mirror of https://github.com/hpcaitech/ColossalAI
385 lines
14 KiB
Python
385 lines
14 KiB
Python
import warnings
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from functools import partial
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from typing import Callable, Dict, List
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import torch.nn as nn
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from torch import Tensor, nn
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from colossalai.shardformer.layer import (
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FusedLayerNorm,
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LayerNorm,
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Linear1D_Col,
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Linear1D_Row,
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PaddingEmbedding,
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PaddingLMHead,
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VocabParallelEmbedding1D,
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VocabParallelLMHead1D,
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)
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from .._utils import getattr_
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from ..modeling.jit import get_jit_fused_dropout_add_func
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from ..modeling.opt import (
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OPTPipelineForwards,
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get_jit_fused_opt_decoder_layer_forward,
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get_lm_forward_with_dist_cross_entropy,
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get_opt_decoder_forward_for_flash_attention,
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get_opt_flash_attention_forward,
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)
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from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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__all__ = [
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"OPTPolicy",
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"OPTModelPolicy",
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"OPTForCausalLMPolicy",
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"OPTForSequenceClassificationPolicy",
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"OPTForQuestionAnsweringPolicy",
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]
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class OPTPolicy(Policy):
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def __init__(self) -> None:
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super().__init__()
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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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self.tie_weight = self.tie_weight_check()
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self.origin_attn_implement = self.model.config._attn_implementation
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return self.model
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def module_policy(self):
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from transformers.models.opt.modeling_opt import OPTAttention, OPTDecoder, OPTDecoderLayer, OptFlashAttention2
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ATTN_IMPLEMENTATION = {
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"eager": OPTAttention,
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"flash_attention_2": OptFlashAttention2,
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}
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policy = {}
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attn_cls = ATTN_IMPLEMENTATION[self.model.config._attn_implementation]
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embedding_cls = None
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if self.shard_config.enable_tensor_parallelism:
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embedding_cls = VocabParallelEmbedding1D
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else:
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if self.tie_weight:
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embedding_cls = PaddingEmbedding
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if self.shard_config.enable_fused_normalization:
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norm_cls = FusedLayerNorm
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else:
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norm_cls = LayerNorm
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if self.shard_config.enable_sequence_parallelism:
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self.shard_config.enable_sequence_parallelism = False
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warnings.warn("OPT doesn't support sequence parallelism now, will ignore the sequence parallelism flag.")
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if self.shard_config.enable_tensor_parallelism:
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assert (
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self.model.config.num_attention_heads % self.shard_config.tensor_parallel_size == 0
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), f"The number of attention heads must be divisible by tensor parallel size."
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policy[OPTDecoderLayer] = ModulePolicyDescription(
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="fc1",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="fc2",
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target_module=Linear1D_Row,
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),
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]
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)
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policy[attn_cls] = ModulePolicyDescription(
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attribute_replacement={
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"embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
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"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="q_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="k_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="v_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="out_proj",
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target_module=Linear1D_Row,
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),
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],
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)
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if embedding_cls is not None:
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self.append_or_create_submodule_replacement(
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description=SubModuleReplacementDescription(
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suffix="embed_tokens",
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target_module=embedding_cls,
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kwargs={"make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by},
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),
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policy=policy,
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target_key=OPTDecoder,
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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="final_layer_norm",
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target_module=norm_cls,
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ignore_if_not_exist=True,
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),
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policy=policy,
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target_key=OPTDecoder,
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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="self_attn_layer_norm",
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target_module=norm_cls,
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ignore_if_not_exist=True,
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),
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SubModuleReplacementDescription(
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suffix="final_layer_norm",
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target_module=norm_cls,
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ignore_if_not_exist=True,
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),
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],
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policy=policy,
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target_key=OPTDecoderLayer,
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)
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# use flash attention
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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_opt_flash_attention_forward(self.shard_config),
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},
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policy=policy,
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target_key=attn_cls,
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)
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if not self.shard_config.pipeline_stage_manager:
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self.append_or_create_method_replacement(
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description={
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"forward": get_opt_decoder_forward_for_flash_attention(self.shard_config),
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},
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policy=policy,
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target_key=OPTDecoder,
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)
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# use jit fused operator
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if self.shard_config.enable_jit_fused:
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self.append_or_create_method_replacement(
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description={
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"forward": get_jit_fused_opt_decoder_layer_forward(),
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"dropout_add": get_jit_fused_dropout_add_func(),
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},
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policy=policy,
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target_key=OPTDecoderLayer,
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)
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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__ == "OPTModel":
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module = self.model.decoder
