transformers.models.align.modeling_align.eager_attention_forward
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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: Optional[torch.Tensor],
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scaling: float,
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dropout: float = 0.0,
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head_mask: Optional[torch.Tensor] = None,
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**kwargs,
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):
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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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causal_mask = attention_mask[:, :, :, : key.shape[-2]]
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attn_weights = attn_weights + causal_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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if head_mask is not None:
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attn_weights = attn_weights * head_mask.view(1, -1, 1, 1)
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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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# Copied from transformers.models.align.modeling_align.AlignTextSelfAttention with AlignText->Splinter
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class SplinterSelfAttention(nn.Module):
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def __init__(self, config):
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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.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.attention_dropout = config.attention_probs_dropout_prob
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self.scaling = self.attention_head_size**-0.5
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@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
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@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
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@deprecate_kwarg("past_key_value", version="4.54.0")
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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: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
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output_attentions: Optional[bool] = False,
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**kwargs,
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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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query_states = self.query(hidden_states).view(hidden_shape).transpose(1, 2)
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key_states = self.key(hidden_states).view(hidden_shape).transpose(1, 2)
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value_states = self.value(hidden_states).view(hidden_shape).transpose(1, 2)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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head_mask=head_mask,
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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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outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
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return outputs
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# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->Splinter
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class SplinterSelfOutput(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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# Copied from transformers.models.align.modeling_align.AlignTextAttention with AlignText->Splinter
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class SplinterAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.self = SplinterSelfAttention(config)
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self.output = SplinterSelfOutput(config)
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self.pruned_heads = set()
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def prune_heads(self, heads):
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if len(heads) == 0:
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return
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heads, index = find_pruneable_heads_and_indices(
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heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
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)
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# Prune linear layers
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self.self.query = prune_linear_layer(self.self.query, index)
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self.self.key = prune_linear_layer(self.self.key, index)
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self.self.value = prune_linear_layer(self.self.value, index)
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self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
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# Update hyper params and store pruned heads
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self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
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self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
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@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
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@deprecate_kwarg("past_key_value", version="4.54.0")
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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: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
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output_attentions: Optional[bool] = False,
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**kwargs,
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) -> tuple[torch.Tensor]:
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self_outputs = self.self(
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hidden_states,
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attention_mask=attention_mask,
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head_mask=head_mask,
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output_attentions=output_attentions,
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**kwargs,
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)
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attention_output = self.output(self_outputs[0], hidden_states)
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outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
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return outputs
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# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Splinter
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class SplinterIntermediate(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->Splinter
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class SplinterOutput(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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# Copied from transformers.models.align.modeling_align.AlignTextLayer with AlignText->Splinter
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class SplinterLayer(GradientCheckpointingLayer):
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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
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self.attention = SplinterAttention(config)
