mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
671 lines
35 KiB
671 lines
35 KiB
from typing import Dict, List, Optional, Tuple, Union
|
|
|
|
import torch
|
|
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
|
from transformers.modeling_outputs import (
|
|
BaseModelOutputWithPastAndCrossAttentions,
|
|
CausalLMOutputWithCrossAttentions,
|
|
QuestionAnsweringModelOutput,
|
|
SequenceClassifierOutputWithPast,
|
|
TokenClassifierOutput,
|
|
)
|
|
from transformers.models.gpt2.modeling_gpt2 import (
|
|
GPT2DoubleHeadsModel,
|
|
GPT2DoubleHeadsModelOutput,
|
|
GPT2ForQuestionAnswering,
|
|
GPT2ForSequenceClassification,
|
|
GPT2ForTokenClassification,
|
|
GPT2LMHeadModel,
|
|
GPT2Model,
|
|
)
|
|
from transformers.utils import logging
|
|
|
|
from colossalai.pipeline.stage_manager import PipelineStageManager
|
|
|
|
|
|
class GPT2PipelineForwards:
|
|
'''
|
|
This class serves as a micro library for forward function substitution of GPT2 models
|
|
under pipeline setting.
|
|
'''
|
|
|
|
@staticmethod
|
|
def gpt2_model_forward(
|
|
self: GPT2Model,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
stage_manager: Optional[PipelineStageManager] = None,
|
|
hidden_states: Optional[torch.FloatTensor] = None,
|
|
stage_index: Optional[List[int]] = None) -> Union[Dict, Tuple, BaseModelOutputWithPastAndCrossAttentions]:
|
|
|
|
# This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2Model.forward.
|
|
# Please refer to original code of transformers for more details.
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
# Preprocess passed in arguments
|
|
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
|
|
if past_key_values:
|
|
logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.')
|
|
past_key_values = None
|
|
if output_attentions:
|
|
logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.')
|
|
output_attentions = False
|
|
if output_hidden_states:
|
|
logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.')
|
|
output_hidden_states = False
|
|
if use_cache:
|
|
logger.warning_once('use_cache=True is not supported for pipeline models at the moment.')
|
|
use_cache = False
|
|
|
|
if stage_manager.is_first_stage():
|
|
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:
|
|
batch_size, seq_length = input_ids.shape
|
|
input_shape = input_ids.size()
|
|
input_ids = input_ids.view(-1, seq_length)
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
batch_size = inputs_embeds.shape[0]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
if token_type_ids is not None:
|
|
token_type_ids = token_type_ids.view(-1, seq_length)
|
|
else:
|
|
if hidden_states is None:
|
|
raise ValueError("hidden_states shouldn't be None for stages other than the first stage.")
|
|
input_shape = hidden_states.size()[:-1]
|
|
batch_size, seq_length = input_shape[0], input_shape[1]
|
|
device = hidden_states.device
|
|
|
|
# GPT2Attention mask.
|
|
if attention_mask is not None:
|
|
if batch_size <= 0:
|
|
raise ValueError("batch_size has to be defined and > 0")
|
|
attention_mask = attention_mask.view(batch_size, -1)
|
|
# We create a 3D attention mask from a 2D tensor mask.
|
|
# Sizes are [batch_size, 1, 1, to_seq_length]
|
|
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
|
|
# this attention mask is more simple than the triangular masking of causal attention
|
|
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
|
|
attention_mask = attention_mask[:, None, None, :]
|
|
|
|
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
|
|
# masked positions, this operation will create a tensor which is 0.0 for
|
|
# positions we want to attend and the dtype's smallest value for masked positions.
|
|
# Since we are adding it to the raw scores before the softmax, this is
|
|
# effectively the same as removing these entirely.
|
|
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
|
|
attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
|
|
|
|
# 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.add_cross_attention 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_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
|
else:
|
|
encoder_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
|
|
# head_mask has shape n_layer x batch x n_heads x N x N
|
|
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
|
|
|
|
if stage_manager.is_first_stage():
|
|
if position_ids is not None:
|
|
position_ids = position_ids.view(-1, seq_length)
|
|
else:
|
|
position_ids = torch.arange(0, seq_length, dtype=torch.long, device=device)
|
|
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.wte(input_ids)
|
|
position_embeds = self.wpe(position_ids)
|
|
hidden_states = inputs_embeds + position_embeds
|
|
if token_type_ids is not None:
|
|
token_type_embeds = self.wte(token_type_ids)
|
|
hidden_states = hidden_states + token_type_embeds
|
|
hidden_states = self.drop(hidden_states)
|
|
|
|
output_shape = input_shape + (hidden_states.size(-1),)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
|
|
use_cache = False
|
|
presents = () if use_cache else None
|
|
all_self_attentions = () if output_attentions else None
|
|
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
|
|
all_hidden_states = () if output_hidden_states else None
|
|
|
|
# Going through held blocks.
|
|
start_idx, end_idx = stage_index[0], stage_index[1]
|
|
for i in range(start_idx, end_idx):
|
|
block = self.h[i]
|
|
torch.cuda.set_device(hidden_states.device)
|
|
# Ensure that attention_mask is always on the same device as hidden_states
|
|
if attention_mask is not None:
|
|
attention_mask = attention_mask.to(hidden_states.device)
|
|
if isinstance(head_mask, torch.Tensor):
|
|
head_mask = head_mask.to(hidden_states.device)
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
|
|
def create_custom_forward(module):
|
|
|
|
def custom_forward(*inputs):
|
|
# None for past_key_value
|
|
return module(*inputs, use_cache, output_attentions)
|
|
|
|
return custom_forward
|
|
|
|
outputs = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(block),
|
|
hidden_states,
|
|
None,
|
|
attention_mask,
|
|
head_mask[i],
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
)
|
|
else:
|
|
outputs = block(
|
|
hidden_states,
|
|
layer_past=None,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask[i],
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
if use_cache is True:
|
|
presents = presents + (outputs[1],)
|
|
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
|
|
if self.config.add_cross_attention:
|
|
all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)
|
|
|
|
if stage_manager.is_last_stage():
|
|
hidden_states = self.ln_f(hidden_states)
|
|
|
|
hidden_states = hidden_states.view(output_shape)
|
|
|
|
# Add last hidden state
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if stage_manager.is_last_stage():
|
|
if not return_dict:
|
|
return tuple(
|
|
v for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions]
|
|
if v is not None)
|
|
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=presents,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
else:
|
|
# always return dict for intermediate stage
|
|
return {'hidden_states': hidden_states}
|
|
|
|
@staticmethod
|
|
def gpt2_lmhead_model_forward(
|
|
self: GPT2LMHeadModel,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
stage_manager: Optional[PipelineStageManager] = None,
|
|
hidden_states: Optional[torch.FloatTensor] = None,
|
|
stage_index: Optional[List[int]] = None) -> Union[Dict, Tuple, CausalLMOutputWithCrossAttentions]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
|
|
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
|
|
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
|
|
|
|
This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel.forward.
|
|
Please refer to original code of transformers for more details.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer,
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
stage_manager=stage_manager,
|
|
hidden_states=hidden_states,
|
|
stage_index=stage_index)
|
|
|
|
# If not at the last stage, return hidden_states as in GPT2Model
|
|
if not stage_manager.is_last_stage():
|
|
return {'hidden_states': outputs['hidden_states']}
|
|
|
|
hidden_states = outputs[0]
|
|
lm_logits = self.lm_head(hidden_states)
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(lm_logits.device)
|
|
# Shift so that tokens < n predict n
|
|
shift_logits = lm_logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
# Flatten the tokens
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
|
|
if not return_dict:
|
|
output = (lm_logits,) + outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return CausalLMOutputWithCrossAttentions(
|
|
loss=loss,
|
|
logits=lm_logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
cross_attentions=outputs.cross_attentions,
|
|
)
|
|
|
|
@staticmethod
|
|
def gpt2_double_heads_model_forward(
|
|
self: GPT2DoubleHeadsModel,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
mc_token_ids: Optional[torch.LongTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
mc_labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
stage_manager: Optional[PipelineStageManager] = None,
|
|
hidden_states: Optional[torch.FloatTensor] = None,
|
|
stage_index: Optional[List[int]] = None) -> Union[Dict, Tuple, GPT2DoubleHeadsModelOutput]:
|
|
r"""
|
|
mc_token_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
|
|
Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
|
|
1]`.
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
|
|
`labels = input_ids`. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to
|
|
`-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size - 1]`
|
|
mc_labels (`torch.LongTensor` of shape `(batch_size)`, *optional*):
|
|
Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
|
|
where *num_choices* is the size of the second dimension of the input tensors. (see *input_ids* above)
|
|
|
|
This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModel.forward.
|
|
Please refer to original code of transformers for more details.
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer,
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
stage_manager=stage_manager,
|
|
hidden_states=hidden_states,
|
|
stage_index=stage_index)
|
|
|
|
# If not at the last stage, return hidden_states as in GPT2Model
|
|
if not stage_manager.is_last_stage():
|
|
return {'hidden_states': outputs['hidden_states']}
|
|
|
|
hidden_states = outputs[0]
|
|
lm_logits = self.lm_head(hidden_states)
|
|
mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1)
|
|
|
|
mc_loss = None
|
|
if mc_labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
mc_loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1))
|
|
lm_loss = None
|
|
if labels is not None:
|
|
labels = labels.to(lm_logits.device)
|
|
shift_logits = lm_logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
loss_fct = CrossEntropyLoss()
|
|
lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (lm_logits, mc_logits) + outputs[1:]
|
|
if mc_loss is not None:
|
|
output = (mc_loss,) + output
|
|
return ((lm_loss,) + output) if lm_loss is not None else output
|
|
|
|
return GPT2DoubleHeadsModelOutput(
|
|
loss=lm_loss,
|
|
mc_loss=mc_loss,
|
|
logits=lm_logits,
|
|
mc_logits=mc_logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
@staticmethod
|
|
def gpt2_for_question_answering_forward(
|
|
self: GPT2ForQuestionAnswering,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = 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,
|
|
stage_manager: Optional[PipelineStageManager] = None,
|
|
hidden_states: Optional[torch.FloatTensor] = None,
|
|
stage_index: Optional[List[int]] = None) -> Union[Dict, Tuple, QuestionAnsweringModelOutput]:
|
|
r"""
|
|
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
|
|
# This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2ForQuestionAnswering.forward.
|
|
# Please refer to original code of transformers for more details.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer,
|
|
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,
|
|
stage_manager=stage_manager,
|
|
hidden_states=hidden_states,
|
|
stage_index=stage_index)
|
|
|
|
# If not at the last stage, return hidden_states as in GPT2Model
|
|
if not stage_manager.is_last_stage():
|
|
return {'hidden_states': outputs['hidden_states']}
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
start_logits = start_logits.squeeze(-1).contiguous()
|
|
end_logits = end_logits.squeeze(-1).contiguous()
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if len(start_positions.size()) > 1:
|
|
start_positions = start_positions.squeeze(-1).to(start_logits.device)
|
|
if len(end_positions.size()) > 1:
|
|
end_positions = end_positions.squeeze(-1).to(end_logits.device)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.size(1)
|
|
start_positions = start_positions.clamp(0, ignored_index)
|
|
end_positions = end_positions.clamp(0, ignored_index)
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
if not return_dict:
|
|
output = (start_logits, end_logits) + outputs[2:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
@staticmethod
|
|
def gpt2_for_token_classification_forward(
|
|
self: GPT2ForTokenClassification,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
stage_manager: Optional[PipelineStageManager] = None,
|
|
hidden_states: Optional[torch.FloatTensor] = None,
|
|
stage_index: Optional[List[int]] = None) -> Union[Dict, Tuple, TokenClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
|
|
# This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2ForTokenClassification.forward.
|
|
# Please refer to original code of transformers for more details.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer,
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
stage_manager=stage_manager,
|
|
hidden_states=hidden_states,
|
|
stage_index=stage_index)
|
|
|
|
# If not at the last stage, return hidden_states as in GPT2Model
|
|
if not stage_manager.is_last_stage():
|
|
return {'hidden_states': outputs['hidden_states']}
|
|
|
|
hidden_states = outputs[0]
|
|
hidden_states = self.dropout(hidden_states)
|
|
logits = self.classifier(hidden_states)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
labels = labels.to(logits.device)
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
@staticmethod
|
|
def gpt2_for_sequence_classification_forward(
|
|
self: GPT2ForSequenceClassification,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
stage_manager: Optional[PipelineStageManager] = None,
|
|
hidden_states: Optional[torch.FloatTensor] = None,
|
|
stage_index: Optional[List[int]] = None) -> Union[Dict, Tuple, SequenceClassifierOutputWithPast]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
|
|
# This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2ForSequenceClassification.forward.
|
|
# Please refer to original code of transformers for more details.
|
|
"""
|
|
logger = logging.get_logger(__name__)
|
|
|
|
if input_ids is not None:
|
|
batch_size, _ = input_ids.shape[:2]
|
|
else:
|
|
batch_size, _ = hidden_states.shape[:2]
|
|
assert (self.config.pad_token_id is not None
|
|
or batch_size == 1), "Cannot handle batch sizes > 1 if no padding token is defined."
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer,
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
stage_manager=stage_manager,
|
|
hidden_states=hidden_states,
|
|
stage_index=stage_index)
|
|
|
|
# If not at the last stage, return hidden_states as in GPT2Model
|
|
if not stage_manager.is_last_stage():
|
|
return {'hidden_states': outputs['hidden_states']}
|
|
|
|
hidden_states = outputs[0]
|
|
logits = self.score(hidden_states)
|
|
|
|
if self.config.pad_token_id is None:
|
|
sequence_lengths = -1
|
|
else:
|
|
if input_ids is not None:
|
|
sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
|
|
else:
|
|
sequence_lengths = -1
|
|
logger.warning_once(
|
|
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
|
"unexpected if using padding tokens in conjunction with `inputs_embeds.`")
|
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(pooled_logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(pooled_logits, labels)
|
|
if not return_dict:
|
|
output = (pooled_logits,) + outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutputWithPast(
|
|
loss=loss,
|
|
logits=pooled_logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|