mirror of
https://github.com/huggingface/transformers.git
synced 2025-08-02 03:01:07 +06:00
e435574721
* deprecate for 1 version * style * fix some tests * fix esm * skip for now, GC requires positional args but we have keyword args * remove transpose for scores in modified models only * skip fx trace tests
1150 lines
49 KiB
Python
Executable File
1150 lines
49 KiB
Python
Executable File
# coding=utf-8
|
|
# Copyright 2023-present NAVER Corp, The Microsoft Research Asia LayoutLM Team Authors and the HuggingFace Inc. team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""PyTorch Bros model."""
|
|
|
|
import math
|
|
from dataclasses import dataclass
|
|
from typing import Optional, Union
|
|
|
|
import torch
|
|
import torch.utils.checkpoint
|
|
from torch import nn
|
|
from torch.nn import CrossEntropyLoss
|
|
|
|
from ...activations import ACT2FN
|
|
from ...modeling_layers import GradientCheckpointingLayer
|
|
from ...modeling_outputs import (
|
|
BaseModelOutputWithCrossAttentions,
|
|
BaseModelOutputWithPoolingAndCrossAttentions,
|
|
TokenClassifierOutput,
|
|
)
|
|
from ...modeling_utils import PreTrainedModel
|
|
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
|
|
from ...utils import ModelOutput, auto_docstring, can_return_tuple, logging
|
|
from ...utils.deprecation import deprecate_kwarg
|
|
from .configuration_bros import BrosConfig
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
@dataclass
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Base class for outputs of token classification models.
|
|
"""
|
|
)
|
|
class BrosSpadeOutput(ModelOutput):
|
|
r"""
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Classification loss.
|
|
initial_token_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
|
|
Classification scores for entity initial tokens (before SoftMax).
|
|
subsequent_token_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, sequence_length+1)`):
|
|
Classification scores for entity sequence tokens (before SoftMax).
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
initial_token_logits: Optional[torch.FloatTensor] = None
|
|
subsequent_token_logits: Optional[torch.FloatTensor] = None
|
|
hidden_states: Optional[tuple[torch.FloatTensor]] = None
|
|
attentions: Optional[tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
class BrosPositionalEmbedding1D(nn.Module):
|
|
# Reference: https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/mem_transformer.py#L15
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
self.dim_bbox_sinusoid_emb_1d = config.dim_bbox_sinusoid_emb_1d
|
|
|
|
inv_freq = 1 / (
|
|
10000 ** (torch.arange(0.0, self.dim_bbox_sinusoid_emb_1d, 2.0) / self.dim_bbox_sinusoid_emb_1d)
|
|
)
|
|
self.register_buffer("inv_freq", inv_freq)
|
|
|
|
def forward(self, pos_seq: torch.Tensor) -> torch.Tensor:
|
|
seq_size = pos_seq.size()
|
|
b1, b2, b3 = seq_size
|
|
sinusoid_inp = pos_seq.view(b1, b2, b3, 1) * self.inv_freq.view(1, 1, 1, self.dim_bbox_sinusoid_emb_1d // 2)
|
|
pos_emb = torch.cat([sinusoid_inp.sin(), sinusoid_inp.cos()], dim=-1)
|
|
return pos_emb
|
|
|
|
|
|
class BrosPositionalEmbedding2D(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
self.dim_bbox = config.dim_bbox
|
|
self.x_pos_emb = BrosPositionalEmbedding1D(config)
|
|
self.y_pos_emb = BrosPositionalEmbedding1D(config)
|
|
|
|
def forward(self, bbox: torch.Tensor) -> torch.Tensor:
|
|
stack = []
|
|
for i in range(self.dim_bbox):
|
|
if i % 2 == 0:
|
|
stack.append(self.x_pos_emb(bbox[..., i]))
|
|
else:
|
|
stack.append(self.y_pos_emb(bbox[..., i]))
|
|
bbox_pos_emb = torch.cat(stack, dim=-1)
|
|
return bbox_pos_emb
|
|
|
|
|
|
class BrosBboxEmbeddings(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.bbox_sinusoid_emb = BrosPositionalEmbedding2D(config)
|
|
self.bbox_projection = nn.Linear(config.dim_bbox_sinusoid_emb_2d, config.dim_bbox_projection, bias=False)
|
|
|
|