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else:
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module = self.model.model.decoder
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stage_manager = self.pipeline_stage_manager
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held_layers = []
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layers_per_stage = stage_manager.distribute_layers(len(module.layers))
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if stage_manager.is_first_stage():
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held_layers.append(module.embed_tokens)
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held_layers.append(module.embed_positions)
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held_layers.append(module.project_in)
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start_idx, end_idx = stage_manager.get_stage_index(layers_per_stage)
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held_layers.extend(module.layers[start_idx:end_idx])
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if stage_manager.is_last_stage():
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held_layers.append(module.final_layer_norm)
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held_layers.append(module.project_out)
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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 self.pipeline_stage_manager:
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stage_manager = self.pipeline_stage_manager
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if self.model.__class__.__name__ == "OPTModel":
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module = self.model.decoder
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else:
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module = self.model.model.decoder
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layers_per_stage = stage_manager.distribute_layers(len(module.layers))
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stage_index = stage_manager.get_stage_index(layers_per_stage)
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method_replacement = {
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"forward": partial(
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new_forward,
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stage_manager=stage_manager,
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stage_index=stage_index,
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shard_config=self.shard_config,
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)
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}
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self.append_or_create_method_replacement(
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description=method_replacement, policy=policy, target_key=model_cls
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)
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class OPTModelPolicy(OPTPolicy):
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def module_policy(self):
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from transformers.models.opt.modeling_opt import OPTModel
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policy = super().module_policy()
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if self.pipeline_stage_manager:
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self.set_pipeline_forward(
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model_cls=OPTModel,
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new_forward=OPTPipelineForwards.opt_model_forward,
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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 OPTModel."""
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return []
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class OPTForCausalLMPolicy(OPTPolicy):
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def module_policy(self):
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from transformers.models.opt.modeling_opt import OPTForCausalLM
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policy = super().module_policy()
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if self.shard_config.enable_tensor_parallelism:
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self.append_or_create_submodule_replacement(
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description=SubModuleReplacementDescription(
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suffix="lm_head",
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target_module=VocabParallelLMHead1D,
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kwargs=dict(
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gather_output=not self.shard_config.parallel_output,
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make_vocab_size_divisible_by=self.shard_config.make_vocab_size_divisible_by,
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),
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),
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policy=policy,
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target_key=OPTForCausalLM,
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)
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if self.shard_config.parallel_output:
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method_replacement = {"forward": get_lm_forward_with_dist_cross_entropy(self.shard_config)}
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self.append_or_create_method_replacement(
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description=method_replacement, policy=policy, target_key=OPTForCausalLM
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)
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else:
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self.append_or_create_submodule_replacement(
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description=SubModuleReplacementDescription(
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suffix="lm_head",
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target_module=PaddingLMHead,
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kwargs=dict(make_vocab_size_divisible_by=self.shard_config.make_vocab_size_divisible_by),
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),
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policy=policy,
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target_key=OPTForCausalLM,
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)
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if self.pipeline_stage_manager:
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self.set_pipeline_forward(
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model_cls=OPTForCausalLM,
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new_forward=OPTPipelineForwards.opt_for_causal_lm_forward,
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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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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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opt_model = self.model
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if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
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num_stages = self.pipeline_stage_manager.num_stages
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if id(opt_model.model.decoder.embed_tokens.weight) == id(opt_model.lm_head.weight):
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return [
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{
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0: opt_model.model.decoder.embed_tokens.weight,
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num_stages - 1: opt_model.lm_head.weight,
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}
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]
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return []
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def postprocess(self):
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if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None:
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binding_map = {
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"model.decoder.embed_tokens": "lm_head",
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}
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for k, v in binding_map.items():
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src_mod = getattr_(self.model, k)
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dst_mod = getattr_(self.model, v)
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dst_mod.weight = src_mod.weight
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return self.model
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class OPTForSequenceClassificationPolicy(OPTPolicy):
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def module_policy(self):
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from transformers.models.opt.modeling_opt import OPTForSequenceClassification
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policy = super().module_policy()
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if self.pipeline_stage_manager:
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self.set_pipeline_forward(
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model_cls=OPTForSequenceClassification,
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new_forward=OPTPipelineForwards.opt_for_sequence_classification_forward,
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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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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 OPTForSequenceClassification"
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return []
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class OPTForQuestionAnsweringPolicy(OPTPolicy):
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def module_policy(self):
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from transformers.models.opt.modeling_opt import OPTForQuestionAnswering
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policy = super().module_policy()
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if self.pipeline_stage_manager:
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self.set_pipeline_forward(
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model_cls=OPTForQuestionAnswering,
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new_forward=OPTPipelineForwards.opt_for_question_answering_forward,
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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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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 OPTForSequenceClassification"
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return []
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