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self.intermediate = SplinterIntermediate(config)
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self.output = SplinterOutput(config)
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@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
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@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
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@deprecate_kwarg("past_key_value", version="4.54.0")
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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: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
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output_attentions: Optional[bool] = False,
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**kwargs,
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) -> tuple[torch.Tensor]:
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self_attention_outputs = self.attention(
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hidden_states,
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attention_mask=attention_mask,
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head_mask=head_mask,
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output_attentions=output_attentions,
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**kwargs,
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)
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attention_output = self_attention_outputs[0]
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outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
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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, attention_output
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)
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outputs = (layer_output,) + outputs
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return outputs
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def feed_forward_chunk(self, attention_output):
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intermediate_output = self.intermediate(attention_output)
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layer_output = self.output(intermediate_output, attention_output)
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return layer_output
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# Copied from transformers.models.align.modeling_align.AlignTextEncoder with AlignText->Splinter
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class SplinterEncoder(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([SplinterLayer(config) for i in range(config.num_hidden_layers)])
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self.gradient_checkpointing = False
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@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
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@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
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@deprecate_kwarg("past_key_values", version="4.54.0")
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@deprecate_kwarg("use_cache", version="4.54.0")
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@can_return_tuple
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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: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = False,
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output_hidden_states: Optional[bool] = False,
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return_dict: Optional[bool] = True,
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**kwargs,
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) -> Union[tuple[torch.Tensor], BaseModelOutput]:
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all_hidden_states = () if output_hidden_states else None
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all_self_attentions = () if output_attentions 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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layer_head_mask = head_mask[i] if head_mask is not None else None
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layer_outputs = layer_module(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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head_mask=layer_head_mask,
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output_attentions=output_attentions,
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**kwargs,
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)
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hidden_states = layer_outputs[0]
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if output_attentions:
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all_self_attentions = all_self_attentions + (layer_outputs[1],)
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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return BaseModelOutput(
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last_hidden_state=hidden_states,
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hidden_states=all_hidden_states,
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attentions=all_self_attentions,
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)
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@auto_docstring
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class SplinterPreTrainedModel(PreTrainedModel):
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config: SplinterConfig
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base_model_prefix = "splinter"
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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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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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@auto_docstring
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class SplinterModel(SplinterPreTrainedModel):
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"""
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The model is an encoder (with only self-attention) following the architecture described in [Attention is all you
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need](https://huggingface.co/papers/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones,
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Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
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"""
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def __init__(self, config):
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super().__init__(config)
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self.config = config
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self.embeddings = SplinterEmbeddings(config)
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self.encoder = SplinterEncoder(config)
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# Initialize weights and apply final processing
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self.post_init()
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def get_input_embeddings(self):
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return self.embeddings.word_embeddings
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def set_input_embeddings(self, value):
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self.embeddings.word_embeddings = value
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def _prune_heads(self, heads_to_prune):
