mirror of https://github.com/hpcaitech/ColossalAI
72 lines
3.3 KiB
Python
72 lines
3.3 KiB
Python
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from functools import partial
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from typing import Callable, Dict, List
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from torch import Tensor, nn
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import colossalai.shardformer.layer as col_nn
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from colossalai.shardformer.policies.base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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from colossalai.shardformer.policies.gpt2 import GPT2Policy
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from ..modeling.gpt2 import GPT2PipelineForwards
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class GPT2LMHeadModelPipelinePolicy(GPT2Policy):
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def __init__(self) -> None:
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super().__init__()
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def module_policy(self):
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from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
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module_policy = super().module_policy()
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if self.shard_config.enable_tensor_parallelism:
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addon_module = {
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GPT2LMHeadModel:
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ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="lm_head", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True})
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])
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}
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module_policy.update(addon_module)
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if self.pipeline_stage_manager is not None:
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self.set_pipeline_forward(model_cls=GPT2LMHeadModel,
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new_forward=GPT2PipelineForwards.gpt2_lmhead_model_forward,
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policy=module_policy)
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return module_policy
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def get_held_layers(self) -> List[nn.Module]:
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held_layers = super().get_held_layers()
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# make the tie weight lm_head and embedding in the same device to save memory
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# if self.pipeline_stage_manager.is_first_stage():
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if self.pipeline_stage_manager.is_first_stage():
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held_layers.append(self.model.lm_head)
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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'''The weights of wte and lm_head are shared.'''
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module = self.model
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stage_manager = self.pipeline_stage_manager
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if stage_manager is not None:
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if stage_manager.num_stages > 1 and id(module.transformer.wte.weight) == id(module.lm_head.weight):
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first_stage, last_stage = 0, stage_manager.num_stages - 1
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return [{first_stage: module.transformer.wte.weight, last_stage: module.lm_head.weight}]
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return []
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def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None:
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"""If under pipeline parallel setting, replacing the original forward method of huggingface
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to customized forward method, and add this changing to policy."""
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if not self.pipeline_stage_manager:
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raise ValueError("set_pipeline_forward method can only be called when pipeline parallel is enabled.")
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stage_manager = self.pipeline_stage_manager
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if self.model.__class__.__name__ == 'GPT2Model':
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module = self.model
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else:
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module = self.model.transformer
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layers_per_stage = Policy.distribute_layers(len(module.h), stage_manager.num_stages)
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stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
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method_replacement = {'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)}
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self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls)
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