mirror of https://github.com/hpcaitech/ColossalAI
406 lines
16 KiB
Python
406 lines
16 KiB
Python
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 ..modeling.gpt2 import GPT2PipelineForwards, get_gpt2_flash_attention_forward, gpt2_sequence_parallel_forward_fn
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from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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__all__ = [
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"GPT2Policy",
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"GPT2ModelPolicy",
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"GPT2LMHeadModelPolicy",
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"GPT2DoubleHeadsModelPolicy",
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"GPT2ForTokenClassificationPolicy",
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"GPT2ForSequenceClassificationPolicy",
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]
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class GPT2Policy(Policy):
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def config_sanity_check(self):
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pass
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def preprocess(self):
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# reshape the embedding layer
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r"""
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Reshape the Embedding layer to make the embedding dimension divisible by world_size
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"""
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if self.shard_config.enable_tensor_parallelism:
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vocab_size = self.model.config.vocab_size
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world_size = self.shard_config.tensor_parallel_size
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if vocab_size % world_size != 0:
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new_vocab_size = vocab_size + world_size - vocab_size % world_size
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self.model.resize_token_embeddings(new_vocab_size)
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return self.model
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def module_policy(self):
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from transformers.models.gpt2.modeling_gpt2 import GPT2Attention, GPT2Block, GPT2Model
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policy = {}
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if self.shard_config.enable_fused_normalization:
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norm_cls = col_nn.FusedLayerNorm
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else:
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norm_cls = col_nn.LayerNorm
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use_sequence_parallel = self.shard_config.enable_sequence_parallelism
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overlap = self.shard_config.enable_sequence_overlap
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if self.shard_config.enable_tensor_parallelism:
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policy[GPT2Model] = ModulePolicyDescription(
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="wte",
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target_module=col_nn.VocabParallelEmbedding1D,
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),
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SubModuleReplacementDescription(
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suffix="drop",
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target_module=col_nn.DropoutForParallelInput,
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),
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]
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)
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policy[GPT2Block] = ModulePolicyDescription(
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attribute_replacement={
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"attn.embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
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"attn.split_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
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"attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
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},
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="attn.c_attn",
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target_module=col_nn.GPT2FusedLinearConv1D_Col,
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kwargs={"n_fused": 3, "seq_parallel": use_sequence_parallel, "overlap": overlap},
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),
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SubModuleReplacementDescription(
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suffix="attn.c_proj",
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target_module=col_nn.GPT2FusedLinearConv1D_Row,
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kwargs={
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"seq_parallel": use_sequence_parallel,
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},
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),
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SubModuleReplacementDescription(
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suffix="mlp.c_fc",
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target_module=col_nn.GPT2FusedLinearConv1D_Col,
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kwargs={"n_fused": 1, "seq_parallel": use_sequence_parallel, "overlap": overlap},
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),
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SubModuleReplacementDescription(
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suffix="mlp.c_proj",
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target_module=col_nn.GPT2FusedLinearConv1D_Row,
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kwargs={
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"seq_parallel": use_sequence_parallel,
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},
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),
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SubModuleReplacementDescription(
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suffix="attn.attn_dropout",
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target_module=col_nn.DropoutForParallelInput,
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),
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SubModuleReplacementDescription(
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suffix="attn.resid_dropout",
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target_module=col_nn.DropoutForParallelInput,
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),
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SubModuleReplacementDescription(
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suffix="mlp.dropout",
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target_module=col_nn.DropoutForParallelInput,
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),
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],
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)
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# optimization configuration
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self.append_or_create_submodule_replacement(
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description=SubModuleReplacementDescription(
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suffix="ln_f",
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target_module=norm_cls,
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),
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policy=policy,
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target_key=GPT2Model,
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)
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self.append_or_create_submodule_replacement(
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description=[
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SubModuleReplacementDescription(
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suffix="ln_1",
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target_module=norm_cls,
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kwargs={"sp_partial_derived": use_sequence_parallel},
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),
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SubModuleReplacementDescription(
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suffix="ln_2",
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target_module=norm_cls,
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kwargs={"sp_partial_derived": use_sequence_parallel},
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),
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SubModuleReplacementDescription(
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suffix="ln_cross_attn",
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target_module=norm_cls,
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ignore_if_not_exist=True,
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kwargs={"sp_partial_derived": use_sequence_parallel},
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),
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],
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policy=policy,
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target_key=GPT2Block,
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)
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if self.shard_config.enable_flash_attention:
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self.append_or_create_method_replacement(
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description={
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"forward": get_gpt2_flash_attention_forward(),
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},
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policy=policy,
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target_key=GPT2Attention,
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)
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if self.shard_config.enable_sequence_parallelism:
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policy[GPT2Model].method_replacement = {"forward": gpt2_sequence_parallel_forward_fn(self.shard_config)}
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return policy
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def postprocess(self):
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return self.model
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def get_held_layers(self) -> List[nn.Module]:
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"""Get pipeline layers for current stage."""
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assert self.pipeline_stage_manager is not None
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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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stage_manager = self.pipeline_stage_manager
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held_layers = []
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layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages)
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if stage_manager.is_first_stage():
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held_layers.append(module.wte)
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held_layers.append(module.wpe)
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held_layers.append(module.drop)
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start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
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held_layers.extend(module.h[start_idx:end_idx])
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if stage_manager.is_last_stage():
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held_layers.append(module.ln_f)
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return held_layers
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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 = {
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"forward": partial(
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new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config
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)
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}
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self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls)
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# GPT2Model
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class GPT2ModelPolicy(GPT2Policy):
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def module_policy(self):
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from transformers.models.gpt2.modeling_gpt2 import GPT2Model
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policy = super().module_policy()
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if self.pipeline_stage_manager is not None:
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self.set_pipeline_forward(
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model_cls=GPT2Model, new_forward=GPT2PipelineForwards.gpt2_model_forward, policy=policy
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)
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return policy
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def get_held_layers(self) -> List[nn.Module]:
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return super().get_held_layers()
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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"""No shared params in GPT2Model."""
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return []
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# GPT2LMHeadModel
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class GPT2LMHeadModelPolicy(GPT2Policy):
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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: ModulePolicyDescription(
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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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)
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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(
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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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)
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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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if self.pipeline_stage_manager.is_last_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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# GPT2DoubleHeadsModel
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class GPT2DoubleHeadsModelPolicy(GPT2Policy):
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def module_policy(self):
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from transformers.models.gpt2.modeling_gpt2 import GPT2DoubleHeadsModel
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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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GPT2DoubleHeadsModel: ModulePolicyDescription(
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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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)
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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(
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model_cls=GPT2DoubleHeadsModel,
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new_forward=GPT2PipelineForwards.gpt2_double_heads_model_forward,
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policy=module_policy,
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)
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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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if self.pipeline_stage_manager.is_last_stage():
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multiple_choice_head = self.model.multiple_choice_head
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held_layers.append(self.model.lm_head)
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held_layers.append(multiple_choice_head.summary)
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held_layers.append(multiple_choice_head.activation)
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held_layers.append(multiple_choice_head.first_dropout)
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held_layers.append(multiple_choice_head.last_dropout)
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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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# GPT2ForQuestionAnswering
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class GPT2ForQuestionAnsweringPolicy(GPT2Policy):
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def module_policy(self):
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from transformers.models.gpt2.modeling_gpt2 import GPT2ForQuestionAnswering
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module_policy = super().module_policy()
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if self.pipeline_stage_manager is not None:
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self.set_pipeline_forward(
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model_cls=GPT2ForQuestionAnswering,
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new_forward=GPT2PipelineForwards.gpt2_for_question_answering_forward,
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policy=module_policy,
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)
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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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if self.pipeline_stage_manager.is_last_stage():
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held_layers.append(self.model.qa_outputs)
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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"""No shared_params in gpt2 for QA."""
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return []
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# GPT2ForTokenClassification
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class GPT2ForTokenClassificationPolicy(GPT2Policy):
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def module_policy(self):
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from transformers.models.gpt2.modeling_gpt2 import GPT2ForTokenClassification
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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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GPT2ForTokenClassification: ModulePolicyDescription(
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sub_module_replacement=[
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SubModuleReplacementDescription(suffix="dropout", target_module=col_nn.DropoutForParallelInput)
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]
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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(
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model_cls=GPT2ForTokenClassification,
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new_forward=GPT2PipelineForwards.gpt2_for_token_classification_forward,
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policy=module_policy,
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)
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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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if self.pipeline_stage_manager.is_last_stage():
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held_layers.append(self.model.dropout)
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held_layers.append(self.model.classifier)
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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"""No shared params in GPT2ForTokenClassification."""
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return []
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# GPT2ForSequenceClassification
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class GPT2ForSequenceClassificationPolicy(GPT2Policy):
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def module_policy(self):
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from transformers.models.gpt2.modeling_gpt2 import GPT2ForSequenceClassification
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module_policy = super().module_policy()
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if self.pipeline_stage_manager is not None:
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self.set_pipeline_forward(
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model_cls=GPT2ForSequenceClassification,
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new_forward=GPT2PipelineForwards.gpt2_for_sequence_classification_forward,
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policy=module_policy,
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)
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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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if self.pipeline_stage_manager.is_last_stage():
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held_layers.append(self.model.score)
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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"""No shared params in GPT2ForTokenClassification."""
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return []
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