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"""
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Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
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class PreTrainedModel
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"""
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for layer, heads in heads_to_prune.items():
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self.encoder.layer[layer].attention.prune_heads(heads)
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|
@deprecate_kwarg("encoder_hidden_states", version="4.54.0")
|
|
@deprecate_kwarg("encoder_attention_mask", version="4.54.0")
|
|
@deprecate_kwarg("past_key_values", version="4.54.0")
|
|
@deprecate_kwarg("use_cache", version="4.54.0")
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[list[torch.FloatTensor]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, BaseModelOutput]:
|
|
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.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
position_ids (`torch.LongTensor` of shape `batch_size, sequence_length`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
|
|
input_shape = input_ids.size()
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
batch_size, seq_length = input_shape
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(((batch_size, seq_length)), device=device)
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
|
|
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
|
# ourselves in which case we just need to make it broadcastable to all heads.
|
|
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
)
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
attention_mask=extended_attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=True,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
|
|
return BaseModelOutput(
|
|
last_hidden_state=sequence_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
class SplinterFullyConnectedLayer(nn.Module):
|
|
def __init__(self, input_dim, output_dim, hidden_act="gelu"):
|
|
super().__init__()
|
|
|
|
self.input_dim = input_dim
|
|
self.output_dim = output_dim
|
|
|
|
self.dense = nn.Linear(self.input_dim, self.output_dim)
|
|
self.act_fn = ACT2FN[hidden_act]
|
|
self.LayerNorm = nn.LayerNorm(self.output_dim)
|
|
|
|
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(inputs)
|
|
hidden_states = self.act_fn(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class QuestionAwareSpanSelectionHead(nn.Module):
|
|
"""
|
|
Implementation of Question-Aware Span Selection (QASS) head, described in Splinter's paper:
|
|
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
self.query_start_transform = SplinterFullyConnectedLayer(config.hidden_size, config.hidden_size)
|
|
self.query_end_transform = SplinterFullyConnectedLayer(config.hidden_size, config.hidden_size)
|
|
self.start_transform = SplinterFullyConnectedLayer(config.hidden_size, config.hidden_size)
|
|
self.end_transform = SplinterFullyConnectedLayer(config.hidden_size, config.hidden_size)
|
|
|
|
self.start_classifier = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
|
|
self.end_classifier = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
|
|
|
|
def forward(self, inputs, positions):
|
|
_, _, dim = inputs.size()
|
|
index = positions.unsqueeze(-1).repeat(1, 1, dim) # [batch_size, num_positions, dim]
|
|
gathered_reps = torch.gather(inputs, dim=1, index=index) # [batch_size, num_positions, dim]
|
|
|
|
query_start_reps = self.query_start_transform(gathered_reps) # [batch_size, num_positions, dim]
|
|
query_end_reps = self.query_end_transform(gathered_reps) # [batch_size, num_positions, dim]
|
|
start_reps = self.start_transform(inputs) # [batch_size, seq_length, dim]
|
|
end_reps = self.end_transform(inputs) # [batch_size, seq_length, dim]
|
|
|
|
hidden_states = self.start_classifier(query_start_reps) # [batch_size, num_positions, dim]
|
|
start_reps = start_reps.permute(0, 2, 1) # [batch_size, dim, seq_length]
|
|
start_logits = torch.matmul(hidden_states, start_reps)
|
|
|
|
hidden_states = self.end_classifier(query_end_reps)
|
|
end_reps = end_reps.permute(0, 2, 1)
|
|
end_logits = torch.matmul(hidden_states, end_reps)
|
|
|
|
return start_logits, end_logits
|
|
|
|
|
|
@auto_docstring
|
|
class SplinterForQuestionAnswering(SplinterPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.splinter = SplinterModel(config)
|
|
self.splinter_qass = QuestionAwareSpanSelectionHead(config)
|
|
self.question_token_id = config.question_token_id
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
start_positions: Optional[torch.LongTensor] = None,
|
|
end_positions: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
question_positions: Optional[torch.LongTensor] = None,
|
|
) -> Union[tuple, 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.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
position_ids (`torch.LongTensor` of shape `batch_size, sequence_length`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
question_positions (`torch.LongTensor` of shape `(batch_size, num_questions)`, *optional*):
|
|
The positions of all question tokens. If given, start_logits and end_logits will be of shape `(batch_size,
|
|
num_questions, sequence_length)`. If None, the first question token in each sequence in the batch will be
|
|
the only one for which start_logits and end_logits are calculated and they will be of shape `(batch_size,
|
|
sequence_length)`.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
question_positions_were_none = False
|
|
if question_positions is None:
|
|
if input_ids is not None:
|
|
question_position_for_each_example = torch.argmax(
|
|
(torch.eq(input_ids, self.question_token_id)).int(), dim=-1
|
|
)
|
|
else:
|
|
question_position_for_each_example = torch.zeros(
|
|
inputs_embeds.size(0), dtype=torch.long, layout=inputs_embeds.layout, device=inputs_embeds.device
|
|
)
|
|
question_positions = question_position_for_each_example.unsqueeze(-1)
|
|
question_positions_were_none = True
|
|
|
|
outputs = self.splinter(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
start_logits, end_logits = self.splinter_qass(sequence_output, question_positions)
|
|
|
|
if question_positions_were_none:
|
|
start_logits, end_logits = start_logits.squeeze(1), end_logits.squeeze(1)
|
|
|
|
if attention_mask is not None:
|
|
start_logits = start_logits + (1 - attention_mask) * torch.finfo(start_logits.dtype).min
|
|
end_logits = end_logits + (1 - attention_mask) * torch.finfo(end_logits.dtype).min
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if len(start_positions.size()) > 1:
|
|
start_positions = start_positions.squeeze(-1)
|
|
if len(end_positions.size()) > 1:
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.size(1)
|
|
start_positions.clamp_(0, ignored_index)
|
|
end_positions.clamp_(0, ignored_index)
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
if not return_dict:
|
|
output = (start_logits, end_logits) + outputs[1:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@dataclass
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Class for outputs of Splinter as a span selection model.
|
|
"""
|
|
)
|
|
class SplinterForPreTrainingOutput(ModelOutput):
|
|
r"""
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when start and end positions are provided):
|
|
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
|
|
start_logits (`torch.FloatTensor` of shape `(batch_size, num_questions, sequence_length)`):
|
|
Span-start scores (before SoftMax).
|
|
end_logits (`torch.FloatTensor` of shape `(batch_size, num_questions, sequence_length)`):
|
|
Span-end scores (before SoftMax).
