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[hotfix] Add layer norm gradients all-reduce for sequence parallel (#4926)

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* [hotfix] Add layer norm gradients all-reduce for sequence parallel. (#4915)

* Add layer norm gradients all-reduce for sequence parallel.

* skip pipeline inference test

* [hotfix] fixing polices of sequence parallel (#4922)

* Add layer norm gradients all-reduce for sequence parallel.

* fix parameter passing when calling get_autopolicy

---------

Co-authored-by: littsk <1214689160@qq.com>

* Hotfix/add grad all reduce for sequence parallel (#4927)

* Add layer norm gradients all-reduce for sequence parallel.


* fix parameter passing when calling get_autopolicy

* fix bug using wrong variables

---------

Co-authored-by: littsk <1214689160@qq.com>

* fix policy initialization

* fix bloom and chatglm policices

* polish code of handling layernorm

* fix moe module

* polish code of class initializing

---------

Co-authored-by: Zhongkai Zhao <kanezz620@gmail.com>
This commit is contained in:
littsk
2023-11-03 13:32:43 +08:00
committed by GitHub
parent d99b2c961a
commit 1a3315e336
30 changed files with 1120 additions and 552 deletions
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56
colossalai/shardformer/policies/llama.py
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@@ -6,7 +6,7 @@ import torch.nn as nn
from torch import Tensor
from torch.nn import Module
from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D
from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col, Linear1D_Row, RMSNorm, VocabParallelEmbedding1D
from ..modeling.llama import LlamaPipelineForwards, get_llama_flash_attention_forward
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
@@ -35,6 +35,11 @@ class LlamaPolicy(Policy):
policy = {}
if self.shard_config.enable_fused_normalization:
norm_cls = FusedRMSNorm
else:
norm_cls = RMSNorm
if self.shard_config.enable_sequence_parallelism:
self.shard_config.enable_sequence_parallelism = False
warnings.warn("Llama dosen't support sequence parallelism now, will ignore the sequence parallelism flag.")
@@ -93,31 +98,31 @@ class LlamaPolicy(Policy):
)
# optimization configuration
if self.shard_config.enable_fused_normalization:
self.append_or_create_submodule_replacement(
description=[
SubModuleReplacementDescription(
suffix="input_layernorm",
target_module=FusedRMSNorm,
),
SubModuleReplacementDescription(
suffix="post_attention_layernorm",
target_module=FusedRMSNorm,
),
],
policy=policy,
target_key=LlamaDecoderLayer,
)
self.append_or_create_submodule_replacement(
description=SubModuleReplacementDescription(
suffix="norm",
target_module=FusedRMSNorm,
self.append_or_create_submodule_replacement(
description=[
SubModuleReplacementDescription(
suffix="input_layernorm",
target_module=norm_cls,
),
policy=policy,
target_key=LlamaModel,
)
SubModuleReplacementDescription(
suffix="post_attention_layernorm",
target_module=norm_cls,
),
],
policy=policy,
target_key=LlamaDecoderLayer,
)
self.append_or_create_submodule_replacement(
description=SubModuleReplacementDescription(
suffix="norm",
target_module=norm_cls,
),
policy=policy,
target_key=LlamaModel,
)
# use flash attention
if self.shard_config.enable_flash_attention:
self.append_or_create_method_replacement(
description={
@@ -174,9 +179,6 @@ class LlamaPolicy(Policy):
class LlamaModelPolicy(LlamaPolicy):
def __init__(self) -> None:
super().__init__()
def module_policy(self):
policy = super().module_policy()
from transformers.models.llama.modeling_llama import LlamaModel