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Commit 18ebcf40 authored by Jianghai's avatar Jianghai Committed by Hongxin Liu
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[pipeline] reformat for unified design (#4283) 

* bert_reformat

* reformat

* reformat

* fix a typo

* format

* format

* fix bug
parent 0a8f3c85
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colossalai/shardformer/modeling/bert.py 0 → 100644
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from typing import Any, Dict, List, Optional, Tuple
import torch
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.modeling_outputs import (
BaseModelOutputWithPoolingAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
MaskedLMOutput,
MultipleChoiceModelOutput,
NextSentencePredictorOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from transformers.models.bert.modeling_bert import (
BertForMaskedLM,
BertForMultipleChoice,
BertForNextSentencePrediction,
BertForPreTraining,
BertForPreTrainingOutput,
BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification,
BertLMHeadModel,
BertModel,
)
from transformers.utils import logging
from colossalai.pipeline.stage_manager import PipelineStageManager
class BertPipelineForwards:
'''
This class serves as a micro library for forward function substitution of Bert models
under pipeline setting.
'''
@staticmethod
def bert_model_forward(
self: BertModel,
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,
stage_manager: Optional[PipelineStageManager] = None,
hidden_states: Optional[torch.FloatTensor] = None, # this is from the previous stage
stage_index: Optional[List[int]] = None,
):
# TODO: add explaination of the output here.
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
logger = logging.get_logger(__name__)
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 self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
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:
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 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)
else:
input_shape = hidden_states.size()[:-1]
batch_size, seq_length = input_shape
device = hidden_states.device
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), 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)
attention_mask = extended_attention_mask
# 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)
hidden_states = hidden_states if hidden_states is not None else None
if stage_manager.is_first_stage():
hidden_states = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
# inherit from bert_layer,this should be changed when we add the feature to record hidden_states
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
if self.encoder.gradient_checkpointing and self.encoder.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
use_cache = False
next_decoder_cache = () if use_cache else None
start_idx, end_idx = stage_index[0], stage_index[1]
# layer_outputs
layer_outputs = hidden_states if hidden_states is not None else None
for idx, encoder_layer in enumerate(self.encoder.layer[start_idx:end_idx], start=start_idx):
if stage_manager.is_first_stage() and idx == 0:
encoder_attention_mask = encoder_extended_attention_mask
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[idx] if head_mask is not None else None
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.encoder.gradient_checkpointing and self.encoder.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, past_key_value, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(encoder_layer),
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
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,)
# end of a stage loop
sequence_output = layer_outputs[0] if layer_outputs is not None else None
if stage_manager.is_last_stage():
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + layer_outputs[1:]
# return dict is not supported at this moment
else:
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
# output of non-first and non-last stages: must be a dict
else:
# intermediate stage always return dict
return {
'hidden_states': hidden_states,
}
@staticmethod
def bert_for_pretraining_forward(
self: BertForPreTraining,
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,
labels: Optional[torch.Tensor] = None,
next_sentence_label: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.FloatTensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
stage_index: Optional[List[int]] = None,
):
logger = logging.get_logger(__name__)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
outputs = BertPipelineForwards.bert_model_forward(
self.bert,
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 if hidden_states is not None else None,
stage_index=stage_index,
)
past_key_values = None
all_hidden_states = None
all_self_attentions = None
all_cross_attentions = None
if stage_manager.is_last_stage():
sequence_output, pooled_output = outputs[:2]
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
# the last stage for pretraining model
total_loss = None
if labels is not None and next_sentence_label is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
total_loss = masked_lm_loss + next_sentence_loss
if not return_dict:
output = (prediction_scores, seq_relationship_score) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return BertForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
seq_relationship_logits=seq_relationship_score,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
hidden_states = outputs.get('hidden_states')
# intermediate stage always return dict
return {
'hidden_states': hidden_states,
}
@staticmethod
def bert_lm_head_model_forward(
self: BertLMHeadModel,
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,
labels: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.Tensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.FloatTensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
stage_index: Optional[List[int]] = None,
):
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
logger = logging.get_logger(__name__)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
outputs = BertPipelineForwards.bert_model_forward(
self.bert,
input_ids,
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,
past_key_values=past_key_values,
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 if hidden_states is not None else None,
stage_index=stage_index)
past_key_values = None
all_hidden_states = None
all_self_attentions = None
all_cross_attentions = None
if stage_manager.is_last_stage():
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
lm_loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((lm_loss,) + output) if lm_loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=lm_loss,
logits=prediction_scores,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
else:
hidden_states = outputs.get('hidden_states')
# intermediate stage always return dict
return {'hidden_states': hidden_states}
@staticmethod
def bert_for_masked_lm_forward(
self: BertForMaskedLM,
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,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.Tensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
stage_index: Optional[List[int]] = None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
"""
logger = logging.get_logger(__name__)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
outputs = BertPipelineForwards.bert_model_forward(
self.bert,
input_ids,
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,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
hidden_states=hidden_states,
stage_manager=stage_manager,
stage_index=stage_index,
)
if stage_manager.is_last_stage():
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss() # -100 index = padding token
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
hidden_states = outputs.get('hidden_states')
return {'hidden_states': hidden_states}
@staticmethod
def bert_for_next_sentence_prediction_forward(
self: BertForNextSentencePrediction,
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,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.Tensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
stage_index: Optional[List[int]] = None,
**kwargs,
):
#-> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
(see `input_ids` docstring). Indices should be in `[0, 1]`:
- 0 indicates sequence B is a continuation of sequence A,
- 1 indicates sequence B is a random sequence.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, BertForNextSentencePrediction
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
>>> model = BertForNextSentencePrediction.from_pretrained("bert-base-uncased")
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
>>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
>>> outputs = model(**encoding, labels=torch.LongTensor([1]))
>>> logits = outputs.logits
>>> assert logits[0, 0] < logits[0, 1] # next sentence was random
```
"""
logger = logging.get_logger(__name__)
if "next_sentence_label" in kwargs:
warnings.warn(
"The `next_sentence_label` argument is deprecated and will be removed in a future version, use"
" `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("next_sentence_label")
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = BertPipelineForwards.bert_model_forward(self.bert,
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,
hidden_states=hidden_states,
stage_manager=stage_manager,
stage_index=stage_index)
if stage_manager.is_last_stage():
pooled_output = outputs[1]
seq_relationship_scores = self.cls(pooled_output)
next_sentence_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1))
if not return_dict:
output = (seq_relationship_scores,) + outputs[2:]
return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
return NextSentencePredictorOutput(
loss=next_sentence_loss,
logits=seq_relationship_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
hidden_states = outputs.get('hidden_states')
# intermediate stage always return dict
return {'hidden_states': hidden_states}
@staticmethod
def bert_for_sequence_classification_forward(
self: BertForSequenceClassification,
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,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.Tensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
stage_index: Optional[List[int]] = None,
):
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).
