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[pipeline] fix return_dict/fix pure_pipeline_test (#4331)

pull/4445/head
Baizhou Zhang 2023-07-27 14:53:20 +08:00 committed by Hongxin Liu
parent 411cf1d2db
commit da3cef27ad
5 changed files with 29 additions and 53 deletions
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33
colossalai/shardformer/modeling/bert.py
View File

@ -1,3 +1,4 @@
import warnings
from typing import Any, Dict, List, Optional, Tuple
import torch
@ -277,9 +278,6 @@ class BertPipelineForwards:
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,
@ -387,9 +385,6 @@ class BertPipelineForwards:
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,
@ -478,9 +473,6 @@ class BertPipelineForwards:
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,
@ -579,16 +571,15 @@ class BertPipelineForwards:
FutureWarning,
)
labels = kwargs.pop("next_sentence_label")
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,
@ -661,10 +652,6 @@ class BertPipelineForwards:
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,
@ -753,10 +740,6 @@ class BertPipelineForwards:
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,
@ -832,10 +815,6 @@ class BertPipelineForwards:
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]
@ -928,10 +907,6 @@ class BertPipelineForwards:
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,

12
colossalai/shardformer/modeling/bloom.py
View File

@ -313,9 +313,6 @@ class BloomPipelineForwards:
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,
@ -411,9 +408,6 @@ class BloomPipelineForwards:
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,
@ -537,9 +531,6 @@ class BloomPipelineForwards:
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,
@ -626,9 +617,6 @@ class BloomPipelineForwards:
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,

2
colossalai/shardformer/modeling/gpt2.py
View File

@ -52,6 +52,8 @@ class GPT2PipelineForwards:
# This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2Model.forward.
# Please refer to original code of transformers for more details.
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
logger = logging.get_logger(__name__)
# Preprocess passed in arguments

28
colossalai/shardformer/policies/opt.py
View File

@ -8,6 +8,18 @@ import torch
import torch.nn as nn
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
QuestionAnsweringModelOutput,
SequenceClassifierOutputWithPast,
)
from transformers.models.opt.modeling_opt import (
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
)
from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.shardformer.layer import FusedLayerNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D
@ -317,7 +329,7 @@ class OPTPipelineForwards:
@staticmethod
def opt_model_forward(
self: 'OPTModel',
self: OPTModel,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
@ -330,7 +342,7 @@ class OPTPipelineForwards:
stage_manager: Optional[PipelineStageManager] = None,
hidden_states: Optional[torch.FloatTensor] = None,
stage_index: Optional[List[int]] = None,
) -> Union[Tuple, 'BaseModelOutputWithPast']:
) -> Union[Tuple, BaseModelOutputWithPast]:
'''
This forward method is modified based on transformers.models.opt.modeling_opt.OPTModel.forward
'''
@ -506,7 +518,7 @@ class OPTPipelineForwards:
@staticmethod
def opt_for_causal_lm_forward(
self: 'OPTForCausalLM',
self: OPTForCausalLM,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
@ -520,7 +532,7 @@ class OPTPipelineForwards:
stage_manager: Optional[PipelineStageManager] = None,
hidden_states: Optional[torch.FloatTensor] = None,
stage_index: Optional[List[int]] = None,
) -> Union[Tuple, 'CausalLMOutputWithPast']:
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
@ -646,7 +658,7 @@ class OPTPipelineForwards:
@staticmethod
def opt_for_sequence_classification_forward(
self: 'OPTForSequenceClassification',
self: OPTForSequenceClassification,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
@ -660,7 +672,7 @@ class OPTPipelineForwards:
stage_manager: Optional[PipelineStageManager] = None,
hidden_states: Optional[torch.FloatTensor] = None,
stage_index: Optional[List[int]] = None,
) -> Union[Tuple, 'SequenceClassifierOutputWithPast']:
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
@ -746,7 +758,7 @@ class OPTPipelineForwards:
@staticmethod
def opt_for_question_answering_forward(
self: 'OPTForQuestionAnswering',
self: OPTForQuestionAnswering,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
@ -761,7 +773,7 @@ class OPTPipelineForwards:
stage_manager: Optional[PipelineStageManager] = None,
hidden_states: Optional[torch.FloatTensor] = None,
stage_index: Optional[List[int]] = None,
) -> Union[Tuple, 'QuestionAnsweringModelOutput']:
) -> Union[Tuple, QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.

7
tests/test_shardformer/test_model/test_pure_pipeline.py
View File

@ -1,6 +1,5 @@
import copy
import random
from contextlib import nullcontext
from typing import Any, Callable, Iterator, List, Optional, Tuple
import numpy as np
@ -100,8 +99,8 @@ class data_loader():
return torch.ones((4, 128), dtype=torch.int).cuda() * 10
def loss(x, y):
return (x[0].float().mean() - y[0].float().mean())
def loss(y, x):
return (y[0].float().mean() - x[0].float().mean())
@parameterize('enable_fused_normalization', [False])
@ -137,7 +136,7 @@ def run_llama_test(enable_fused_normalization, enable_tensor_parallelism, use_la
batch = next(data_iter)
with torch.no_grad():
y = model_copy(batch)
org_loss = loss(batch, y)
org_loss = loss(y, batch)
optimizer = torch.optim.AdamW(org_model.parameters(), lr=1e-3)
schedule = OneForwardOneBackwardSchedule(num_microbatches, stage_manager)
shard_config = ShardConfig(enable_fused_normalization=enable_fused_normalization,