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[Sharderformer] Support zbv in Sharderformer Policy (#6150)

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* [feat] Sharderformer support zbv

* [feat] support chatglm2, command, deepseek for zbv

* [feat] support zbv in shardformer policy:
falcon,gptj,mistral,opt,qwen2,t5, vit, whisper

* [feat] support GPT2FusedLinearConv1D

* [feat] support GPT2FusedLinear (without tp)

* [fix] debug FusedConvLinear

* [shardfromer] support gpt2 policy for zbv, support GPT2FusedLinearConv
Col and Row.

* [Shardformer] support FusedLinear1D base for zbv

* [shardformer] support zbv in FusedLinear1D base, Col, Row

* [shardformer] support zbv in blip2 and sam policy

* [shardformer] fix bug incorrect number of gradients; add fusedLinear
base testcase;

* [fix] fix incorrect number of gradients ;

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* [Shardformer] add en doc for zbv;

* [fix] fix typo in Model compatibility table

* [fix] fix API Reference typo

* [Shardformer] add zh-Han doc for zbv

* [fix] fix Linear name; update en & zh doc

* [fix] fix shardformer doc import err

* [fix] fix shardconfig import in doc

* [fix] fix shardformer doc

* [fix] fix shardconfig doc

* [fix] fix config

* [fix] remove shardconfig

* [fix] fix doc

* [feat] add zbv doc string

* [fix] rm doc

* [fix] fix doc

* [fix] empty zbv doc

* [fix] ifx torch version

* [fix] fix torch version

* [fix] fix torch versions

* [fix] fix torch versions

* [fix] fix pyramid versions

* [fix] fix pyramid, zope version

* [fix] try fix workflow

* [fix] try import ShardConfig in yml

* [fix] fix workflow

* [fix] fix workflow

* [fix] fix workflow

* [fix] fix workflow

* [fix] fix ci

* [fix] fix zbv doc

* [fix] fix param for qkv linear, gpt2fused linear; fix requirments;

* [fix] fix policy use fused_linear

* [fix] fix weight grad none, err caused by  weight ptr change

* [fix] fix comm in WeightGradStore

* [fix] fix WeightGradStore pop param

* [fix] remove useless param in doc; fix gpt2 qkv test;

* [shardformer] simplify execute_w_pass_grad_accum;

* [fix] rm useless comments

* [shardformer] simplify execute_w_pass_grad_accum & execute_w_pass

* [shardformer] Run meaningful doc test

* [shadformer] fix doc test cmd;