"""
logger = logging.get_logger(__name__)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = BertPipelineForwards.bert_model_forward(self.bert,
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,
hidden_states=hidden_states,
stage_manager=stage_manager,
stage_index=stage_index)
if stage_manager.is_last_stage():
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
hidden_states = outputs.get('hidden_states')
return {'hidden_states': hidden_states}
@staticmethod
def bert_for_token_classification_forward(
self: BertForTokenClassification,
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,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.Tensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
stage_index: Optional[List[int]] = None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
logger = logging.get_logger(__name__)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = BertPipelineForwards.bert_model_forward(
self.bert,
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,
hidden_states=hidden_states,
stage_manager=stage_manager,
stage_index=stage_index,
)
if stage_manager.is_last_stage():
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()
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,
)
else:
hidden_states = outputs.get('hidden_states')
return {'hidden_states': hidden_states}
@staticmethod
def bert_for_multiple_choice_forward(
self: BertForMultipleChoice,
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,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.Tensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
stage_index: Optional[List[int]] = None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
logger = logging.get_logger(__name__)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# in our pipeline design,input ids are copied for every stage and shouldn't be none
# the input_ids for multiple choice model is [batch_size, num_choices, sequence_length]
if stage_manager.is_last_stage():
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
inputs_embeds = (inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None else None)
outputs = BertPipelineForwards.bert_model_forward(
self.bert,
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,
hidden_states=hidden_states,
stage_manager=stage_manager,
stage_index=stage_index,
)
if stage_manager.is_last_stage():
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
hidden_states = outputs.get('hidden_states')
return {'hidden_states': hidden_states}
@staticmethod
def bert_for_question_answering_forward(
self: BertForQuestionAnswering,
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.Tensor] = None,
end_positions: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.Tensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
stage_index: Optional[List[int]] = None,
):
# NOTE: the arg start_position and end_position are used only for the last stage
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.
"""
logger = logging.get_logger(__name__)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = BertPipelineForwards.bert_model_forward(
self.bert,
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,
hidden_states=hidden_states,
stage_manager=stage_manager,
stage_index=stage_index,
)
if stage_manager.is_last_stage():
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
hidden_states = outputs.get('hidden_states')
return {'hidden_states': hidden_states}
colossalai/shardformer/modeling/bloom.py
View file @ 18ebcf40
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.distributed as dist
from torch.distributed import ProcessGroup
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
QuestionAnsweringModelOutput,
SequenceClassifierOutputWithPast,
TokenClassifierOutput,
)
from transformers.models.bloom.modeling_bloom import (
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
)
from transformers.utils import logging
from colossalai.pipeline.stage_manager import PipelineStageManager
def build_bloom_alibi_tensor_fn(process_group: ProcessGroup) -> torch.Tensor:
......@@ -67,3 +88,602 @@ def build_bloom_alibi_tensor_fn(process_group: ProcessGroup) -> torch.Tensor:
return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype)
return build_bloom_alibi_tensor
class BloomPipelineForwards:
'''
This class serves as a micro library for bloom pipeline forwards.
'''
@staticmethod
def bloom_model_forward(
self: BloomModel,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: 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,
**deprecated_arguments,
) -> Union[Tuple[torch.Tensor, ...], 'BaseModelOutputWithPastAndCrossAttentions']:
logger = logging.get_logger(__name__)
if deprecated_arguments.pop("position_ids", False) is not False:
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
warnings.warn(
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
" passing `position_ids`.",
FutureWarning,
)
if len(deprecated_arguments) > 0:
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
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)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# add warnings here
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
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape batch_size x num_heads x N x N
# head_mask has shape n_layer x batch x num_heads x N x N
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
# case: First stage of training
if stage_manager.is_first_stage():
# check input_ids and inputs_embeds
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
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
hidden_states = self.word_embeddings_layernorm(inputs_embeds)
# initialize in the first stage and then pass to the next stage
else:
input_shape = hidden_states.shape[:-1]
batch_size, seq_length = input_shape
# extra recording tensor should be generated in the first stage
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
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
if past_key_values is None:
past_key_values = tuple([None] * len(self.h))
# Compute alibi tensor: check build_alibi_tensor documentation,build for every stage
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values[0] is not None:
past_key_values_length = past_key_values[0][0].shape[2] # source_len
seq_length_with_past = seq_length_with_past + past_key_values_length
if attention_mask is None:
attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
else:
attention_mask = attention_mask.to(hidden_states.device)
alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
# causal_mask is constructed every stage and its input is passed through different stages
causal_mask = self._prepare_attn_mask(
attention_mask,
input_shape=(batch_size, seq_length),
past_key_values_length=past_key_values_length,
)
start_idx, end_idx = stage_index[0], stage_index[1]
for i, (block, layer_past) in enumerate(zip(self.h[start_idx:end_idx], past_key_values[start_idx:end_idx]),
start=start_idx):
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=use_cache, output_attentions=output_attentions)
return custom_forward
outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
alibi,
causal_mask,
layer_past,
head_mask[i],
)
else:
outputs = block(
hidden_states,
layer_past=layer_past,
attention_mask=causal_mask,
head_mask=head_mask[i],
use_cache=use_cache,
output_attentions=output_attentions,
alibi=alibi,
)
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 stage_manager.is_last_stage():
# Add last hidden state
hidden_states = self.ln_f(hidden_states)
# TODO: deal with all_hidden_states, all_self_attentions, presents
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] if v is not None)
# attention_mask is not returned ; presents = past_key_values
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
else:
# always return dict for imediate stage
return {'hidden_states': hidden_states}
@staticmethod
def bloom_for_causal_lm_forward(self: BloomForCausalLM,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = 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,
**deprecated_arguments):
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]`
"""
logger = logging.get_logger(__name__)
if deprecated_arguments.pop("position_ids", False) is not False:
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
warnings.warn(
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
" passing `position_ids`.",
FutureWarning,
)
if len(deprecated_arguments) > 0:
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
transformer_outputs = BloomPipelineForwards.bloom_model_forward(self.transformer,
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
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)
past_key_values = None
all_hidden_states = None
all_self_attentions = None
all_cross_attentions = None
if stage_manager.is_last_stage():
hidden_states = transformer_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()
batch_size, seq_length, vocab_size = shift_logits.shape
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(batch_size * seq_length, vocab_size),
shift_labels.view(batch_size * seq_length))
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
else:
hidden_states = transformer_outputs.get('hidden_states')
return {'hidden_states': hidden_states}
@staticmethod
def bloom_for_sequence_classification_forward(
self: BloomForSequenceClassification,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = 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,
**deprecated_arguments,
):
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).
"""
logger = logging.get_logger(__name__)
if deprecated_arguments.pop("position_ids", False) is not False:
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
warnings.warn(
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
" passing `position_ids`.",
FutureWarning,
)
if len(deprecated_arguments) > 0:
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
transformer_outputs = BloomPipelineForwards.bloom_model_forward(
self.transformer,
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
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,
)
past_key_values = None
all_hidden_states = None
all_self_attentions = None
all_cross_attentions = None
if stage_manager.is_last_stage():
batch_size = hidden_states.shape[0]
#update batch size
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
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(
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, labels)
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(pooled_logits, labels)
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
else:
hidden_states = transformer_outputs.get('hidden_states')
return {'hidden_states': hidden_states}
@staticmethod
def bloom_for_token_classification_forward(
self: BloomForTokenClassification,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = 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,
**deprecated_arguments,
):
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).