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
start_logits: Optional[torch.FloatTensor] = None
|
|
end_logits: Optional[torch.FloatTensor] = None
|
|
hidden_states: Optional[tuple[torch.FloatTensor]] = None
|
|
attentions: Optional[tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Splinter Model for the recurring span selection task as done during the pretraining. The difference to the QA task
|
|
is that we do not have a question, but multiple question tokens that replace the occurrences of recurring spans
|
|
instead.
|
|
"""
|
|
)
|
|
class SplinterForPreTraining(SplinterPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.splinter = SplinterModel(config)
|
|
self.splinter_qass = QuestionAwareSpanSelectionHead(config)
|
|
self.question_token_id = config.question_token_id
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
start_positions: Optional[torch.LongTensor] = None,
|
|
end_positions: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
question_positions: Optional[torch.LongTensor] = None,
|
|
) -> Union[tuple, SplinterForPreTrainingOutput]:
|
|
r"""
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, num_questions, 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_questions, sequence_length`, *optional*):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
|
|
1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
position_ids (`torch.LongTensor` of shape `batch_size, num_questions, 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_questions, 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.
|
|
start_positions (`torch.LongTensor` of shape `(batch_size, num_questions)`, *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, num_questions)`, *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.
|
|
question_positions (`torch.LongTensor` of shape `(batch_size, num_questions)`, *optional*):
|
|
The positions of all question tokens. If given, start_logits and end_logits will be of shape `(batch_size,
|
|
num_questions, sequence_length)`. If None, the first question token in each sequence in the batch will be
|
|
the only one for which start_logits and end_logits are calculated and they will be of shape `(batch_size,
|
|
sequence_length)`.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if question_positions is None and start_positions is not None and end_positions is not None:
|
|
raise TypeError("question_positions must be specified in order to calculate the loss")
|
|
|
|
elif question_positions is None and input_ids is None:
|
|
raise TypeError("question_positions must be specified when input_embeds is used")
|
|
|
|
elif question_positions is None:
|
|
question_positions = self._prepare_question_positions(input_ids)
|
|
|
|
outputs = self.splinter(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
batch_size, sequence_length, dim = sequence_output.size()
|
|
# [batch_size, num_questions, sequence_length]
|
|
start_logits, end_logits = self.splinter_qass(sequence_output, question_positions)
|
|
|
|
num_questions = question_positions.size(1)
|
|
if attention_mask is not None:
|
|
attention_mask_for_each_question = attention_mask.unsqueeze(1).expand(
|
|
batch_size, num_questions, sequence_length
|
|
)
|
|
start_logits = start_logits + (1 - attention_mask_for_each_question) * torch.finfo(start_logits.dtype).min
|
|
end_logits = end_logits + (1 - attention_mask_for_each_question) * torch.finfo(end_logits.dtype).min
|
|
|
|
total_loss = None
|
|
# [batch_size, num_questions, sequence_length]
|
|
if start_positions is not None and end_positions is not None:
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
start_positions.clamp_(0, max(0, sequence_length - 1))
|
|
end_positions.clamp_(0, max(0, sequence_length - 1))
|
|
|
|
# Ignore zero positions in the loss. Splinter never predicts zero
|
|
# during pretraining and zero is used for padding question
|
|
# tokens as well as for start and end positions of padded
|
|
# question tokens.
|
|
loss_fct = CrossEntropyLoss(ignore_index=self.config.pad_token_id)
|
|
start_loss = loss_fct(
|
|
start_logits.view(batch_size * num_questions, sequence_length),
|
|
start_positions.view(batch_size * num_questions),
|
|
)
|
|
end_loss = loss_fct(
|
|
end_logits.view(batch_size * num_questions, sequence_length),
|
|
end_positions.view(batch_size * num_questions),
|
|
)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
if not return_dict:
|
|
output = (start_logits, end_logits) + outputs[1:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return SplinterForPreTrainingOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
def _prepare_question_positions(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
rows, flat_positions = torch.where(input_ids == self.config.question_token_id)
|
|
num_questions = torch.bincount(rows)
|
|
positions = torch.full(
|
|
(input_ids.size(0), num_questions.max()),
|
|
self.config.pad_token_id,
|
|
dtype=torch.long,
|
|
device=input_ids.device,
|
|
)
|
|
cols = torch.cat([torch.arange(n) for n in num_questions])
|
|
positions[rows, cols] = flat_positions
|
|
return positions
|
|
|
|
|
|
__all__ = [
|
|
"SplinterForQuestionAnswering",
|
|
"SplinterForPreTraining",
|
|
"SplinterLayer",
|
|
"SplinterModel",
|
|
"SplinterPreTrainedModel",
|
|
]
|