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
duanjunwen
2025-01-02 10:22:26 +08:00
committed by GitHub
parent af06d162cf
commit a9bedc7a43
27 changed files with 3511 additions and 316 deletions
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156
colossalai/shardformer/policies/bloom.py
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@@ -59,6 +59,8 @@ class BloomPolicy(Policy):
sp_partial_derived = sp_mode == "split_gather"
use_zbv = self.pipeline_stage_manager is not None and self.pipeline_stage_manager.use_zbv
if self.shard_config.enable_tensor_parallelism:
assert (
self.model.config.n_head % self.shard_config.tensor_parallel_size == 0
@@ -78,6 +80,7 @@ class BloomPolicy(Policy):
kwargs={
"seq_parallel_mode": sp_mode,
"fp8_communication": self.shard_config.fp8_communication,
"use_zbv": use_zbv,
},
),
SubModuleReplacementDescription(
@@ -86,6 +89,7 @@ class BloomPolicy(Policy):
kwargs={
"seq_parallel_mode": sp_mode,
"fp8_communication": self.shard_config.fp8_communication,
"use_zbv": use_zbv,
},
),
SubModuleReplacementDescription(
@@ -98,6 +102,7 @@ class BloomPolicy(Policy):
kwargs={
"seq_parallel_mode": sp_mode,
"fp8_communication": self.shard_config.fp8_communication,
"use_zbv": use_zbv,
},
),
SubModuleReplacementDescription(
@@ -106,6 +111,7 @@ class BloomPolicy(Policy):
kwargs={
"seq_parallel_mode": sp_mode,
"fp8_communication": self.shard_config.fp8_communication,
"use_zbv": use_zbv,
},
),
],
@@ -120,6 +126,52 @@ class BloomPolicy(Policy):
},
)
if use_zbv:
policy[BloomBlock] = ModulePolicyDescription(
sub_module_replacement=[
SubModuleReplacementDescription(
suffix="self_attention.query_key_value",
target_module=col_nn.LinearWithGradAccum,
kwargs={
"seq_parallel_mode": sp_mode,
"fp8_communication": self.shard_config.fp8_communication,
"use_zbv": use_zbv,
},
),
SubModuleReplacementDescription(
suffix="self_attention.dense",
target_module=col_nn.LinearWithGradAccum,
kwargs={
"seq_parallel_mode": sp_mode,
"fp8_communication": self.shard_config.fp8_communication,
"use_zbv": use_zbv,
},
),
SubModuleReplacementDescription(
suffix="self_attention.attention_dropout",
target_module=col_nn.DropoutForParallelInput,
),
SubModuleReplacementDescription(
suffix="mlp.dense_h_to_4h",
target_module=col_nn.LinearWithGradAccum,
kwargs={
"seq_parallel_mode": sp_mode,
"fp8_communication": self.shard_config.fp8_communication,
"use_zbv": use_zbv,
},
),
SubModuleReplacementDescription(
suffix="mlp.dense_4h_to_h",
target_module=col_nn.LinearWithGradAccum,
kwargs={
"seq_parallel_mode": sp_mode,
"fp8_communication": self.shard_config.fp8_communication,
"use_zbv": use_zbv,
},
),
],
)
if embedding_cls is not None:
self.append_or_create_submodule_replacement(
description=[
@@ -247,14 +299,27 @@ class BloomPolicy(Policy):
stage_manager = self.pipeline_stage_manager
held_layers = []
layers_per_stage = stage_manager.distribute_layers(len(module.h))
if stage_manager.is_first_stage():
held_layers.append(module.word_embeddings)
held_layers.append(module.word_embeddings_layernorm)
start_idx, end_idx = stage_manager.get_stage_index(layers_per_stage)
held_layers.extend(module.h[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.ln_f)
if stage_manager.is_interleave:
layers_per_stage = stage_manager.distribute_layers(len(module.h))
stage_indices = stage_manager.get_stage_index(layers_per_stage)
if stage_manager.is_first_stage(ignore_chunk=True):
held_layers.append(module.word_embeddings)
held_layers.append(module.word_embeddings_layernorm)
for start_idx, end_idx in stage_indices:
held_layers.extend(module.h[start_idx:end_idx])
if (stage_manager.use_zbv and stage_manager.is_first_stage(ignore_chunk=True)) or (
not stage_manager.use_zbv and stage_manager.is_last_stage(ignore_chunk=True)
):
held_layers.append(module.ln_f)
else:
layers_per_stage = stage_manager.distribute_layers(len(module.h))
if stage_manager.is_first_stage():
held_layers.append(module.word_embeddings)
held_layers.append(module.word_embeddings_layernorm)
start_idx, end_idx = stage_manager.get_stage_index(layers_per_stage)
held_layers.extend(module.h[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.ln_f)
return held_layers
@@ -328,8 +393,14 @@ class BloomForCausalLMPolicy(BloomPolicy):
"""Get pipeline layers for current stage."""
stage_manager = self.pipeline_stage_manager
held_layers = super().get_held_layers()
if stage_manager.is_last_stage():
held_layers.append(self.model.lm_head)