"""
logger = logging.get_logger(__name__)
if deprecated_arguments.pop("position_ids", False) is not False:
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
warnings.warn(
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
" passing `position_ids`.",
FutureWarning,
)
if len(deprecated_arguments) > 0:
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
transformer_outputs = BloomPipelineForwards.bloom_model_forward(
self.transformer,
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
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,
)
past_key_values = None
all_hidden_states = None
all_self_attentions = None
all_cross_attentions = None
if stage_manager.is_last_stage():
hidden_states = transformer_outputs[0]
hidden_states = self.dropout(hidden_states)
logits = self.classifier(hidden_states)
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
batch_size, seq_length = labels.shape
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(batch_size * seq_length, self.num_labels),
labels.view(batch_size * seq_length))
if not return_dict:
output = (logits,) + transformer_outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
else:
hidden_states = transformer_outputs.get('hidden_states')
return {'hidden_states': hidden_states}
@staticmethod
def bloom_for_question_answering_forward(
self: BloomForQuestionAnswering,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = 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,
):
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.
"""
logger = logging.get_logger(__name__)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
outputs = BloomPipelineForwards.bloom_model_forward(
self.transformer,
input_ids,
attention_mask=attention_mask,
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,
)
past_key_values = None
all_hidden_states = None
all_self_attentions = None
all_cross_attentions = None
if stage_manager.is_last_stage():
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
hidden_states = outputs.get('hidden_states')
return {'hidden_states': hidden_states}
colossalai/shardformer/modeling/llama.py 0 → 100644
View file @ 18ebcf40
from typing import Callable, List, Optional
import torch
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
SequenceClassifierOutputWithPast,
)
from transformers.models.llama.modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel
from transformers.utils import logging
from colossalai.pipeline.stage_manager import PipelineStageManager
class LlamaPipelineForwards:
'''
This class serves as a micro library for forward function substitution of Llama models
under pipeline setting.
'''
def llama_model_forward(
self: LlamaModel,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: 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,
):
logger = logging.get_logger(__name__)
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)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if stage_manager.is_first_stage():
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
hidden_states = inputs_embeds
else:
input_shape = hidden_states.shape[:-1]
batch_size, seq_length = input_shape
device = hidden_states.device
seq_length_with_past = seq_length
past_key_values_length = 0
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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 past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if position_ids is None:
position_ids = torch.arange(past_key_values_length,
seq_length + past_key_values_length,
dtype=torch.long,
device=device)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
else:
position_ids = position_ids.view(-1, seq_length).long()
# embed positions, for the first stage, hidden_states is the input embeddings,
# for the other stages, hidden_states is the output of the previous stage
if attention_mask is None:
attention_mask = torch.ones((batch_size, seq_length_with_past),
dtype=torch.bool,
device=hidden_states.device)
attention_mask = self._prepare_decoder_attention_mask(attention_mask, (batch_size, seq_length), hidden_states,
past_key_values_length)
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
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
start_idx, end_idx = stage_index[0], stage_index[1]
for idx, decoder_layer in enumerate(self.layers[start_idx:end_idx], start=start_idx):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, output_attentions, None)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(decoder_layer),
hidden_states,
attention_mask,
position_ids,
None,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if stage_manager.is_last_stage():
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if stage_manager.is_last_stage():
if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
# always return dict for imediate stage
return {'hidden_states': hidden_states}
def llama_for_causal_lm_forward(
self: LlamaForCausalLM,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[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,
):
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, LlamaForCausalLM
>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
>>> prompt = "Hey, are you consciours? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
```"""
logger = logging.get_logger(__name__)
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
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = LlamaPipelineForwards.llama_model_forward(
self.model,
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
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,
)
past_key_values = None
all_hidden_states = None
all_self_attentions = None
all_cross_attentions = None
if stage_manager.is_last_stage():
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
hidden_states = outputs.get('hidden_states')
return {'hidden_states': hidden_states}
def llama_for_sequence_classification_forward(
self: LlamaForSequenceClassification,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[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,
):
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).
"""
logger = logging.get_logger(__name__)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
transformer_outputs = LlamaPipelineForwards.llama_model_forward(
self.model,
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
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 input_ids is not None:
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
batch_size = inputs_embeds.shape[0]
else:
batch_size = hidden_states.shape[0]
if stage_manager.is_last_stage():
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
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
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
loss = None
if labels is not None:
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(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,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
else:
hidden_states = transformer_outputs.get('hidden_states')
return {'hidden_states': hidden_states}
colossalai/shardformer/policies/bert.py
View file @ 18ebcf40
from functools import partial
from typing import Callable, Dict, List, Optional
from typing import Callable, Dict, List
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import CrossEntropyLoss, Module
from transformers.modeling_outputs import (
BaseModelOutputWithPoolingAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
MultipleChoiceModelOutput,
NextSentencePredictorOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from transformers.models.bert.modeling_bert import (
BertForMaskedLM,
BertForMultipleChoice,
BertForNextSentencePrediction,
BertForPreTraining,
BertForPreTrainingOutput,
BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification,
BertLMHeadModel,
BertModel,
)
from transformers.utils import logging
from torch.nn import Module
import colossalai.shardformer.layer as col_nn
from colossalai.pipeline.stage_manager import PipelineStageManager
from ..modeling.bert import BertPipelineForwards
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
logger = logging.get_logger(__name__)
__all__ = [
'BertPolicy', 'BertModelPolicy', 'BertForPreTrainingPolicy', 'BertLMdHeadModelPolicy', 'BertForMaskedLMPolicy',
'BertForNextSentencePredictionPolicy', 'BertForSequenceClassificationPolicy', 'BertForTokenClassificationPolicy',
......@@ -207,6 +182,27 @@ class BertPolicy(Policy):
return
def get_held_layers(self) -> List[Module]:
"""Get pipeline layers for current stage."""
assert self.pipeline_stage_manager is not None
if self.model.__class__.__name__ == 'BertModel':
module = self.model
else:
module = self.model.bert
stage_manager = self.pipeline_stage_manager
held_layers = []
layers_per_stage = self.distribute_layers(len(module.encoder.layer), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.embeddings)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.encoder.layer[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.pooler)
return held_layers
# BertModel
class BertModelPolicy(BertPolicy):
......@@ -217,21 +213,15 @@ class BertModelPolicy(BertPolicy):
def module_policy(self):
policy = super().module_policy()
from transformers.models.bert.modeling_bert import BertModel
self.set_pipeline_forward(model_cls=BertModel, new_forward=bert_model_forward, policy=policy)
if self.pipeline_stage_manager:
self.set_pipeline_forward(model_cls=BertModel,
new_forward=BertPipelineForwards.bert_model_forward,
policy=policy)
return policy
def get_held_layers(self) -> List[Module]:
"""Get pipeline layers for current stage."""
module = self.model
stage_manager = self.pipeline_stage_manager
held_layers = []
layers_per_stage = self.distribute_layers(len(module.encoder.layer), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.embeddings)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.encoder.layer[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.pooler)
held_layers = super().get_held_layers()
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
......@@ -250,30 +240,24 @@ class BertForPreTrainingPolicy(BertPolicy):
policy = self.add_lm_head_policy(policy)
policy = self.add_lm_prediction_policy(policy)
from transformers.models.bert.modeling_bert import BertForPreTraining
self.set_pipeline_forward(model_cls=BertForPreTraining, new_forward=bert_for_pretraining_forward, policy=policy)
if self.pipeline_stage_manager:
self.set_pipeline_forward(model_cls=BertForPreTraining,
new_forward=BertPipelineForwards.bert_for_pretraining_forward,
policy=policy)
return policy
def get_held_layers(self) -> List[Module]:
"""Get pipeline layers for current stage"""
module = self.model
held_layers = super().get_held_layers()
stage_manager = self.pipeline_stage_manager
layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages)
held_layers = []
if stage_manager.is_first_stage():
held_layers.append(module.bert.embeddings)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.bert.encoder.layer[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.bert.pooler)
held_layers.append(module.cls)
held_layers.append(self.model.cls)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
model = self.model
if self.pipeline_stage_manager:
if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
if id(model.bert.embeddings.word_embeddings.weight) == id(model.cls.predictions.decoder.weight):
# tie weights
return [{
......@@ -294,29 +278,25 @@ class BertLMHeadModelPolicy(BertPolicy):
policy = self.add_lm_head_policy(policy)
policy = self.add_lm_prediction_policy(policy)
from transformers.models.bert.modeling_bert import BertLMHeadModel
self.set_pipeline_forward(model_cls=BertLMHeadModel, new_forward=bert_lm_head_model_forward, policy=policy)
if self.pipeline_stage_manager:
self.set_pipeline_forward(model_cls=BertLMHeadModel,
new_forward=BertPipelineForwards.bert_lm_head_model_forward,
policy=policy)
return policy
def get_held_layers(self) -> List[Module]:
"""
get pipeline layers for current stage
"""
module = self.model
held_layers = []
held_layers = super().get_held_layers()
stage_manager = self.pipeline_stage_manager
layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.bert.embeddings)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.bert.encoder.layer[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.bert.pooler)
held_layers.append(module.cls)
held_layers.append(self.model.cls)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
bert_model = self.model.bert
if self.pipeline_stage_manager:
if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
if id(bert_model.embeddings.word_embeddings.weight) == id(self.model.cls.predictions.decoder.weight):
# tie weights
return [{
......@@ -337,29 +317,25 @@ class BertForMaskedLMPolicy(BertPolicy):
policy = self.add_lm_head_policy(policy)
mpolicy = self.add_lm_prediction_policy(policy)
from transformers.models.bert.modeling_bert import BertForMaskedLM
self.set_pipeline_forward(model_cls=BertForMaskedLM, new_forward=bert_for_masked_lm_forward, policy=policy)
if self.pipeline_stage_manager:
self.set_pipeline_forward(model_cls=BertForMaskedLM,
new_forward=BertPipelineForwards.bert_for_masked_lm_forward,
policy=policy)
return policy
def get_held_layers(self) -> List[Module]:
"""
get pipeline layers for current stage
"""
module = self.model
held_layers = []
held_layers = super().get_held_layers()
stage_manager = self.pipeline_stage_manager
layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.bert.embeddings)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.bert.encoder.layer[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.bert.pooler)
held_layers.append(module.cls)
held_layers.append(self.model.cls)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
bert_model = self.model.bert
if self.pipeline_stage_manager:
if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
if id(bert_model.embeddings.word_embeddings.weight) == id(self.model.cls.predictions.decoder.weight):
# tie weights
return [{
......@@ -391,10 +367,10 @@ class BertForSequenceClassificationPolicy(BertPolicy):
])
}
policy.update(addon_module)
self.set_pipeline_forward(model_cls=BertForSequenceClassification,
new_forward=bert_for_sequence_classification_forward,
policy=policy)
if self.pipeline_stage_manager:
self.set_pipeline_forward(model_cls=BertForSequenceClassification,
new_forward=BertPipelineForwards.bert_for_sequence_classification_forward,
policy=policy)
return policy
......@@ -402,18 +378,11 @@ class BertForSequenceClassificationPolicy(BertPolicy):
"""
get pipeline layers for current stage
"""
module = self.model
held_layers = []
held_layers = super().get_held_layers()
stage_manager = self.pipeline_stage_manager
layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.bert.embeddings)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.bert.encoder.layer[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.bert.pooler)
held_layers.append(module.dropout)
held_layers.append(module.classifier)
held_layers.append(self.model.dropout)
held_layers.append(self.model.classifier)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
......@@ -443,10 +412,10 @@ class BertForTokenClassificationPolicy(BertPolicy):
])
}
policy.update(addon_module)
self.set_pipeline_forward(model_cls=BertForTokenClassification,
new_forward=bert_for_token_classification_forward,
policy=policy)
if self.pipeline_stage_manager:
self.set_pipeline_forward(model_cls=BertForTokenClassification,
new_forward=BertPipelineForwards.bert_for_token_classification_forward,
policy=policy)
return policy
......@@ -454,18 +423,11 @@ class BertForTokenClassificationPolicy(BertPolicy):
"""
get pipeline layers for current stage
"""
module = self.model
held_layers = []
held_layers = super().get_held_layers()
stage_manager = self.pipeline_stage_manager
layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.bert.embeddings)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.bert.encoder.layer[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.bert.pooler)
held_layers.append(module.dropout)
held_layers.append(module.classifier)
held_layers.append(self.model.dropout)
held_layers.append(self.model.classifier)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
......@@ -482,9 +444,10 @@ class BertForNextSentencePredictionPolicy(BertPolicy):
def module_policy(self):
policy = super().module_policy()
from transformers.models.bert.modeling_bert import BertForNextSentencePrediction
self.set_pipeline_forward(model_cls=BertForNextSentencePrediction,
new_forward=bert_for_next_sentence_prediction_forward,
policy=policy)
if self.pipeline_stage_manager:
self.set_pipeline_forward(model_cls=BertForNextSentencePrediction,
new_forward=BertPipelineForwards.bert_for_next_sentence_prediction_forward,
policy=policy)
return policy
......@@ -492,17 +455,10 @@ class BertForNextSentencePredictionPolicy(BertPolicy):
"""
get pipeline layers for current stage
"""
module = self.model
held_layers = []
held_layers = super().get_held_layers()
stage_manager = self.pipeline_stage_manager
layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.bert.embeddings)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.bert.encoder.layer[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.bert.pooler)
held_layers.append(module.cls)
held_layers.append(self.model.cls)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
......@@ -532,10 +488,10 @@ class BertForMultipleChoicePolicy(BertPolicy):
])
}
policy.update(addon_module)
self.set_pipeline_forward(model_cls=BertForMultipleChoice,
new_forward=bert_for_multiple_choice_forward,
policy=policy)
if self.pipeline_stage_manager:
self.set_pipeline_forward(model_cls=BertForMultipleChoice,
new_forward=BertPipelineForwards.bert_for_multiple_choice_forward,
policy=policy)
return policy
......@@ -543,18 +499,11 @@ class BertForMultipleChoicePolicy(BertPolicy):
"""
get pipeline layers for current stage
"""
module = self.model
held_layers = []
held_layers = super().get_held_layers()
stage_manager = self.pipeline_stage_manager
layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.bert.embeddings)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.bert.encoder.layer[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.bert.pooler)
held_layers.append(module.dropout)
held_layers.append(module.classifier)
held_layers.append(self.model.dropout)
held_layers.append(self.model.classifier)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
......@@ -570,9 +519,10 @@ class BertForQuestionAnsweringPolicy(BertPolicy):
def module_policy(self):
from transformers.models.bert.modeling_bert import BertForQuestionAnswering
policy = super().module_policy()
self.set_pipeline_forward(model_cls=BertForQuestionAnswering,
new_forward=bert_for_question_answering_forward,
policy=policy)
if self.pipeline_stage_manager:
self.set_pipeline_forward(model_cls=BertForQuestionAnswering,
new_forward=BertPipelineForwards.bert_for_question_answering_forward,
policy=policy)
return policy
......@@ -580,957 +530,12 @@ class BertForQuestionAnsweringPolicy(BertPolicy):
"""
get pipeline layers for current stage
"""
module = self.model
held_layers = []
held_layers = super().get_held_layers()
stage_manager = self.pipeline_stage_manager
layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.bert.embeddings)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.bert.encoder.layer[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.bert.pooler)
held_layers.append(module.qa_outputs)
held_layers.append(self.model.qa_outputs)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
# no shared params for sequence classification model
return []
def bert_model_forward(
self: BertModel,
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,
stage_manager: Optional[PipelineStageManager] = None,
hidden_states: Optional[torch.FloatTensor] = None, # this is from the previous stage
stage_index: Optional[List[int]] = None,
):