def forward(self, bbox: torch.Tensor):
|
|
bbox_t = bbox.transpose(0, 1)
|
|
bbox_pos = bbox_t[None, :, :, :] - bbox_t[:, None, :, :]
|
|
bbox_pos_emb = self.bbox_sinusoid_emb(bbox_pos)
|
|
bbox_pos_emb = self.bbox_projection(bbox_pos_emb)
|
|
|
|
return bbox_pos_emb
|
|
|
|
|
|
class BrosTextEmbeddings(nn.Module):
|
|
"""Construct the embeddings from word, position and token_type embeddings."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
|
|
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
|
|
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
|
|
|
|
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
|
|
# any TensorFlow checkpoint file
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
|
|
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
|
|
self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
|
|
self.register_buffer(
|
|
"token_type_ids",
|
|
torch.zeros(
|
|
self.position_ids.size(),
|
|
dtype=torch.long,
|
|
device=self.position_ids.device,
|
|
),
|
|
persistent=False,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
if input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
else:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
|
|
seq_length = input_shape[1]
|
|
|
|
if position_ids is None:
|
|
position_ids = self.position_ids[:, :seq_length]
|
|
|
|
if token_type_ids is None:
|
|
if hasattr(self, "token_type_ids"):
|
|
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
|
|
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
|
|
token_type_ids = buffered_token_type_ids_expanded
|
|
else:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.word_embeddings(input_ids)
|
|
token_type_embeddings = self.token_type_embeddings(token_type_ids)
|
|
|
|
embeddings = inputs_embeds + token_type_embeddings
|
|
if self.position_embedding_type == "absolute":
|
|
position_embeddings = self.position_embeddings(position_ids)
|
|
embeddings += position_embeddings
|
|
embeddings = self.LayerNorm(embeddings)
|
|
embeddings = self.dropout(embeddings)
|
|
return embeddings
|
|
|
|
|
|
class BrosSelfAttention(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
|
|
raise ValueError(
|
|
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
|
|
f"heads ({config.num_attention_heads})"
|
|
)
|
|
|
|
self.num_attention_heads = config.num_attention_heads
|
|
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
|
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
|
|
|
self.query = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.key = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.value = nn.Linear(config.hidden_size, self.all_head_size)
|
|
|
|
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
|
|
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
|
|
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
|
|
self.max_position_embeddings = config.max_position_embeddings
|
|
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
|
|
|
|
self.is_decoder = config.is_decoder
|
|
|
|
@deprecate_kwarg("past_key_value", version="4.54.0")
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
bbox_pos_emb: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[torch.Tensor] = False,
|
|
) -> tuple[torch.Tensor]:
|
|
hidden_shape = (hidden_states.shape[0], -1, self.num_attention_heads, self.attention_head_size)
|
|
query_layer = self.query(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
|
|
# If this is instantiated as a cross-attention module, the keys
|
|
# and values come from an encoder; the attention mask needs to be
|
|
# such that the encoder's padding tokens are not attended to.
|
|
is_cross_attention = encoder_hidden_states is not None
|
|
|
|
if is_cross_attention:
|
|
key_layer = self.key(encoder_hidden_states).view(hidden_shape).transpose(1, 2)
|
|
value_layer = self.value(encoder_hidden_states).view(hidden_shape).transpose(1, 2)
|
|
attention_mask = encoder_attention_mask
|
|
else:
|
|
key_layer = self.key(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
value_layer = self.value(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
|
|
# Take the dot product between "query" and "key" to get the raw attention scores.
|
|
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
|
|
|
|
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
|
|
seq_length = hidden_states.size()[1]
|
|
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
|
|
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
|
|
distance = position_ids_l - position_ids_r
|
|
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
|
|
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
|
|
|
|
if self.position_embedding_type == "relative_key":
|
|
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
|
|
attention_scores = attention_scores + relative_position_scores
|
|
elif self.position_embedding_type == "relative_key_query":
|
|
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
|
|
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
|
|
|
|
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
|
|
|
|
# bbox positional encoding
|
|
batch_size, n_head, seq_length, d_head = query_layer.shape
|
|
bbox_pos_emb = bbox_pos_emb.view(seq_length, seq_length, batch_size, d_head)
|
|
bbox_pos_emb = bbox_pos_emb.permute([2, 0, 1, 3])
|
|
bbox_pos_scores = torch.einsum("bnid,bijd->bnij", (query_layer, bbox_pos_emb))
|
|
|
|
attention_scores = attention_scores + bbox_pos_scores
|
|
|
|
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
|
|
if attention_mask is not None:
|
|
# Apply the attention mask is (precomputed for all layers in BrosModel forward() function)
|
|
attention_scores = attention_scores + attention_mask
|
|
|
|
# Normalize the attention scores to probabilities.
|
|
attention_probs = nn.Softmax(dim=-1)(attention_scores)
|
|
|
|
# This is actually dropping out entire tokens to attend to, which might
|
|
# seem a bit unusual, but is taken from the original Transformer paper.
|
|
attention_probs = self.dropout(attention_probs)
|
|
|
|
# Mask heads if we want to
|
|
if head_mask is not None:
|
|
attention_probs = attention_probs * head_mask
|
|
|
|
context_layer = torch.matmul(attention_probs, value_layer)
|
|
|
|
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
|
|
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
|
|
context_layer = context_layer.view(*new_context_layer_shape)
|
|
|
|
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
|
|
|
|
if self.is_decoder:
|
|
outputs = outputs + (None,)
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->Bros
|
|
class BrosSelfOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
class BrosAttention(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.self = BrosSelfAttention(config)
|
|
self.output = BrosSelfOutput(config)
|
|
self.pruned_heads = set()
|
|
|
|
def prune_heads(self, heads):
|
|
if len(heads) == 0:
|
|
return
|
|
heads, index = find_pruneable_heads_and_indices(
|
|
heads,
|
|
self.self.num_attention_heads,
|
|
self.self.attention_head_size,
|
|
self.pruned_heads,
|
|
)
|
|
|
|
# Prune linear layers
|
|
self.self.query = prune_linear_layer(self.self.query, index)
|
|
self.self.key = prune_linear_layer(self.self.key, index)
|
|
self.self.value = prune_linear_layer(self.self.value, index)
|
|
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
|
|
|
|
# Update hyper params and store pruned heads
|
|
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
|
|
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
|
|
self.pruned_heads = self.pruned_heads.union(heads)
|
|
|
|
@deprecate_kwarg("past_key_value", version="4.54.0")
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
bbox_pos_emb: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> tuple[torch.Tensor]:
|
|
self_outputs = self.self(
|
|
hidden_states=hidden_states,
|
|
bbox_pos_emb=bbox_pos_emb,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
attention_output = self.output(self_outputs[0], hidden_states)
|
|
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Bros
|
|
class BrosIntermediate(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
|
|
if isinstance(config.hidden_act, str):
|
|
self.intermediate_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.intermediate_act_fn = config.hidden_act
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class BrosOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
class BrosLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
|
self.seq_len_dim = 1
|
|
self.attention = BrosAttention(config)
|
|
self.is_decoder = config.is_decoder
|
|
self.add_cross_attention = config.add_cross_attention
|
|
if self.add_cross_attention:
|
|
if not self.is_decoder:
|
|
raise Exception(f"{self} should be used as a decoder model if cross attention is added")
|
|
self.crossattention = BrosAttention(config)
|
|
self.intermediate = BrosIntermediate(config)
|
|
self.output = BrosOutput(config)
|
|
|
|
@deprecate_kwarg("past_key_value", version="4.54.0")
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
bbox_pos_emb: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> tuple[torch.Tensor]:
