mirror of https://github.com/hpcaitech/ColossalAI
444 lines
20 KiB
Python
444 lines
20 KiB
Python
from functools import partial
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from typing import Callable, Dict, List, Optional, Tuple
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import numpy as np
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from torch import Tensor, nn
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from colossalai.shardformer.layer import (
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DropoutForParallelInput,
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Embedding1D,
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FusedRMSNorm,
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Linear1D_Col,
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Linear1D_Row,
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VocabParallelEmbedding1D,
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)
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from colossalai.shardformer.policies.base_policy import ModulePolicyDescription
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from .._utils import getattr_, setattr_
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from ..modeling.jit import get_jit_fused_dropout_add_func
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from ..modeling.t5 import (
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T5PipelineForwards,
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get_jit_fused_T5_layer_ff_forward,
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get_t5_flash_attention_forward,
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get_T5_layer_cross_attention_forward,
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get_T5_layer_self_attention_forward,
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)
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from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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__all__ = ["distribute_t5_layers", "T5ModelPolicy", "T5ForConditionalGenerationPolicy", "T5EncoderPolicy"]
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class T5BasePolicy(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.t5.modeling_t5 import (
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T5Attention,
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T5DenseActDense,
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T5DenseGatedActDense,
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T5LayerCrossAttention,
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T5LayerFF,
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T5LayerSelfAttention,
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T5Stack,
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)
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policy = {}
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if self.shard_config.enable_tensor_parallelism:
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policy[T5Stack] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="dropout",
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target_module=DropoutForParallelInput,
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),
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SubModuleReplacementDescription(
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suffix="embed_tokens",
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target_module=VocabParallelEmbedding1D,
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)
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])
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policy[T5LayerSelfAttention] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="dropout",
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target_module=DropoutForParallelInput,
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),
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])
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policy[T5LayerCrossAttention] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="dropout",
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target_module=DropoutForParallelInput,
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)
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])
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policy[T5Attention] = ModulePolicyDescription(attribute_replacement={
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"d_model":
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self.model.config.d_model // self.shard_config.tensor_parallel_size,
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"n_heads":
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self.model.config.num_heads // self.shard_config.tensor_parallel_size,
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"inner_dim":
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self.model.config.num_heads * self.model.config.d_kv // 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",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="k",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="v",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="o",
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target_module=Linear1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="relative_attention_bias",
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target_module=Embedding1D,
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kwargs=dict(gather_output=False),
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ignore_if_not_exist=True)
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])
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policy[T5LayerFF] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="dropout",
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target_module=DropoutForParallelInput,
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),
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])
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policy[T5DenseGatedActDense] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="wi_0 ",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="wi_1",
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target_module=Linear1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="wo", target_module=Linear1D_Col, kwargs=dict(gather_output=True)),
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SubModuleReplacementDescription(
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suffix="dropout",
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target_module=DropoutForParallelInput,
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)
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])
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policy[T5DenseActDense] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="wi",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="wo",
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target_module=Linear1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="dropout",
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target_module=DropoutForParallelInput,
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)
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])
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# optimization configuration
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if self.shard_config.enable_fused_normalization:
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self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
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suffix="layer_norm",
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target_module=FusedRMSNorm,
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),
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policy=policy,
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target_key=T5LayerFF)
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self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
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suffix="layer_norm",
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target_module=FusedRMSNorm,
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),
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policy=policy,
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target_key=T5LayerFF)
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self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
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suffix="layer_norm", target_module=FusedRMSNorm),
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policy=policy,
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target_key=T5LayerSelfAttention)
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self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
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suffix="layer_norm", target_module=FusedRMSNorm),
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policy=policy,
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target_key=T5LayerCrossAttention)
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self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
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suffix="final_layer_norm", target_module=FusedRMSNorm),
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policy=policy,
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target_key=T5Stack)
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# use flash attention
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if self.shard_config.enable_flash_attention:
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policy[T5Attention] = ModulePolicyDescription(method_replacement={
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'forward': get_t5_flash_attention_forward(),
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})
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# use jit operator
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if self.shard_config.enable_jit_fused:
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policy[T5LayerFF] = ModulePolicyDescription(method_replacement={
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'forward': get_jit_fused_T5_layer_ff_forward(),
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'dropout_add': get_jit_fused_dropout_add_func(),
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})
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policy[T5LayerSelfAttention] = ModulePolicyDescription(method_replacement={
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'forward': get_T5_layer_self_attention_forward(),
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'dropout_add': get_jit_fused_dropout_add_func(),
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})
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policy[T5LayerCrossAttention] = ModulePolicyDescription(method_replacement={
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'forward': get_T5_layer_cross_attention_forward(),
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'dropout_add': get_jit_fused_dropout_add_func(),
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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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@staticmethod
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def distribute_t5_layers(num_encoder_layers: int, num_decoder_layers: int,
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num_stages: int) -> Tuple[List[int], int]:
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"""
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Distribute t5 layers into stages when pipeline parallel is used.
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Return the layer distribution as a list and the starting stage of decoder.
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If decoder doesn't exist, returned decoder starting stage is set to num_encoder_layers.
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"""
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# number of encoder layers must be a positive integer
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if num_encoder_layers <= 0:
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raise ValueError("The number of encoder layers for T5 must be a positive integer.")
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# number of layers should be large enough to fill in every stage
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if num_encoder_layers + num_decoder_layers < num_stages:
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raise ValueError("The total number of layers can't be smaller than number of stages.")
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# in the case of T5EncoderModel, set decoder starting stage to num_stages since it doesn't exist
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if num_decoder_layers == 0:
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return Policy.distribute_layers(num_encoder_layers, num_stages), num_stages
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# the number of stages distributed between encoder and decoder is optmized in this way:
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# num_encoder_stages = argmin(abs(num_encoder_layers / encoder_stages - num_decoder_layers / decoder_stages))
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# s.t. num_encoder_stages + num_decoder_stages = num_stages, num_encoder_stages >= 1, num_decoder_stages >= 1
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def objective(num_encoder_stages):
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return abs(num_encoder_layers / num_encoder_stages - num_decoder_layers / (num_stages - num_encoder_stages))
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num_encoder_stages = np.argmin([objective(i) for i in range(1, num_stages)]) + 1
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num_decoder_stages = num_stages - num_encoder_stages
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encoder_distribution = Policy.distribute_layers(num_encoder_layers, num_encoder_stages)
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decoder_distribution = Policy.distribute_layers(num_decoder_layers, num_decoder_stages)
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return encoder_distribution + decoder_distribution, num_encoder_stages
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@staticmethod
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def get_t5_stage_index(layers_per_stage: List[int], stage: int,
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decoder_starting_stage: int) -> Tuple[bool, int, int]:
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"""
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Input the distribution of layers among stages, the current stage and the first stage of decoder.
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Return the starting/ending idx of layers in encoder/decoder
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"""
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if stage < decoder_starting_stage:
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return Policy.get_stage_index(layers_per_stage[:decoder_starting_stage], stage)
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else:
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return Policy.get_stage_index(layers_per_stage[decoder_starting_stage:], stage - decoder_starting_stage)
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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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stage_manager = self.pipeline_stage_manager
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model = self.model
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encoder = self.model.encoder
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decoder = getattr(self.model, 'decoder', None)
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num_encoder_layers = len(encoder.block)
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num_decoder_layers = len(decoder.block) if decoder else 0
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held_layers = []
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layers_per_stage, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
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num_encoder_layers, num_decoder_layers, stage_manager.num_stages)
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start_idx, end_idx = T5BasePolicy.get_t5_stage_index(layers_per_stage, stage_manager.stage,
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decoder_starting_stage)
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if stage_manager.stage < decoder_starting_stage:
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# current stage is in t5's encoder
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if stage_manager.is_first_stage():
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held_layers.append(model.shared)
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held_layers.append(encoder.embed_tokens)
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held_layers.append(encoder.dropout)
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if stage_manager.stage == decoder_starting_stage - 1:
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held_layers.append(encoder.final_layer_norm)
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held_layers.append(encoder.dropout)
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held_layers.extend(encoder.block[start_idx:end_idx])
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else:
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# current stage is in t5's decoder
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if stage_manager.stage == decoder_starting_stage:
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held_layers.append(decoder.embed_tokens)
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held_layers.append(decoder.dropout)
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if stage_manager.is_last_stage():
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held_layers.append(decoder.final_layer_norm)
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held_layers.append(decoder.dropout)
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held_layers.extend(decoder.block[start_idx:end_idx])
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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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encoder = self.model.encoder
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decoder = getattr(self.model, 'decoder', None)
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num_encoder_layers = len(encoder.block)
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num_decoder_layers = len(decoder.block) if decoder else 0
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layers_per_stage, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
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num_encoder_layers, num_decoder_layers, stage_manager.num_stages)
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stage_index = T5BasePolicy.get_t5_stage_index(layers_per_stage, stage_manager.stage, decoder_starting_stage)
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method_replacement = {
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'forward':
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partial(new_forward,
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stage_manager=stage_manager,
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stage_index=stage_index,
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decoder_starting_stage=decoder_starting_stage)
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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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class T5ModelPolicy(T5BasePolicy):
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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 import T5Model
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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(description=SubModuleReplacementDescription(
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suffix="shared",
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target_module=VocabParallelEmbedding1D,
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),
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policy=policy,
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target_key=T5Model)
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if self.pipeline_stage_manager is not None:
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self.set_pipeline_forward(model_cls=T5Model, new_forward=T5PipelineForwards.t5_model_forward, policy=policy)
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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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module = self.model
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stage_manager = self.pipeline_stage_manager
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if stage_manager is not None and stage_manager.num_stages > 1:
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_, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(len(module.encoder.block),
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len(module.decoder.block),
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stage_manager.num_stages)
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if id(module.decoder.embed_tokens.weight) == id(module.shared.weight):
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return [{0: module.shared.weight, decoder_starting_stage: module.decoder.embed_tokens.weight}]
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return []
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class T5ForConditionalGenerationPolicy(T5BasePolicy):
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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 import T5ForConditionalGeneration
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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(description=[
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SubModuleReplacementDescription(
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suffix="shared",
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target_module=VocabParallelEmbedding1D,
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),
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SubModuleReplacementDescription(suffix="lm_head",
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target_module=Linear1D_Col,
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kwargs=dict(gather_output=True))
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],
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policy=policy,
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target_key=T5ForConditionalGeneration)
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if self.pipeline_stage_manager is not None:
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self.set_pipeline_forward(model_cls=T5ForConditionalGeneration,
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new_forward=T5PipelineForwards.t5_for_conditional_generation_forward,
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policy=policy)
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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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module = self.model
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stage_manager = self.pipeline_stage_manager
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if stage_manager is not None and stage_manager.num_stages > 1:
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_, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(len(module.encoder.block),
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len(module.decoder.block),
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stage_manager.num_stages)
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shared_params = []
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shared_embedding = {}
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if id(module.decoder.embed_tokens.weight) == id(module.shared.weight):
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shared_embedding[0] = module.shared.weight
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shared_embedding[decoder_starting_stage] = module.decoder.embed_tokens.weight
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if id(module.lm_head.weight) == id(module.shared.weight):
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shared_embedding[0] = module.shared.weight
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shared_embedding[stage_manager.num_stages - 1] = module.lm_head.weight
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if len(shared_embedding) > 0:
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shared_params.append(shared_embedding)
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return shared_params
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return []
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class T5EncoderPolicy(T5BasePolicy):
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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 import T5EncoderModel
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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(description=SubModuleReplacementDescription(
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suffix="shared",
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target_module=VocabParallelEmbedding1D,
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),
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policy=policy,
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target_key=T5EncoderModel)
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if self.pipeline_stage_manager is not None:
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self.set_pipeline_forward(model_cls=T5EncoderModel,
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new_forward=T5PipelineForwards.t5_encoder_model_forward,
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policy=policy)
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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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return []
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