# TODO: add explaination of the output here.
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
# debugging
# preprocess:
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 self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
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:
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 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)
else:
input_shape = hidden_states.size()[:-1]
batch_size, seq_length = input_shape
device = hidden_states.device
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), 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)
attention_mask = extended_attention_mask
# 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)
hidden_states = hidden_states if hidden_states is not None else None
if stage_manager.is_first_stage():
hidden_states = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
# inherit from bert_layer,this should be changed when we add the feature to record hidden_states
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
if self.encoder.gradient_checkpointing and self.encoder.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
use_cache = False
next_decoder_cache = () if use_cache else None
start_idx, end_idx = stage_index[0], stage_index[1]
# layer_outputs
layer_outputs = hidden_states if hidden_states is not None else None
for idx, encoder_layer in enumerate(self.encoder.layer[start_idx:end_idx], start=start_idx):
if stage_manager.is_first_stage() and idx == 0:
encoder_attention_mask = encoder_extended_attention_mask
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[idx] if head_mask is not None else None
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.encoder.gradient_checkpointing and self.encoder.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, past_key_value, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(encoder_layer),
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
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,)
# end of a stage loop
sequence_output = layer_outputs[0] if layer_outputs is not None else None
if stage_manager.is_last_stage():
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + layer_outputs[1:]
# return dict is not supported at this moment
else:
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
# output of non-first and non-last stages: must be a dict
else:
# intermediate stage always return dict
return {
'hidden_states': hidden_states,
}
def bert_for_pretraining_forward(
self: BertForPreTraining,
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,
labels: Optional[torch.Tensor] = None,
next_sentence_label: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.FloatTensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
stage_index: Optional[List[int]] = None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
outputs = bert_model_forward(
self.bert,
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 if hidden_states is not None else None,
stage_index=stage_index,
)
past_key_values = None
all_hidden_states = None
all_self_attentions = None
all_cross_attentions = None
if stage_manager.is_last_stage():
sequence_output, pooled_output = outputs[:2]
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
# the last stage for pretraining model
total_loss = None
if labels is not None and next_sentence_label is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
total_loss = masked_lm_loss + next_sentence_loss
if not return_dict:
output = (prediction_scores, seq_relationship_score) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return BertForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
seq_relationship_logits=seq_relationship_score,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
hidden_states = outputs.get('hidden_states')
# intermediate stage always return dict
return {
'hidden_states': hidden_states,
}
def bert_lm_head_model_forward(
self: BertLMHeadModel,
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,
labels: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.Tensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.FloatTensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
stage_index: Optional[List[int]] = None,
):
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
outputs = bert_model_forward(self.bert,
input_ids,
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,
past_key_values=past_key_values,
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 if hidden_states is not None else None,
stage_index=stage_index)
past_key_values = None
all_hidden_states = None
all_self_attentions = None
all_cross_attentions = None
if stage_manager.is_last_stage():
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
lm_loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((lm_loss,) + output) if lm_loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=lm_loss,
logits=prediction_scores,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
else:
hidden_states = outputs.get('hidden_states')
# intermediate stage always return dict
return {'hidden_states': hidden_states}
def bert_for_masked_lm_forward(
self: BertForMaskedLM,
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,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.Tensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
stage_index: Optional[List[int]] = None,
):
# -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
outputs = bert_model_forward(
self.bert,
input_ids,
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,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
hidden_states=hidden_states,
stage_manager=stage_manager,
stage_index=stage_index,
)
if stage_manager.is_last_stage():
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss() # -100 index = padding token
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
hidden_states = outputs.get('hidden_states')
return {'hidden_states': hidden_states}
def bert_for_next_sentence_prediction_forward(
self: BertForNextSentencePrediction,
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,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.Tensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
stage_index: Optional[List[int]] = None,
**kwargs,
):
# -> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
(see `input_ids` docstring). Indices should be in `[0, 1]`:
- 0 indicates sequence B is a continuation of sequence A,
- 1 indicates sequence B is a random sequence.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, BertForNextSentencePrediction
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
>>> model = BertForNextSentencePrediction.from_pretrained("bert-base-uncased")
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
>>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
>>> outputs = model(**encoding, labels=torch.LongTensor([1]))
>>> logits = outputs.logits
>>> assert logits[0, 0] < logits[0, 1] # next sentence was random
```
"""
if "next_sentence_label" in kwargs:
warnings.warn(
"The `next_sentence_label` argument is deprecated and will be removed in a future version, use"
" `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("next_sentence_label")
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = bert_model_forward(self.bert,
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,
hidden_states=hidden_states,
stage_manager=stage_manager,
stage_index=stage_index)
if stage_manager.is_last_stage():
pooled_output = outputs[1]
seq_relationship_scores = self.cls(pooled_output)
next_sentence_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1))
if not return_dict:
output = (seq_relationship_scores,) + outputs[2:]
return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
return NextSentencePredictorOutput(
loss=next_sentence_loss,
logits=seq_relationship_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
hidden_states = outputs.get('hidden_states')
# intermediate stage always return dict
return {'hidden_states': hidden_states}
def bert_for_sequence_classification_forward(
self: BertForSequenceClassification,
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,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.Tensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
stage_index: Optional[List[int]] = None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = bert_model_forward(self.bert,
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,
hidden_states=hidden_states,
stage_manager=stage_manager,
stage_index=stage_index)
if stage_manager.is_last_stage():
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
hidden_states = outputs.get('hidden_states')
return {'hidden_states': hidden_states}
def bert_for_token_classification_forward(
self: BertForTokenClassification,
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,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.Tensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
stage_index: Optional[List[int]] = None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = bert_model_forward(
self.bert,
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,
hidden_states=hidden_states,
stage_manager=stage_manager,
stage_index=stage_index,
)
if stage_manager.is_last_stage():
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()
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,
)
else:
hidden_states = outputs.get('hidden_states')
return {'hidden_states': hidden_states}
def bert_for_multiple_choice_forward(
self: BertForMultipleChoice,
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,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.Tensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
stage_index: Optional[List[int]] = None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# in our pipeline design,input ids are copied for every stage and shouldn't be none
# the input_ids for multiple choice model is [batch_size, num_choices, sequence_length]
if stage_manager.is_last_stage():
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
inputs_embeds = (inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None else None)
outputs = bert_model_forward(
self.bert,
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,
hidden_states=hidden_states,
stage_manager=stage_manager,
stage_index=stage_index,
)
if stage_manager.is_last_stage():
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
hidden_states = outputs.get('hidden_states')
return {'hidden_states': hidden_states}
def bert_for_question_answering_forward(
self: BertForQuestionAnswering,
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.Tensor] = None,
end_positions: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
hidden_states: Optional[torch.Tensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
stage_index: Optional[List[int]] = None,
):
# NOTE: the arg start_position and end_position are used only for the last stage
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = bert_model_forward(
self.bert,
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,
hidden_states=hidden_states,
stage_manager=stage_manager,
stage_index=stage_index,
)
if stage_manager.is_last_stage():
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
hidden_states = outputs.get('hidden_states')
return {'hidden_states': hidden_states}
colossalai/shardformer/policies/bloom.py
View file @ 18ebcf40
import warnings
from functools import partial
from typing import Callable, Dict, List, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, Module, MSELoss
from transformers.modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
QuestionAnsweringModelOutput,
SequenceClassifierOutputWithPast,
TokenClassifierOutput,
)
from transformers.models.bloom.modeling_bloom import (
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
)
from transformers.utils import logging
from torch.nn import Module
import colossalai.shardformer.layer as col_nn
from colossalai.pipeline.stage_manager import PipelineStageManager
from ..modeling.bloom import build_bloom_alibi_tensor_fn
from ..modeling.bloom import BloomPipelineForwards, build_bloom_alibi_tensor_fn
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
logger = logging.get_logger(__name__)
class BloomPolicy(Policy):
......@@ -150,6 +130,28 @@ class BloomPolicy(Policy):
target_key=model_cls)
return
def get_held_layers(self) -> List[Module]:
"""Get pipeline layers for current stage."""
assert self.pipeline_stage_manager is not None
if self.model.__class__.__name__ == 'BloomModel':
module = self.model
else:
module = self.model.transformer
stage_manager = self.pipeline_stage_manager
held_layers = []
layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.word_embeddings)
held_layers.append(module.word_embeddings_layernorm)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.h[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.ln_f)
return held_layers
class BloomModelPolicy(BloomPolicy):
......@@ -159,27 +161,17 @@ class BloomModelPolicy(BloomPolicy):
def module_policy(self):
policy = super().module_policy()
from transformers.models.bloom.modeling_bloom import BloomModel
self.set_pipeline_forward(model_cls=BloomModel, new_forward=bloom_model_forward, policy=policy)
if self.pipeline_stage_manager:
self.set_pipeline_forward(model_cls=BloomModel,
new_forward=BloomPipelineForwards.bloom_model_forward,
policy=policy)
return policy
def get_held_layers(self) -> List[Module]:
"""
get pipeline layers for current stage
"""
module = self.model
stage_manager = self.pipeline_stage_manager
held_layers = []
layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.word_embeddings)
held_layers.append(module.word_embeddings_layernorm)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.h[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.ln_f)
held_layers = super().get_held_layers()
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
......@@ -199,29 +191,23 @@ class BloomForCausalLMPolicy(BloomPolicy):
suffix="lm_head", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True)),
policy=policy,
target_key=BloomForCausalLM)
self.set_pipeline_forward(model_cls=BloomForCausalLM, new_forward=bloom_for_causal_lm_forward, policy=policy)
if self.pipeline_stage_manager:
self.set_pipeline_forward(model_cls=BloomForCausalLM,
new_forward=BloomPipelineForwards.bloom_for_causal_lm_forward,
policy=policy)
return policy
def get_held_layers(self) -> List[Module]:
"""Get pipeline layers for current stage."""
module = self.model
stage_manager = self.pipeline_stage_manager
held_layers = []
layers_per_stage = self.distribute_layers(len(module.transformer.h), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.transformer.word_embeddings)
held_layers.append(module.transformer.word_embeddings_layernorm)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.transformer.h[start_idx:end_idx])
held_layers = super().get_held_layers()
if stage_manager.is_last_stage():
held_layers.append(module.transformer.ln_f)
held_layers.append(module.lm_head)
held_layers.append(self.model.lm_head)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
bloom_model = self.model
if self.pipeline_stage_manager:
if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
if id(bloom_model.transformer.word_embeddings.weight) == id(bloom_model.lm_head.weight):
# tie weights
return [{
......@@ -243,25 +229,18 @@ class BloomForSequenceClassificationPolicy(BloomPolicy):
suffix="score", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True)),
policy=policy,
target_key=BloomForSequenceClassification)
self.set_pipeline_forward(model_cls=BloomForSequenceClassification,
new_forward=bloom_for_sequence_classification_forward,
policy=policy)
if self.pipeline_stage_manager:
self.set_pipeline_forward(model_cls=BloomForSequenceClassification,
new_forward=BloomPipelineForwards.bloom_for_sequence_classification_forward,
policy=policy)
return policy
def get_held_layers(self) -> List[Module]:
"""Get pipeline layers for current stage."""
module = self.model
stage_manager = self.pipeline_stage_manager
held_layers = []
layers_per_stage = self.distribute_layers(len(module.transformer.h), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.transformer.word_embeddings)
held_layers.append(module.transformer.word_embeddings_layernorm)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.transformer.h[start_idx:end_idx])
held_layers = super().get_held_layers()
if stage_manager.is_last_stage():
held_layers.append(module.transformer.ln_f)
held_layers.append(module.score)
held_layers.append(self.model.score)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
......@@ -288,28 +267,20 @@ class BloomForTokenClassificationPolicy(BloomPolicy):
],
policy=policy,
target_key=BloomForTokenClassification)
self.set_pipeline_forward(model_cls=BloomForTokenClassification,
new_forward=bloom_for_token_classification_forward,
policy=policy)
if self.pipeline_stage_manager:
self.set_pipeline_forward(model_cls=BloomForTokenClassification,
new_forward=BloomPipelineForwards.bloom_for_token_classification_forward,
policy=policy)
return policy
def get_held_layers(self) -> List[Module]:
"""Get pipeline layers for current stage."""
module = self.model
stage_manager = self.pipeline_stage_manager
held_layers = []
layers_per_stage = self.distribute_layers(len(module.transformer.h), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.transformer.word_embeddings)
held_layers.append(module.transformer.word_embeddings_layernorm)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.transformer.h[start_idx:end_idx])
held_layers = super().get_held_layers()
if stage_manager.is_last_stage():
held_layers.append(module.transformer.ln_f)
held_layers.append(module.dropout)
held_layers.append(module.classifier)
held_layers.append(self.model.dropout)
held_layers.append(self.model.classifier)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
......@@ -322,605 +293,20 @@ class BloomForQuestionAnsweringPolicy(BloomPolicy):
def module_policy(self):
from transformers.models.bloom.modeling_bloom import BloomForQuestionAnswering
policy = super().module_policy()
self.set_pipeline_forward(model_cls=BloomForQuestionAnswering,
new_forward=bloom_for_question_answering_forward,
policy=policy)
if self.pipeline_stage_manager:
self.set_pipeline_forward(model_cls=BloomForQuestionAnswering,
new_forward=BloomPipelineForwards.bloom_for_question_answering_forward,
policy=policy)
return policy
def get_held_layers(self) -> List[Module]:
"""Get pipeline layers for current stage."""