|
|
self_attention_outputs = self.attention(
|
|
hidden_states,
|
|
bbox_pos_emb=bbox_pos_emb,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
|
|
# if decoder, the last output is tuple of self-attn cache
|
|
if self.is_decoder:
|
|
outputs = self_attention_outputs[1:-1]
|
|
else:
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
|
|
|
if self.is_decoder and encoder_hidden_states is not None:
|
|
if hasattr(self, "crossattention"):
|
|
raise Exception(
|
|
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_outputs = self.crossattention(
|
|
attention_output,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
attention_output = cross_attention_outputs[0]
|
|
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
|
|
|
|
layer_output = apply_chunking_to_forward(
|
|
self.feed_forward_chunk,
|
|
self.chunk_size_feed_forward,
|
|
self.seq_len_dim,
|
|
attention_output,
|
|
)
|
|
outputs = (layer_output,) + outputs
|
|
|
|
# if decoder, return the attn key/values as the last output
|
|
if self.is_decoder:
|
|
outputs = outputs + (None,)
|
|
|
|
return outputs
|
|
|
|
def feed_forward_chunk(self, attention_output):
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
return layer_output
|
|
|
|
|
|
class BrosEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([BrosLayer(config) for _ in range(config.num_hidden_layers)])
|
|
|
|
@deprecate_kwarg("past_key_values", version="4.54.0")
|
|
@deprecate_kwarg("use_cache", version="4.54.0")
|
|
@can_return_tuple
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
bbox_pos_emb: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
output_hidden_states: Optional[bool] = False,
|
|
return_dict: Optional[bool] = True,
|
|
) -> Union[tuple[torch.Tensor], BaseModelOutputWithCrossAttentions]:
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
|
|
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
|
|
|
layer_outputs = layer_module(
|
|
hidden_states=hidden_states,
|
|
bbox_pos_emb=bbox_pos_emb,
|
|
attention_mask=attention_mask,
|
|
head_mask=layer_head_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
|
if self.config.add_cross_attention:
|
|
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
return BaseModelOutputWithCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->Bros
|
|
class BrosPooler(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
|
|
|
|
|
|
class BrosRelationExtractor(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.n_relations = config.n_relations
|
|
self.backbone_hidden_size = config.hidden_size
|
|
self.head_hidden_size = config.hidden_size
|
|
self.classifier_dropout_prob = config.classifier_dropout_prob
|
|
|
|
self.drop = nn.Dropout(self.classifier_dropout_prob)
|
|
self.query = nn.Linear(self.backbone_hidden_size, self.n_relations * self.head_hidden_size)
|
|
|
|
self.key = nn.Linear(self.backbone_hidden_size, self.n_relations * self.head_hidden_size)
|
|
|
|
self.dummy_node = nn.Parameter(torch.zeros(1, self.backbone_hidden_size))
|
|
|
|
def forward(self, query_layer: torch.Tensor, key_layer: torch.Tensor):
|
|
query_layer = self.query(self.drop(query_layer))
|
|
|
|
dummy_vec = self.dummy_node.unsqueeze(0).repeat(1, key_layer.size(1), 1)
|
|
key_layer = torch.cat([key_layer, dummy_vec], axis=0)
|
|
key_layer = self.key(self.drop(key_layer))
|
|
|
|
query_layer = query_layer.view(
|
|
query_layer.size(0), query_layer.size(1), self.n_relations, self.head_hidden_size
|
|
)
|
|
key_layer = key_layer.view(key_layer.size(0), key_layer.size(1), self.n_relations, self.head_hidden_size)
|
|
|
|
relation_score = torch.matmul(
|
|
query_layer.permute(2, 1, 0, 3), key_layer.permute(2, 1, 3, 0)
|
|
) # equivalent to torch.einsum("ibnd,jbnd->nbij", (query_layer, key_layer))
|
|
|
|
return relation_score
|
|
|
|
|
|
@auto_docstring
|
|
class BrosPreTrainedModel(PreTrainedModel):
|
|
config_class = BrosConfig
|
|
base_model_prefix = "bros"
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, nn.Linear):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
@auto_docstring
|
|
class BrosModel(BrosPreTrainedModel):
|
|
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.embeddings = BrosTextEmbeddings(config)
|
|
self.bbox_embeddings = BrosBboxEmbeddings(config)
|
|
self.encoder = BrosEncoder(config)
|
|
|
|
self.pooler = BrosPooler(config) if add_pooling_layer else None
|
|
|
|
self.init_weights()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
def _prune_heads(self, heads_to_prune):
|
|
"""
|
|
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
|
|
class PreTrainedModel
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
|
|
|
@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,
|
|
bbox: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[list[torch.FloatTensor]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
|
|
r"""
|
|
bbox ('torch.FloatTensor' of shape '(batch_size, num_boxes, 4)'):
|
|
Bounding box coordinates for each token in the input sequence. Each bounding box is a list of four values
|
|
(x1, y1, x2, y2), where (x1, y1) is the top left corner, and (x2, y2) is the bottom right corner of the
|
|
bounding box.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> import torch
|
|
>>> from transformers import BrosProcessor, BrosModel
|
|
|
|
>>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")
|
|
|
|
>>> model = BrosModel.from_pretrained("jinho8345/bros-base-uncased")
|
|
|
|
>>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
|
|
>>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)
|
|
>>> encoding["bbox"] = bbox
|
|
|
|
>>> outputs = model(**encoding)
|
|
>>> last_hidden_states = outputs.last_hidden_state
|
|
```"""
|
|
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:
|
|
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")
|
|
|
|
if bbox is None:
|
|
raise ValueError("You have to specify bbox")
|
|
|
|
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(input_shape, device=device)
|
|
|
|
if token_type_ids is None:
|
|
if hasattr(self.embeddings, "token_type_ids"):
|
|
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
|
|
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
|
|
token_type_ids = buffered_token_type_ids_expanded
|
|
else:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
|
|
# 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, device)
|
|
|
|
# If a 2D or 3D attention mask is provided for the cross-attention
|
|
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
|
if self.config.is_decoder and encoder_hidden_states is not None:
|
|
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
|
|
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
|
|
if encoder_attention_mask is None:
|
|
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
|
|
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
|
else:
|
|
encoder_extended_attention_mask = None
|
|
|
|
# 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,
|
|
)
|
|
|
|
# if bbox has 2 points (4 float tensors) per token, convert it to 4 points (8 float tensors) per token
|
|
if bbox.shape[-1] == 4:
|
|
bbox = bbox[:, :, [0, 1, 2, 1, 2, 3, 0, 3]]
|
|
scaled_bbox = bbox * self.config.bbox_scale
|
|
bbox_position_embeddings = self.bbox_embeddings(scaled_bbox)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
bbox_pos_emb=bbox_position_embeddings,
|
|
attention_mask=extended_attention_mask,
|
|
head_mask=head_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_extended_attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=True,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
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,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
cross_attentions=encoder_outputs.cross_attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class BrosForTokenClassification(BrosPreTrainedModel):
|
|
_keys_to_ignore_on_load_unexpected = [r"pooler"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.bros = BrosModel(config)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if hasattr(config, "classifier_dropout") else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
self.init_weights()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
bbox: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
bbox_first_token_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], TokenClassifierOutput]:
|
|
r"""
|
|
bbox ('torch.FloatTensor' of shape '(batch_size, num_boxes, 4)'):
|
|
Bounding box coordinates for each token in the input sequence. Each bounding box is a list of four values
|
|
(x1, y1, x2, y2), where (x1, y1) is the top left corner, and (x2, y2) is the bottom right corner of the
|
|
bounding box.
|
|
bbox_first_token_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to indicate the first token of each bounding box. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> import torch
|
|
>>> from transformers import BrosProcessor, BrosForTokenClassification
|
|
|
|
>>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")
|
|
|
|
>>> model = BrosForTokenClassification.from_pretrained("jinho8345/bros-base-uncased")
|
|
|
|
>>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
|
|
>>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)
|
|
>>> encoding["bbox"] = bbox
|
|
|
|
>>> outputs = model(**encoding)
|
|
```"""
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.bros(
|
|
input_ids,
|
|
bbox=bbox,
|
|
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=True,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
if bbox_first_token_mask is not None:
|
|
bbox_first_token_mask = bbox_first_token_mask.view(-1)
|
|
loss = loss_fct(
|
|
logits.view(-1, self.num_labels)[bbox_first_token_mask], labels.view(-1)[bbox_first_token_mask]
|
|
)
|
|
else:
|
|
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,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Bros Model with a token classification head on top (initial_token_layers and subsequent_token_layer on top of the
|
|
hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. The initial_token_classifier is used to
|
|
predict the first token of each entity, and the subsequent_token_classifier is used to predict the subsequent
|
|
tokens within an entity. Compared to BrosForTokenClassification, this model is more robust to serialization errors
|
|
since it predicts next token from one token.
|
|
"""
|
|
)
|
|
class BrosSpadeEEForTokenClassification(BrosPreTrainedModel):
|
|
_keys_to_ignore_on_load_unexpected = [r"pooler"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.config = config
|
|
self.num_labels = config.num_labels
|
|
self.n_relations = config.n_relations
|
|
self.backbone_hidden_size = config.hidden_size
|
|
|
|
self.bros = BrosModel(config)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if hasattr(config, "classifier_dropout") else config.hidden_dropout_prob
|
|
)
|
|
|
|
# Initial token classification for Entity Extraction (NER)
|
|
self.initial_token_classifier = nn.Sequential(
|
|
nn.Dropout(classifier_dropout),
|
|
nn.Linear(config.hidden_size, config.hidden_size),
|
|
nn.Dropout(classifier_dropout),
|
|
nn.Linear(config.hidden_size, config.num_labels),
|
|
)
|
|
|
|
# Subsequent token classification for Entity Extraction (NER)
|
|
self.subsequent_token_classifier = BrosRelationExtractor(config)
|
|
|
|
self.init_weights()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
bbox: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
bbox_first_token_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,
|
|
initial_token_labels: Optional[torch.Tensor] = None,
|
|
subsequent_token_labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], BrosSpadeOutput]:
|
|
r"""
|
|
bbox ('torch.FloatTensor' of shape '(batch_size, num_boxes, 4)'):
|
|
Bounding box coordinates for each token in the input sequence. Each bounding box is a list of four values
|
|
(x1, y1, x2, y2), where (x1, y1) is the top left corner, and (x2, y2) is the bottom right corner of the
|
|
bounding box.
|
|
bbox_first_token_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to indicate the first token of each bounding box. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
initial_token_labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for the initial token classification.
|
|
subsequent_token_labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for the subsequent token classification.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> import torch
|
|
>>> from transformers import BrosProcessor, BrosSpadeEEForTokenClassification
|
|
|
|
>>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")
|
|
|
|
>>> model = BrosSpadeEEForTokenClassification.from_pretrained("jinho8345/bros-base-uncased")
|
|
|
|
>>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
|
|
>>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)
|
|
>>> encoding["bbox"] = bbox
|
|
|
|
>>> outputs = model(**encoding)
|
|
```"""
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.bros(
|
|
input_ids=input_ids,
|
|
bbox=bbox,
|
|
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=True,
|
|
)
|
|
|
|
last_hidden_states = outputs[0]
|
|
last_hidden_states = last_hidden_states.transpose(0, 1).contiguous()
|
|
initial_token_logits = self.initial_token_classifier(last_hidden_states).transpose(0, 1).contiguous()
|
|
subsequent_token_logits = self.subsequent_token_classifier(last_hidden_states, last_hidden_states).squeeze(0)
|
|
|
|
# make subsequent token (sequence token classification) mask
|
|
inv_attention_mask = 1 - attention_mask
|
|
batch_size, max_seq_length = inv_attention_mask.shape
|
|
device = inv_attention_mask.device
|
|
invalid_token_mask = torch.cat([inv_attention_mask, torch.zeros([batch_size, 1]).to(device)], axis=1).bool()
|
|
subsequent_token_logits = subsequent_token_logits.masked_fill(
|
|
invalid_token_mask[:, None, :], torch.finfo(subsequent_token_logits.dtype).min
|
|
)
|
|
self_token_mask = torch.eye(max_seq_length, max_seq_length + 1).to(device=device, dtype=torch.bool)
|
|
subsequent_token_logits = subsequent_token_logits.masked_fill(
|
|
self_token_mask[None, :, :], torch.finfo(subsequent_token_logits.dtype).min
|
|
)
|
|
subsequent_token_mask = attention_mask.view(-1).bool()
|
|
|
|
loss = None
|
|
if initial_token_labels is not None and subsequent_token_labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
|
|
# get initial token loss
|
|
initial_token_labels = initial_token_labels.view(-1)
|
|
if bbox_first_token_mask is not None:
|
|
bbox_first_token_mask = bbox_first_token_mask.view(-1)
|
|
initial_token_loss = loss_fct(
|
|
initial_token_logits.view(-1, self.num_labels)[bbox_first_token_mask],
|
|
initial_token_labels[bbox_first_token_mask],
|
|
)
|
|
else:
|
|
initial_token_loss = loss_fct(initial_token_logits.view(-1, self.num_labels), initial_token_labels)
|
|
|
|
subsequent_token_labels = subsequent_token_labels.view(-1)
|
|
subsequent_token_loss = loss_fct(
|
|
subsequent_token_logits.view(-1, max_seq_length + 1)[subsequent_token_mask],
|
|
subsequent_token_labels[subsequent_token_mask],
|
|
)
|
|
|
|
loss = initial_token_loss + subsequent_token_loss
|
|
|
|
return BrosSpadeOutput(
|
|
loss=loss,
|
|
initial_token_logits=initial_token_logits,
|
|
subsequent_token_logits=subsequent_token_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Bros Model with a token classification head on top (a entity_linker layer on top of the hidden-states output) e.g.
|
|
for Entity-Linking. The entity_linker is used to predict intra-entity links (one entity to another entity).
|
|
"""
|
|
)
|
|
class BrosSpadeELForTokenClassification(BrosPreTrainedModel):
|
|
_keys_to_ignore_on_load_unexpected = [r"pooler"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.config = config
|
|
self.num_labels = config.num_labels
|
|
self.n_relations = config.n_relations
|
|
self.backbone_hidden_size = config.hidden_size
|
|
|
|
self.bros = BrosModel(config)
|
|
(config.classifier_dropout if hasattr(config, "classifier_dropout") else config.hidden_dropout_prob)
|
|
|
|
self.entity_linker = BrosRelationExtractor(config)
|
|
|
|
self.init_weights()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
bbox: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
bbox_first_token_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], TokenClassifierOutput]:
|
|
r"""
|
|
bbox ('torch.FloatTensor' of shape '(batch_size, num_boxes, 4)'):
|
|
Bounding box coordinates for each token in the input sequence. Each bounding box is a list of four values
|
|
(x1, y1, x2, y2), where (x1, y1) is the top left corner, and (x2, y2) is the bottom right corner of the
|
|
bounding box.
|
|
bbox_first_token_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to indicate the first token of each bounding box. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> import torch
|
|
>>> from transformers import BrosProcessor, BrosSpadeELForTokenClassification
|
|
|
|
>>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")
|
|
|
|
>>> model = BrosSpadeELForTokenClassification.from_pretrained("jinho8345/bros-base-uncased")
|
|
|
|
>>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
|
|
>>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)
|
|
>>> encoding["bbox"] = bbox
|
|
|
|
>>> outputs = model(**encoding)
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.bros(
|
|
input_ids=input_ids,
|
|
bbox=bbox,
|
|
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=True,
|
|
)
|
|
|
|
last_hidden_states = outputs[0]
|
|
last_hidden_states = last_hidden_states.transpose(0, 1).contiguous()
|
|
|
|
logits = self.entity_linker(last_hidden_states, last_hidden_states).squeeze(0)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
|
|
batch_size, max_seq_length = attention_mask.shape
|
|
device = attention_mask.device
|
|
|
|
self_token_mask = torch.eye(max_seq_length, max_seq_length + 1).to(device=device, dtype=torch.bool)
|
|
|
|
mask = bbox_first_token_mask.view(-1)
|
|
bbox_first_token_mask = torch.cat(
|
|
[
|
|
~bbox_first_token_mask,
|
|
torch.zeros([batch_size, 1], dtype=torch.bool, device=device),
|
|
],
|
|
axis=1,
|
|
)
|
|
logits = logits.masked_fill(bbox_first_token_mask[:, None, :], torch.finfo(logits.dtype).min)
|
|
logits = logits.masked_fill(self_token_mask[None, :, :], torch.finfo(logits.dtype).min)
|
|
|
|
loss = loss_fct(logits.view(-1, max_seq_length + 1)[mask], labels.view(-1)[mask])
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"BrosPreTrainedModel",
|
|
"BrosModel",
|
|
"BrosForTokenClassification",
|
|
"BrosSpadeEEForTokenClassification",
|
|
"BrosSpadeELForTokenClassification",
|
|
]
|