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ColossalAI/colossalai/shardformer/policies/gpt2.py

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from functools import partial
from typing import Callable, Dict, List
from torch import Tensor, nn
import colossalai.shardformer.layer as col_nn
from .._utils import getattr_, setattr_
from ..modeling.gpt2 import GPT2PipelineForwards, get_gpt2_flash_attention_forward, gpt2_sequence_parallel_forward_fn
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
__all__ = [
'GPT2Policy', 'GPT2ModelPolicy', 'GPT2LMHeadModelPolicy', 'GPT2DoubleHeadsModelPolicy',
'GPT2ForTokenClassificationPolicy', 'GPT2ForSequenceClassificationPolicy'
]
class GPT2Policy(Policy):
def config_sanity_check(self):
pass
def preprocess(self):
# reshape the embedding layer
r"""
Reshape the Embedding layer to make the embedding dimension divisible by world_size
"""
if self.shard_config.enable_tensor_parallelism:
vocab_size = self.model.config.vocab_size
world_size = self.shard_config.tensor_parallel_size
if vocab_size % world_size != 0:
new_vocab_size = vocab_size + world_size - vocab_size % world_size
self.model.resize_token_embeddings(new_vocab_size)
return self.model
def module_policy(self):
from transformers.models.gpt2.modeling_gpt2 import GPT2Attention, GPT2Block, GPT2Model
policy = {}
use_sequence_parallel = self.shard_config.enable_sequence_parallelism
overlap = self.shard_config.enable_sequence_overlap
if self.shard_config.enable_tensor_parallelism:
policy[GPT2Model] = ModulePolicyDescription(sub_module_replacement=[
SubModuleReplacementDescription(
suffix="wte",
target_module=col_nn.VocabParallelEmbedding1D,
),
SubModuleReplacementDescription(
suffix="drop",
target_module=col_nn.DropoutForParallelInput,
),
])
policy[GPT2Block] = ModulePolicyDescription(
attribute_replacement={
"attn.embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
"attn.split_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
"attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
},
sub_module_replacement=[
SubModuleReplacementDescription(
suffix="attn.c_attn",
target_module=col_nn.GPT2FusedLinearConv1D_Col,
kwargs={
"n_fused": 3,
"seq_parallel": use_sequence_parallel,
"overlap": overlap
},
),
SubModuleReplacementDescription(suffix="attn.c_proj",
target_module=col_nn.GPT2FusedLinearConv1D_Row,
kwargs={
"seq_parallel": use_sequence_parallel,
}),
SubModuleReplacementDescription(
suffix="mlp.c_fc",
target_module=col_nn.GPT2FusedLinearConv1D_Col,
kwargs={
"n_fused": 1,
"seq_parallel": use_sequence_parallel,
"overlap": overlap
},
),
SubModuleReplacementDescription(suffix="mlp.c_proj",
target_module=col_nn.GPT2FusedLinearConv1D_Row,
kwargs={
"seq_parallel": use_sequence_parallel,
}),
SubModuleReplacementDescription(
suffix="attn.attn_dropout",
target_module=col_nn.DropoutForParallelInput,
),
SubModuleReplacementDescription(
suffix="attn.resid_dropout",
target_module=col_nn.DropoutForParallelInput,
),
SubModuleReplacementDescription(
suffix="mlp.dropout",
target_module=col_nn.DropoutForParallelInput,
),
])
# optimization configuration
if self.shard_config.enable_fused_normalization:
self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
suffix="ln_f",
target_module=col_nn.FusedLayerNorm,
),
policy=policy,
target_key=GPT2Model)
self.append_or_create_submodule_replacement(description=[
SubModuleReplacementDescription(
suffix="ln_1",
target_module=col_nn.FusedLayerNorm,
),
SubModuleReplacementDescription(
suffix="ln_2",
target_module=col_nn.FusedLayerNorm,
),
SubModuleReplacementDescription(suffix="ln_cross_attn",
target_module=col_nn.FusedLayerNorm,
ignore_if_not_exist=True)
],
policy=policy,
target_key=GPT2Block)
if self.shard_config.enable_flash_attention:
self.append_or_create_method_replacement(description={
'forward': get_gpt2_flash_attention_forward(),
},
policy=policy,
target_key=GPT2Attention)
if self.shard_config.enable_sequence_parallelism:
policy[GPT2Model].method_replacement = {"forward": gpt2_sequence_parallel_forward_fn(self.shard_config)}
return policy
def postprocess(self):
return self.model
def get_held_layers(self) -> List[nn.Module]:
"""Get pipeline layers for current stage."""
assert self.pipeline_stage_manager is not None
if self.model.__class__.__name__ == 'GPT2Model':
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.wte)
held_layers.append(module.wpe)
held_layers.append(module.drop)
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
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 not self.pipeline_stage_manager:
raise ValueError("set_pipeline_forward method can only be called when pipeline parallel is enabled.")
stage_manager = self.pipeline_stage_manager
if self.model.__class__.__name__ == 'GPT2Model':
module = self.model
else:
module = self.model.transformer
layers_per_stage = Policy.distribute_layers(len(module.h), stage_manager.num_stages)
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
method_replacement = {
'forward':
partial(new_forward,
stage_manager=stage_manager,
stage_index=stage_index,
shard_config=self.shard_config)
}
self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls)
# GPT2Model
class GPT2ModelPolicy(GPT2Policy):
def __init__(self) -> None:
super().__init__()
def module_policy(self):
from transformers.models.gpt2.modeling_gpt2 import GPT2Model
policy = super().module_policy()
if self.pipeline_stage_manager is not None:
self.set_pipeline_forward(model_cls=GPT2Model,
new_forward=GPT2PipelineForwards.gpt2_model_forward,
policy=policy)
return policy
def get_held_layers(self) -> List[nn.Module]:
return super().get_held_layers()
def get_shared_params(self) -> List[Dict[int, Tensor]]:
"""No shared params in GPT2Model."""
return []
# GPT2LMHeadModel
class GPT2LMHeadModelPolicy(GPT2Policy):
def __init__(self) -> None:
super().__init__()
def module_policy(self):
from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
module_policy = super().module_policy()
if self.shard_config.enable_tensor_parallelism:
addon_module = {
GPT2LMHeadModel:
ModulePolicyDescription(sub_module_replacement=[
SubModuleReplacementDescription(
suffix="lm_head", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True})
])
}
module_policy.update(addon_module)
if self.pipeline_stage_manager is not None:
self.set_pipeline_forward(model_cls=GPT2LMHeadModel,
new_forward=GPT2PipelineForwards.gpt2_lmhead_model_forward,
policy=module_policy)
return module_policy
def get_held_layers(self) -> List[nn.Module]:
held_layers = super().get_held_layers()
if self.pipeline_stage_manager.is_last_stage():
held_layers.append(self.model.lm_head)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
'''The weights of wte and lm_head are shared.'''
module = self.model
stage_manager = self.pipeline_stage_manager
if stage_manager is not None:
if stage_manager.num_stages > 1 and id(module.transformer.wte.weight) == id(module.lm_head.weight):
first_stage, last_stage = 0, stage_manager.num_stages - 1
return [{first_stage: module.transformer.wte.weight, last_stage: module.lm_head.weight}]
return []
# GPT2DoubleHeadsModel
class GPT2DoubleHeadsModelPolicy(GPT2Policy):
def __init__(self) -> None:
super().__init__()
def module_policy(self):
from transformers.models.gpt2.modeling_gpt2 import GPT2DoubleHeadsModel
module_policy = super().module_policy()
if self.shard_config.enable_tensor_parallelism:
addon_module = {
GPT2DoubleHeadsModel:
ModulePolicyDescription(sub_module_replacement=[
SubModuleReplacementDescription(
suffix="lm_head", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True})
])
}
module_policy.update(addon_module)
if self.pipeline_stage_manager is not None:
self.set_pipeline_forward(model_cls=GPT2DoubleHeadsModel,
new_forward=GPT2PipelineForwards.gpt2_double_heads_model_forward,
policy=module_policy)
return module_policy
def get_held_layers(self) -> List[nn.Module]:
held_layers = super().get_held_layers()
if self.pipeline_stage_manager.is_last_stage():
multiple_choice_head = self.model.multiple_choice_head
held_layers.append(self.model.lm_head)
held_layers.append(multiple_choice_head.summary)
held_layers.append(multiple_choice_head.activation)
held_layers.append(multiple_choice_head.first_dropout)
held_layers.append(multiple_choice_head.last_dropout)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
'''The weights of wte and lm_head are shared.'''
module = self.model
stage_manager = self.pipeline_stage_manager
if stage_manager is not None:
if stage_manager.num_stages > 1 and id(module.transformer.wte.weight) == id(module.lm_head.weight):
first_stage, last_stage = 0, stage_manager.num_stages - 1
return [{first_stage: module.transformer.wte.weight, last_stage: module.lm_head.weight}]
return []
# GPT2ForQuestionAnswering
class GPT2ForQuestionAnsweringPolicy(GPT2Policy):
def __init__(self) -> None:
super().__init__()
def module_policy(self):
from transformers.models.gpt2.modeling_gpt2 import GPT2ForQuestionAnswering
module_policy = super().module_policy()
if self.pipeline_stage_manager is not None:
self.set_pipeline_forward(model_cls=GPT2ForQuestionAnswering,
new_forward=GPT2PipelineForwards.gpt2_for_question_answering_forward,
policy=module_policy)
return module_policy
def get_held_layers(self) -> List[nn.Module]:
held_layers = super().get_held_layers()
if self.pipeline_stage_manager.is_last_stage():
held_layers.append(self.model.qa_outputs)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
'''No shared_params in gpt2 for QA.'''
return []
# GPT2ForTokenClassification
class GPT2ForTokenClassificationPolicy(GPT2Policy):
def __init__(self) -> None:
super().__init__()
def module_policy(self):
from transformers.models.gpt2.modeling_gpt2 import GPT2ForTokenClassification
module_policy = super().module_policy()
if self.shard_config.enable_tensor_parallelism:
addon_module = {
GPT2ForTokenClassification:
ModulePolicyDescription(sub_module_replacement=[
SubModuleReplacementDescription(suffix="dropout", target_module=col_nn.DropoutForParallelInput)
])
}
module_policy.update(addon_module)
if self.pipeline_stage_manager is not None:
self.set_pipeline_forward(model_cls=GPT2ForTokenClassification,
new_forward=GPT2PipelineForwards.gpt2_for_token_classification_forward,
policy=module_policy)
return module_policy
def get_held_layers(self) -> List[nn.Module]:
held_layers = super().get_held_layers()
if self.pipeline_stage_manager.is_last_stage():
held_layers.append(self.model.dropout)
held_layers.append(self.model.classifier)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
"""No shared params in GPT2ForTokenClassification."""
return []
# GPT2ForSequenceClassification
class GPT2ForSequenceClassificationPolicy(GPT2Policy):
def __init__(self) -> None:
super().__init__()
def module_policy(self):
from transformers.models.gpt2.modeling_gpt2 import GPT2ForSequenceClassification
module_policy = super().module_policy()
if self.pipeline_stage_manager is not None:
self.set_pipeline_forward(model_cls=GPT2ForSequenceClassification,
new_forward=GPT2PipelineForwards.gpt2_for_sequence_classification_forward,
policy=module_policy)
return module_policy
def get_held_layers(self) -> List[nn.Module]:
held_layers = super().get_held_layers()
if self.pipeline_stage_manager.is_last_stage():
held_layers.append(self.model.score)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
"""No shared params in GPT2ForTokenClassification."""
return []
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