module = self.model
held_layers = super().get_held_layers()
stage_manager = self.pipeline_stage_manager
held_layers = []
layers_per_stage = self.distribute_layers(len(module.transformer.h), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.transformer.word_embeddings)
held_layers.append(module.transformer.word_embeddings_layernorm)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.transformer.h[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.transformer.ln_f)
held_layers.append(module.qa_outputs)
held_layers.append(self.model.qa_outputs)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
"""No shared params in bloom for question answering model"""
return []
def bloom_model_forward(
self: BloomModel,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: 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,
**deprecated_arguments,
) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
if deprecated_arguments.pop("position_ids", False) is not False:
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
warnings.warn(
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
" passing `position_ids`.",
FutureWarning,
)
if len(deprecated_arguments) > 0:
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
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)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# add warnings here
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
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape batch_size x num_heads x N x N
# head_mask has shape n_layer x batch x num_heads x N x N
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
# case: First stage of training
if stage_manager.is_first_stage():
# check input_ids and inputs_embeds
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
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
hidden_states = self.word_embeddings_layernorm(inputs_embeds)
# initialize in the first stage and then pass to the next stage
else:
input_shape = hidden_states.shape[:-1]
batch_size, seq_length = input_shape
# extra recording tensor should be generated in the first stage
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
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
if past_key_values is None:
past_key_values = tuple([None] * len(self.h))
# Compute alibi tensor: check build_alibi_tensor documentation,build for every stage
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values[0] is not None:
past_key_values_length = past_key_values[0][0].shape[2] # source_len
seq_length_with_past = seq_length_with_past + past_key_values_length
if attention_mask is None:
attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
else:
attention_mask = attention_mask.to(hidden_states.device)
alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
# causal_mask is constructed every stage and its input is passed through different stages
causal_mask = self._prepare_attn_mask(
attention_mask,
input_shape=(batch_size, seq_length),
past_key_values_length=past_key_values_length,
)
start_idx, end_idx = stage_index[0], stage_index[1]
for i, (block, layer_past) in enumerate(zip(self.h[start_idx:end_idx], past_key_values[start_idx:end_idx])):
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=use_cache, output_attentions=output_attentions)
return custom_forward
outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
alibi,
causal_mask,
layer_past,
head_mask[i],
)
else:
outputs = block(
hidden_states,
layer_past=layer_past,
attention_mask=causal_mask,
head_mask=head_mask[i],
use_cache=use_cache,
output_attentions=output_attentions,
alibi=alibi,
)
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 stage_manager.is_last_stage():
# Add last hidden state
hidden_states = self.ln_f(hidden_states)
# TODO: deal with all_hidden_states, all_self_attentions, presents
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] if v is not None)
# attention_mask is not returned ; presents = past_key_values
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
else:
# always return dict for imediate stage
return {'hidden_states': hidden_states}
def bloom_for_causal_lm_forward(self: 'BloomForCausalLM',
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = 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,
**deprecated_arguments):
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]`
"""
if deprecated_arguments.pop("position_ids", False) is not False:
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
warnings.warn(
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
" passing `position_ids`.",
FutureWarning,
)
if len(deprecated_arguments) > 0:
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
transformer_outputs = bloom_model_forward(self.transformer,
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
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)
past_key_values = None
all_hidden_states = None
all_self_attentions = None
all_cross_attentions = None
if stage_manager.is_last_stage():
hidden_states = transformer_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()
batch_size, seq_length, vocab_size = shift_logits.shape
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(batch_size * seq_length, vocab_size),
shift_labels.view(batch_size * seq_length))
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
else:
hidden_states = transformer_outputs.get('hidden_states')
return {'hidden_states': hidden_states}
def bloom_for_sequence_classification_forward(
self: BloomForSequenceClassification,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = 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,
**deprecated_arguments,
):
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).
"""
if deprecated_arguments.pop("position_ids", False) is not False:
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
warnings.warn(
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
" passing `position_ids`.",
FutureWarning,
)
if len(deprecated_arguments) > 0:
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
transformer_outputs = bloom_model_forward(
self.transformer,
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
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,
)
past_key_values = None
all_hidden_states = None
all_self_attentions = None
all_cross_attentions = None
if stage_manager.is_last_stage():
batch_size = hidden_states.shape[0]
# update batch size
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
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(
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, labels)
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(pooled_logits, labels)
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
else:
hidden_states = transformer_outputs.get('hidden_states')
return {'hidden_states': hidden_states}
def bloom_for_token_classification_forward(
self: BloomForTokenClassification,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = 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,
**deprecated_arguments,
):
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).
"""
if deprecated_arguments.pop("position_ids", False) is not False:
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
warnings.warn(
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
" passing `position_ids`.",
FutureWarning,
)
if len(deprecated_arguments) > 0:
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
transformer_outputs = bloom_model_forward(
self.transformer,
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
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,
)
past_key_values = None
all_hidden_states = None
all_self_attentions = None
all_cross_attentions = None
if stage_manager.is_last_stage():
hidden_states = transformer_outputs[0]
hidden_states = self.dropout(hidden_states)
logits = self.classifier(hidden_states)
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
batch_size, seq_length = labels.shape
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length))
if not return_dict:
output = (logits,) + transformer_outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
else:
hidden_states = transformer_outputs.get('hidden_states')
return {'hidden_states': hidden_states}
def bloom_for_question_answering_forward(
self: BloomForQuestionAnswering,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = 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,
):
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
outputs = bloom_model_forward(
self.transformer,
input_ids,
attention_mask=attention_mask,
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,
)
past_key_values = None
all_hidden_states = None
all_self_attentions = None
all_cross_attentions = None
if stage_manager.is_last_stage():
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
hidden_states = outputs.get('hidden_states')
return {'hidden_states': hidden_states}
colossalai/shardformer/policies/llama.py
View file @ 18ebcf40
from functools import partial
from typing import Dict, List, Optional, Union
from typing import Callable, Dict, List, Union
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, Module, MSELoss
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
SequenceClassifierOutputWithPast,
)
from transformers.models.llama.modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel
from transformers.utils import logging
from colossalai.pipeline.stage_manager import PipelineStageManager
from torch.nn import Module
from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D
from ..modeling.llama import LlamaPipelineForwards
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
logger = logging.get_logger(__name__)
__all__ = ['LlamaPolicy', 'LlamaForCausalLMPolicy', 'LlamaForSequenceClassificationPolicy']
......@@ -119,32 +109,35 @@ class LlamaPolicy(Policy):
def postprocess(self):
return self.model
class LlamaModelPolicy(LlamaPolicy):
def __init__(self) -> None:
super().__init__()
def module_policy(self):
policy = super().module_policy()
from transformers.models.llama.modeling_llama import LlamaModel
def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None:
"""If under pipeline parallel setting, replacing the original forward method of huggingface
to customized forward method, and add this changing to policy."""
if self.pipeline_stage_manager:
# set None as default
stage_manager = self.pipeline_stage_manager
layers_per_stage = Policy.distribute_layers(len(self.model.layers), stage_manager.num_stages)
if self.model.__class__.__name__ == "LlamaModel":
module = self.model
else:
module = self.model.model
layers_per_stage = Policy.distribute_layers(len(module.layers), stage_manager.num_stages)
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
method_replacement = {
'forward': partial(llama_model_forward, stage_manager=stage_manager, stage_index=stage_index)
}
method_replacement = {'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)}
self.append_or_create_method_replacement(description=method_replacement,
policy=policy,
target_key=LlamaModel)
return policy
target_key=model_cls)
return
def get_held_layers(self) -> List[Module]:
"""Get pipeline layers for current stage."""
module = self.model
assert self.pipeline_stage_manager is not None
if self.model.__class__.__name__ == 'LlamaModel':
module = self.model
else:
module = self.model.model
stage_manager = self.pipeline_stage_manager
held_layers = []
layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages)
if stage_manager.is_first_stage():
......@@ -153,6 +146,28 @@ class LlamaModelPolicy(LlamaPolicy):
held_layers.extend(module.layers[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.norm)
return held_layers
class LlamaModelPolicy(LlamaPolicy):
def __init__(self) -> None:
super().__init__()
def module_policy(self):
policy = super().module_policy()
from transformers.models.llama.modeling_llama import LlamaModel
if self.pipeline_stage_manager:
# set None as default
self.set_pipeline_forward(model_cls=LlamaModel,
new_forward=LlamaPipelineForwards.llama_model_forward,
policy=policy)
return policy
def get_held_layers(self) -> List[Module]:
"""Get pipeline layers for current stage."""
held_layers = super().get_held_layers()
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
......@@ -180,40 +195,30 @@ class LlamaForCausalLMPolicy(LlamaPolicy):
if self.pipeline_stage_manager:
# set None as default
stage_manager = self.pipeline_stage_manager
layers_per_stage = Policy.distribute_layers(len(self.model.model.layers), stage_manager.num_stages)
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
method_replacement = {
'forward': partial(llama_for_causal_lm_forward, stage_manager=stage_manager, stage_index=stage_index)
}
self.append_or_create_method_replacement(description=method_replacement,
policy=policy,
target_key=LlamaForCausalLM)
self.set_pipeline_forward(model_cls=LlamaForCausalLM,
new_forward=LlamaPipelineForwards.llama_for_causal_lm_forward,
policy=policy)
return policy
def get_held_layers(self) -> List[Module]:
"""Get pipeline layers for current stage."""
module = self.model
stage_manager = self.pipeline_stage_manager
held_layers = []
layers_per_stage = self.distribute_layers(len(module.model.layers), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.model.embed_tokens)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.model.layers[start_idx:end_idx])
held_layers = super().get_held_layers()
if stage_manager.is_last_stage():
held_layers.append(module.model.norm)
held_layers.append(module.lm_head)
held_layers.append(self.model.lm_head)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
llama_model = self.model.model
if id(llama_model.embed_tokens.weight) == id(self.model.lm_head.weight):
# tie weights
return [{
0: llama_model.embed_tokens.weight,
self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight
}]
if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
if id(llama_model.embed_tokens.weight) == id(
self.model.lm_head.weight) and self.pipeline_stage_manager.num_stages > 1:
# tie weights
return [{
0: llama_model.embed_tokens.weight,
self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight
}]
return []
......@@ -237,405 +242,19 @@ class LlamaForSequenceClassificationPolicy(LlamaPolicy):
# to be confirmed
if self.pipeline_stage_manager:
# set None as default
stage_manager = self.pipeline_stage_manager
layers_per_stage = Policy.distribute_layers(len(self.model.model.layers), stage_manager.num_stages)
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
method_replacement = {
'forward':
partial(llama_for_sequence_classification_forward,
stage_manager=stage_manager,
stage_index=stage_index)
}
self.append_or_create_method_replacement(description=method_replacement,
policy=policy,
target_key=LlamaForSequenceClassification)
self.set_pipeline_forward(model_cls=LlamaForSequenceClassification,
new_forward=LlamaPipelineForwards.llama_for_sequence_classification_forward,
policy=policy)
return policy
def get_held_layers(self) -> List[Module]:
"""Get pipeline layers for current stage."""
module = self.model
stage_manager = self.pipeline_stage_manager
held_layers = []
layers_per_stage = self.distribute_layers(len(module.model.layers), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.model.embed_tokens)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.model.layers[start_idx:end_idx])
held_layers = super().get_held_layers()
if stage_manager.is_last_stage():
held_layers.append(module.model.norm)
held_layers.append(module.score)
held_layers.append(self.model.score)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
"""No shared params in llama for sequence classification model"""
return []
def llama_model_forward(
self: LlamaModel,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: 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,
):
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)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if stage_manager.is_first_stage():
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
hidden_states = inputs_embeds
else:
input_shape = hidden_states.shape[:-1]
batch_size, seq_length = input_shape
device = hidden_states.device
seq_length_with_past = seq_length
past_key_values_length = 0
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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 past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if position_ids is None:
position_ids = torch.arange(past_key_values_length,
seq_length + past_key_values_length,
dtype=torch.long,
device=device)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
else:
position_ids = position_ids.view(-1, seq_length).long()
# embed positions, for the first stage, hidden_states is the input embeddings,
# for the other stages, hidden_states is the output of the previous stage
if attention_mask is None:
attention_mask = torch.ones((batch_size, seq_length_with_past), dtype=torch.bool, device=hidden_states.device)
attention_mask = self._prepare_decoder_attention_mask(attention_mask, (batch_size, seq_length), hidden_states,
past_key_values_length)
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
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
start_idx, end_idx = stage_index[0], stage_index[1]
for idx, decoder_layer in enumerate(self.layers[start_idx:end_idx]):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, output_attentions, None)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(decoder_layer),
hidden_states,
attention_mask,
position_ids,
None,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if stage_manager.is_last_stage():
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if stage_manager.is_last_stage():
if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
# always return dict for imediate stage
return {'hidden_states': hidden_states}
def llama_for_causal_lm_forward(
self: LlamaForCausalLM,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[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,
):
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, LlamaForCausalLM
>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
>>> prompt = "Hey, are you consciours? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
```"""
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
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = llama_model_forward(
self.model,
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
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,
)
past_key_values = None
all_hidden_states = None
all_self_attentions = None
all_cross_attentions = None
if stage_manager.is_last_stage():
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
hidden_states = outputs.get('hidden_states')
return {'hidden_states': hidden_states}
def llama_for_sequence_classification_forward(
self: LlamaForSequenceClassification,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[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,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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 return_dict:
logger.warning_once('return_dict is not supported for pipeline models at the moment')
return_dict = False
transformer_outputs = llama_model_forward(
self.model,
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
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 input_ids is not None:
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
batch_size = inputs_embeds.shape[0]
else:
batch_size = hidden_states.shape[0]
if stage_manager.is_last_stage():
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
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
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
loss = None
if labels is not None:
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(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,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
else:
hidden_states = transformer_outputs.get('hidden_states')
return {'hidden_states': hidden_states}
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