if stage_manager.is_interleave:
if (stage_manager.use_zbv and stage_manager.is_first_stage(ignore_chunk=True)) or (
not stage_manager.use_zbv and stage_manager.is_last_stage(ignore_chunk=True)
):
held_layers.append(self.model.lm_head)
else:
if stage_manager.is_last_stage():
held_layers.append(self.model.lm_head)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
@@ -351,6 +422,7 @@ class BloomForSequenceClassificationPolicy(BloomPolicy):
from transformers.models.bloom.modeling_bloom import BloomForSequenceClassification
policy = super().module_policy()
use_zbv = self.pipeline_stage_manager is not None and self.pipeline_stage_manager.use_zbv
# handle tensor parallelism
if self.shard_config.enable_tensor_parallelism:
@@ -363,6 +435,18 @@ class BloomForSequenceClassificationPolicy(BloomPolicy):
policy=policy,
target_key=BloomForSequenceClassification,
)
elif use_zbv:
self.append_or_create_submodule_replacement(
description=SubModuleReplacementDescription(
suffix="score",
target_module=col_nn.LinearWithGradAccum,
kwargs=dict(
gather_output=True, fp8_communication=self.shard_config.fp8_communication, use_zbv=use_zbv
),
),
policy=policy,
target_key=BloomForSequenceClassification,
)
if self.pipeline_stage_manager:
self.set_pipeline_forward(
model_cls=BloomForSequenceClassification,
@@ -375,8 +459,14 @@ class BloomForSequenceClassificationPolicy(BloomPolicy):
"""Get pipeline layers for current stage."""
stage_manager = self.pipeline_stage_manager
held_layers = super().get_held_layers()
if stage_manager.is_last_stage():
held_layers.append(self.model.score)
if stage_manager.is_interleave:
if (stage_manager.use_zbv and stage_manager.is_first_stage(ignore_chunk=True)) or (
not stage_manager.use_zbv and stage_manager.is_last_stage(ignore_chunk=True)
):
held_layers.append(self.model.score)
else:
if stage_manager.is_last_stage():
held_layers.append(self.model.score)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
@@ -389,6 +479,7 @@ class BloomForTokenClassificationPolicy(BloomPolicy):
from transformers.models.bloom.modeling_bloom import BloomForTokenClassification
policy = super().module_policy()
use_zbv = self.pipeline_stage_manager is not None and self.pipeline_stage_manager.use_zbv
# handle tensor parallelism
if self.shard_config.enable_tensor_parallelism:
@@ -407,6 +498,24 @@ class BloomForTokenClassificationPolicy(BloomPolicy):
policy=policy,
target_key=BloomForTokenClassification,
)
elif use_zbv:
self.append_or_create_submodule_replacement(
description=[
SubModuleReplacementDescription(
suffix="classifier",
target_module=col_nn.LinearWithGradAccum,
kwargs=dict(
gather_output=True, fp8_communication=self.shard_config.fp8_communication, use_zbv=use_zbv
),
),
SubModuleReplacementDescription(
suffix="dropout",
target_module=col_nn.DropoutForReplicatedInput,
),
],
policy=policy,
target_key=BloomForTokenClassification,
)
if self.pipeline_stage_manager:
self.set_pipeline_forward(
model_cls=BloomForTokenClassification,
@@ -420,9 +529,16 @@ class BloomForTokenClassificationPolicy(BloomPolicy):
"""Get pipeline layers for current stage."""
stage_manager = self.pipeline_stage_manager
held_layers = super().get_held_layers()
if stage_manager.is_last_stage():
held_layers.append(self.model.dropout)
held_layers.append(self.model.classifier)
if stage_manager.is_interleave:
if (stage_manager.use_zbv and stage_manager.is_first_stage(ignore_chunk=True)) or (
not stage_manager.use_zbv and stage_manager.is_last_stage(ignore_chunk=True)
):
held_layers.append(self.model.dropout)
held_layers.append(self.model.classifier)
else:
if stage_manager.is_last_stage():
held_layers.append(self.model.dropout)
held_layers.append(self.model.classifier)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
@@ -448,8 +564,14 @@ class BloomForQuestionAnsweringPolicy(BloomPolicy):
"""Get pipeline layers for current stage."""
held_layers = super().get_held_layers()
stage_manager = self.pipeline_stage_manager
if stage_manager.is_last_stage():
held_layers.append(self.model.qa_outputs)
if stage_manager.is_interleave:
if (stage_manager.use_zbv and stage_manager.is_first_stage(ignore_chunk=True)) or (
not stage_manager.use_zbv and stage_manager.is_last_stage(ignore_chunk=True)
):
held_layers.append(self.model.qa_outputs)
else:
if stage_manager.is_last_stage():
held_layers.append(self.model.qa_outputs)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]: