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[shardformer] fix pipeline forward error if custom layer distribution is used (#5189) 

* Use self.[distribute_layers|get_stage_index] to exploit custom layer distribution

* Change static methods for t5 layer distribution to member functions

* Change static methods for whisper layer distribution to member functions

* Replace whisper policy usage with self one

* Fix test case to use non-static layer distribution methods

* fix: fix typo

---------

Co-authored-by: Wenhao Chen <cwher@outlook.com>
pull/4309/merge
Insu Jang 2024-03-27 01:57:00 -04:00 committed by GitHub
parent e6707a6e8d
commit 00525f7772
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
18 changed files with 136 additions and 106 deletions
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2
applications/ColossalMoE/colossal_moe/models/mixtral_policy.py
View File

@ -110,7 +110,7 @@ class MixtralPolicy(Policy):
module = self.model.model
layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages)
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
method_replacement = {"forward": partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)}
self.append_or_create_method_replacement(
description=method_replacement, policy=policy, target_key=model_cls

7
colossalai/shardformer/policies/base_policy.py
View File

@ -197,8 +197,7 @@ class Policy(ABC):
"""
return []
@staticmethod
def distribute_layers(num_layers: int, num_stages: int) -> List[int]:
def distribute_layers(self, num_layers: int, num_stages: int) -> List[int]:
"""Divide layers into stages"""
quotient = num_layers // num_stages
remainder = num_layers % num_stages
@ -213,8 +212,8 @@ class Policy(ABC):
layers_per_stage[i] += 1
return layers_per_stage
@staticmethod
def get_stage_index(
self,
layers_per_stage: List[int],
stage: int,
num_model_chunks: int = 1,
@ -242,4 +241,4 @@ class Policy(ABC):
end_idx = num_layers_per_stage_accumulated[stage + model_chunk * num_stages + 1]
stage_indices.append([start_idx, end_idx])
return stage_indices[0] if num_model_chunks == 1 else stage_indices
return stage_indices[0] if num_model_chunks == 1 else stage_indices

67
colossalai/shardformer/policies/bert.py
View File

@ -84,17 +84,26 @@ class BertPolicy(Policy):
SubModuleReplacementDescription(
suffix="attention.self.query",
target_module=col_nn.Linear1D_Col,
kwargs={"seq_parallel": use_sequence_parallel, "overlap": overlap},
kwargs={
"seq_parallel": use_sequence_parallel,
"overlap": overlap,
},
),
SubModuleReplacementDescription(
suffix="attention.self.key",
target_module=col_nn.Linear1D_Col,
kwargs={"seq_parallel": use_sequence_parallel, "overlap": overlap},
kwargs={
"seq_parallel": use_sequence_parallel,
"overlap": overlap,
},
),
SubModuleReplacementDescription(
suffix="attention.self.value",
target_module=col_nn.Linear1D_Col,
kwargs={"seq_parallel": use_sequence_parallel, "overlap": overlap},
kwargs={
"seq_parallel": use_sequence_parallel,
"overlap": overlap,
},
),
SubModuleReplacementDescription(
suffix="attention.self.dropout",
@ -112,7 +121,10 @@ class BertPolicy(Policy):
SubModuleReplacementDescription(
suffix="intermediate.dense",
target_module=col_nn.Linear1D_Col,
kwargs={"seq_parallel": use_sequence_parallel, "overlap": overlap},
kwargs={
"seq_parallel": use_sequence_parallel,
"overlap": overlap,
},
),
SubModuleReplacementDescription(
suffix="output.dense",
@ -214,7 +226,9 @@ class BertPolicy(Policy):
if self.shard_config.enable_tensor_parallelism:
self.append_or_create_submodule_replacement(
description=SubModuleReplacementDescription(
suffix="decoder", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}
suffix="decoder",
target_module=col_nn.Linear1D_Col,
kwargs={"gather_output": True},
),
policy=base_policy,
target_key=BertLMPredictionHead,
@ -241,7 +255,9 @@ class BertPolicy(Policy):
"_load_from_state_dict": col_nn.ParallelModule._load_from_state_dict,
}
self.append_or_create_method_replacement(
description=method_replacement, policy=base_policy, target_key=BertLMPredictionHead
description=method_replacement,
policy=base_policy,
target_key=BertLMPredictionHead,
)
return base_policy
@ -264,24 +280,32 @@ class BertPolicy(Policy):
if stage_manager.is_interleave:
layers_per_stage = self.distribute_layers(
len(module.encoder.layer), stage_manager.num_stages * stage_manager.num_model_chunks
len(module.encoder.layer),
stage_manager.num_stages * stage_manager.num_model_chunks,
)
stage_manager.stage_indices = Policy.get_stage_index(
stage_manager.stage_indices = self.get_stage_index(
layers_per_stage,
stage_manager.stage,
num_model_chunks=stage_manager.num_model_chunks,
num_stages=stage_manager.num_stages,
)
method_replacement = {
"forward": partial(new_forward, stage_manager=stage_manager, shard_config=self.shard_config)
"forward": partial(
new_forward,
stage_manager=stage_manager,
shard_config=self.shard_config,
)
}
else:
layers_per_stage = Policy.distribute_layers(len(module.encoder.layer), stage_manager.num_stages)
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
layers_per_stage = self.distribute_layers(len(module.encoder.layer), stage_manager.num_stages)
stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
method_replacement = {
"forward": partial(
new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config
new_forward,
stage_manager=stage_manager,
stage_index=stage_index,
shard_config=self.shard_config,
)
}
@ -301,9 +325,10 @@ class BertPolicy(Policy):
if stage_manager.is_interleave:
assert stage_manager.num_model_chunks is not None
layers_per_stage = self.distribute_layers(
len(module.encoder.layer), stage_manager.num_stages * stage_manager.num_model_chunks
len(module.encoder.layer),
stage_manager.num_stages * stage_manager.num_model_chunks,
)
stage_indices = Policy.get_stage_index(
stage_indices = self.get_stage_index(
layers_per_stage,
stage_manager.stage,
num_model_chunks=stage_manager.num_model_chunks,
@ -320,7 +345,7 @@ class BertPolicy(Policy):
layers_per_stage = self.distribute_layers(len(module.encoder.layer), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.embeddings)
start_idx, end_idx = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.encoder.layer[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.pooler)
@ -336,7 +361,9 @@ class BertModelPolicy(BertPolicy):
if self.pipeline_stage_manager:
self.set_pipeline_forward(
model_cls=BertModel, new_forward=BertPipelineForwards.bert_model_forward, policy=policy
model_cls=BertModel,
new_forward=BertPipelineForwards.bert_model_forward,
policy=policy,
)
return policy
@ -399,7 +426,9 @@ class BertLMHeadModelPolicy(BertPolicy):
if self.pipeline_stage_manager:
self.set_pipeline_forward(
model_cls=BertLMHeadModel, new_forward=BertPipelineForwards.bert_lm_head_model_forward, policy=policy
model_cls=BertLMHeadModel,
new_forward=BertPipelineForwards.bert_lm_head_model_forward,
policy=policy,
)
return policy
@ -437,7 +466,9 @@ class BertForMaskedLMPolicy(BertPolicy):
if self.pipeline_stage_manager:
self.set_pipeline_forward(
model_cls=BertForMaskedLM, new_forward=BertPipelineForwards.bert_for_masked_lm_forward, policy=policy
model_cls=BertForMaskedLM,
new_forward=BertPipelineForwards.bert_for_masked_lm_forward,
policy=policy,
)
return policy

4
colossalai/shardformer/policies/bloom.py
View File

@ -203,8 +203,8 @@ class BloomPolicy(Policy):
else:
module = self.model.transformer
layers_per_stage = Policy.distribute_layers(len(module.h), stage_manager.num_stages)
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages)
stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
method_replacement = {
"forward": partial(
new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config

4
colossalai/shardformer/policies/chatglm2.py
View File

@ -204,8 +204,8 @@ class ChatGLMPolicy(Policy):
else:
module = self.model.transformer
layers_per_stage = Policy.distribute_layers(module.num_layers, stage_manager.num_stages)
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
layers_per_stage = self.distribute_layers(module.num_layers, stage_manager.num_stages)
stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
method_replacement = {
"forward": partial(
new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config

4
colossalai/shardformer/policies/falcon.py
View File

@ -161,8 +161,8 @@ class FalconPolicy(Policy):
else:
module = self.model.transformer
layers_per_stage = Policy.distribute_layers(len(module.h), stage_manager.num_stages)
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages)
stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
method_replacement = {
"forward": partial(
new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config

8
colossalai/shardformer/policies/gpt2.py
View File

@ -188,7 +188,7 @@ class GPT2Policy(Policy):
layers_per_stage = self.distribute_layers(
len(module.h), stage_manager.num_stages * stage_manager.num_model_chunks
)
stage_indices = Policy.get_stage_index(
stage_indices = self.get_stage_index(
layers_per_stage,
stage_manager.stage,
num_model_chunks=stage_manager.num_model_chunks,
@ -229,7 +229,7 @@ class GPT2Policy(Policy):
layers_per_stage = self.distribute_layers(
len(module.h), stage_manager.num_stages * stage_manager.num_model_chunks
)
stage_manager.stage_indices = Policy.get_stage_index(
stage_manager.stage_indices = self.get_stage_index(
layers_per_stage,
stage_manager.stage,
num_model_chunks=stage_manager.num_model_chunks,
@ -243,8 +243,8 @@ class GPT2Policy(Policy):
)
}
else:
layers_per_stage = Policy.distribute_layers(len(module.h), stage_manager.num_stages)
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages)
stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
method_replacement = {
"forward": partial(
new_forward,

4
colossalai/shardformer/policies/gptj.py
View File

@ -200,8 +200,8 @@ class GPTJPolicy(Policy):
else:
module = self.model.transformer
layers_per_stage = Policy.distribute_layers(len(module.h), stage_manager.num_stages)
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages)
stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
method_replacement = {
"forward": partial(
new_forward,

8
colossalai/shardformer/policies/llama.py
View File

@ -167,7 +167,7 @@ class LlamaPolicy(Policy):
layers_per_stage = self.distribute_layers(
len(module.layers), stage_manager.num_stages * stage_manager.num_model_chunks
)
stage_manager.stage_indices = Policy.get_stage_index(
stage_manager.stage_indices = self.get_stage_index(
layers_per_stage,
stage_manager.stage,
num_model_chunks=stage_manager.num_model_chunks,
@ -178,8 +178,8 @@ class LlamaPolicy(Policy):
}
else:
layers_per_stage = Policy.distribute_layers(len(module.layers), stage_manager.num_stages)
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages)
stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
method_replacement = {
"forward": partial(
new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config
@ -207,7 +207,7 @@ class LlamaPolicy(Policy):
layers_per_stage = self.distribute_layers(
len(module.layers), stage_manager.num_stages * stage_manager.num_model_chunks
)
stage_indices = Policy.get_stage_index(
stage_indices = self.get_stage_index(
layers_per_stage,
stage_manager.stage,
num_model_chunks=stage_manager.num_model_chunks,

4
colossalai/shardformer/policies/opt.py
View File

@ -208,8 +208,8 @@ class OPTPolicy(Policy):
else:
module = self.model.model.decoder
layers_per_stage = Policy.distribute_layers(len(module.layers), stage_manager.num_stages)
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages)
stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
method_replacement = {
"forward": partial(
new_forward,

32
colossalai/shardformer/policies/t5.py
View File

@ -1,3 +1,5 @@
from __future__ import annotations
import warnings
from functools import partial
from typing import Callable, Dict, List, Tuple
@ -241,9 +243,8 @@ class T5BasePolicy(Policy):
def postprocess(self):
return self.model
@staticmethod
def distribute_t5_layers(
num_encoder_layers: int, num_decoder_layers: int, num_stages: int
self, num_encoder_layers: int, num_decoder_layers: int, num_stages: int
) -> Tuple[List[int], int]:
"""
Distribute t5 layers into stages when pipeline parallel is used.
@ -261,7 +262,7 @@ class T5BasePolicy(Policy):
# in the case of T5EncoderModel, set decoder starting stage to num_stages since it doesn't exist
if num_decoder_layers == 0:
return Policy.distribute_layers(num_encoder_layers, num_stages), num_stages
return self.distribute_layers(num_encoder_layers, num_stages), num_stages
# the number of stages distributed between encoder and decoder is optimized in this way:
# num_encoder_stages = argmin(abs(num_encoder_layers / encoder_stages - num_decoder_layers / decoder_stages))
@ -272,22 +273,21 @@ class T5BasePolicy(Policy):
num_encoder_stages = np.argmin([objective(i) for i in range(1, num_stages)]) + 1
num_decoder_stages = num_stages - num_encoder_stages
encoder_distribution = Policy.distribute_layers(num_encoder_layers, num_encoder_stages)
decoder_distribution = Policy.distribute_layers(num_decoder_layers, num_decoder_stages)
encoder_distribution = self.distribute_layers(num_encoder_layers, num_encoder_stages)
decoder_distribution = self.distribute_layers(num_decoder_layers, num_decoder_stages)
return encoder_distribution + decoder_distribution, num_encoder_stages
@staticmethod
def get_t5_stage_index(
layers_per_stage: List[int], stage: int, decoder_starting_stage: int
self, layers_per_stage: List[int], stage: int, decoder_starting_stage: int
) -> Tuple[bool, int, int]:
"""
Input the distribution of layers among stages, the current stage and the first stage of decoder.
Return the starting/ending idx of layers in encoder/decoder
"""
if stage < decoder_starting_stage:
return Policy.get_stage_index(layers_per_stage[:decoder_starting_stage], stage)
return self.get_stage_index(layers_per_stage[:decoder_starting_stage], stage)
else:
return Policy.get_stage_index(layers_per_stage[decoder_starting_stage:], stage - decoder_starting_stage)
return self.get_stage_index(layers_per_stage[decoder_starting_stage:], stage - decoder_starting_stage)
def get_held_layers(self) -> List[nn.Module]:
"""Get pipeline layers for current stage."""
@ -302,12 +302,10 @@ class T5BasePolicy(Policy):
num_decoder_layers = len(decoder.block) if decoder else 0
held_layers = []
layers_per_stage, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
layers_per_stage, decoder_starting_stage = self.distribute_t5_layers(
num_encoder_layers, num_decoder_layers, stage_manager.num_stages
)
start_idx, end_idx = T5BasePolicy.get_t5_stage_index(
layers_per_stage, stage_manager.stage, decoder_starting_stage
)
start_idx, end_idx = self.get_t5_stage_index(layers_per_stage, stage_manager.stage, decoder_starting_stage)
if stage_manager.stage < decoder_starting_stage:
# current stage is in t5's encoder
@ -343,10 +341,10 @@ class T5BasePolicy(Policy):
num_encoder_layers = len(encoder.block)
num_decoder_layers = len(decoder.block) if decoder else 0
layers_per_stage, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
layers_per_stage, decoder_starting_stage = self.distribute_t5_layers(
num_encoder_layers, num_decoder_layers, stage_manager.num_stages
)
stage_index = T5BasePolicy.get_t5_stage_index(layers_per_stage, stage_manager.stage, decoder_starting_stage)
stage_index = self.get_t5_stage_index(layers_per_stage, stage_manager.stage, decoder_starting_stage)
method_replacement = {
"forward": partial(
@ -386,7 +384,7 @@ class T5ModelPolicy(T5BasePolicy):
module = self.model
stage_manager = self.pipeline_stage_manager
if stage_manager is not None and stage_manager.num_stages > 1:
_, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
_, decoder_starting_stage = self.distribute_t5_layers(
len(module.encoder.block), len(module.decoder.block), stage_manager.num_stages
)
@ -434,7 +432,7 @@ class T5ForConditionalGenerationPolicy(T5BasePolicy):
module = self.model
stage_manager = self.pipeline_stage_manager
if stage_manager is not None and stage_manager.num_stages > 1:
_, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
_, decoder_starting_stage = self.distribute_t5_layers(
len(module.encoder.block), len(module.decoder.block), stage_manager.num_stages
)

4
colossalai/shardformer/policies/vit.py
View File

@ -149,8 +149,8 @@ class ViTPolicy(Policy):
else:
module = self.model.vit
layers_per_stage = Policy.distribute_layers(len(module.encoder.layer), stage_manager.num_stages)
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
layers_per_stage = self.distribute_layers(len(module.encoder.layer), stage_manager.num_stages)
stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
method_replacement = {"forward": pipeline_forward(stage_manager=stage_manager, stage_index=stage_index)}
self.append_or_create_method_replacement(
description=method_replacement, policy=policy, target_key=model_cls

30
colossalai/shardformer/policies/whisper.py
View File

@ -292,9 +292,8 @@ class WhisperPolicy(Policy):
def postprocess(self):
return self.model
@staticmethod
def distribute_whisper_layers(
num_encoder_layers: int, num_decoder_layers: int, num_stages: int
self, num_encoder_layers: int, num_decoder_layers: int, num_stages: int
) -> Tuple[List[int], int]:
"""
Distribute whisper layers into stages when pipeline parallel is used.
@ -312,7 +311,7 @@ class WhisperPolicy(Policy):
# in the case of whisperEncoderModel, set decoder starting stage to num_stages since it doesn't exist
if num_decoder_layers == 0:
return Policy.distribute_layers(num_encoder_layers, num_stages), num_stages
return self.distribute_layers(num_encoder_layers, num_stages), num_stages
# the number of stages distributed between encoder and decoder is optimized in this way:
# num_encoder_stages = argmin(abs(num_encoder_layers / encoder_stages - num_decoder_layers / decoder_stages))
@ -323,22 +322,21 @@ class WhisperPolicy(Policy):
num_encoder_stages = np.argmin([objective(i) for i in range(1, num_stages)]) + 1
num_decoder_stages = num_stages - num_encoder_stages
encoder_distribution = Policy.distribute_layers(num_encoder_layers, num_encoder_stages)
decoder_distribution = Policy.distribute_layers(num_decoder_layers, num_decoder_stages)
encoder_distribution = self.distribute_layers(num_encoder_layers, num_encoder_stages)
decoder_distribution = self.distribute_layers(num_decoder_layers, num_decoder_stages)
return encoder_distribution + decoder_distribution, num_encoder_stages
@staticmethod
def get_whisper_stage_index(
layers_per_stage: List[int], stage: int, decoder_starting_stage: int
self, layers_per_stage: List[int], stage: int, decoder_starting_stage: int
) -> Tuple[bool, int, int]:
"""
Input the distribution of layers among stages, the current stage and the first stage of decoder.
Return the starting/ending idx of layers in encoder/decoder
"""
if stage < decoder_starting_stage:
return Policy.get_stage_index(layers_per_stage[:decoder_starting_stage], stage)
return self.get_stage_index(layers_per_stage[:decoder_starting_stage], stage)
else:
return Policy.get_stage_index(
return self.get_stage_index(
layers_per_stage[decoder_starting_stage:],
stage - decoder_starting_stage,
)
@ -369,12 +367,10 @@ class WhisperPolicy(Policy):
num_decoder_layers = 0
held_layers = []
layers_per_stage, decoder_starting_stage = WhisperPolicy.distribute_whisper_layers(
layers_per_stage, decoder_starting_stage = self.distribute_whisper_layers(
num_encoder_layers, num_decoder_layers, stage_manager.num_stages
)
start_idx, end_idx = WhisperPolicy.get_whisper_stage_index(
layers_per_stage, stage_manager.stage, decoder_starting_stage
)
start_idx, end_idx = self.get_whisper_stage_index(layers_per_stage, stage_manager.stage, decoder_starting_stage)
if stage_manager.stage < decoder_starting_stage:
# current stage is in whisper's encoder
@ -424,12 +420,10 @@ class WhisperPolicy(Policy):
else:
num_decoder_layers = 0
layers_per_stage, decoder_starting_stage = WhisperPolicy.distribute_whisper_layers(
layers_per_stage, decoder_starting_stage = self.distribute_whisper_layers(
num_encoder_layers, num_decoder_layers, stage_manager.num_stages
)
stage_index = WhisperPolicy.get_whisper_stage_index(
layers_per_stage, stage_manager.stage, decoder_starting_stage
)
stage_index = self.get_whisper_stage_index(layers_per_stage, stage_manager.stage, decoder_starting_stage)
method_replacement = {
"forward": partial(
@ -511,7 +505,7 @@ class WhisperForConditionalGenerationPolicy(WhisperPolicy):
stage_manager = self.pipeline_stage_manager
if stage_manager is not None and stage_manager.num_stages > 1:
_, decoder_starting_stage = WhisperPolicy.distribute_whisper_layers(
_, decoder_starting_stage = self.distribute_whisper_layers(
num_encoder_layers, num_decoder_layers, stage_manager.num_stages
)
shared_params = []

17
examples/language/openmoe/model/openmoe_policy.py
View File

@ -98,11 +98,11 @@ class OpenMoePolicy(Policy):
module = self.model.model
layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages)
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
method_replacement = {"forward": partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)}
self.append_or_create_method_replacement(description=method_replacement,
policy=policy,
target_key=model_cls)
self.append_or_create_method_replacement(
description=method_replacement, policy=policy, target_key=model_cls
)
return
@ -126,12 +126,9 @@ class OpenMoePolicy(Policy):
held_layers.append(module.norm)
return held_layers
@staticmethod
def distribute_layers(num_layers: int, num_stages: int) -> List[int]:
"""Divide layers into stages
"""
def distribute_layers(self, num_layers: int, num_stages: int) -> List[int]:
"""Divide layers into stages"""
if num_layers == 24 and num_stages == 4:
return [7, 7, 7, 3]
elif num_layers == 24 and num_stages == 2:
@ -142,7 +139,7 @@ class OpenMoePolicy(Policy):
return [8, 4]
else:
print(f"num_layers: {num_layers}, num_stages: {num_stages} not optimized, use origin pp policy")
return Policy.distribute_layers(num_layers, num_stages)
return super().distribute_layers(num_layers, num_stages)
class OpenMoeModelPolicy(OpenMoePolicy):

8
tests/test_booster/test_plugin/test_3d_plugin.py
View File

@ -83,7 +83,7 @@ def run_fn(init_method, model_fn, data_gen_fn, output_transform_fn) -> Optional[
@parameterize("init_method", ["none", "lazy"])
def check_3d_plugin(init_method: str = "none", early_stop: bool = True):
"""check gemini plugin over model zoo
"""check hybrid plugin over model zoo
Args:
early_stop (bool, optional): Whether to stop when getting the first error. Defaults to True.
@ -260,7 +260,7 @@ def run_grad_acc_test(test_args):
origin_model, origin_optimizer, dataloader=dataloader
)
for p1, p2 in zip(model.unwrap().parameters(), origin_model.unwrap().parameters()):
assert_close(p1.to(p2.dtype), p2, atol=1e-2, rtol=1e-2)
assert_close(p1.to(p2.dtype), p2, atol=1e-2, rtol=1e-2)
def run_dist(rank, world_size, port, early_stop: bool = True):
@ -271,9 +271,9 @@ def run_dist(rank, world_size, port, early_stop: bool = True):
@rerun_if_address_is_in_use()
def test_gemini_plugin(early_stop: bool = True):
def test_3d_plugin(early_stop: bool = True):
spawn(run_dist, 4, early_stop=early_stop)
if __name__ == "__main__":
test_gemini_plugin(early_stop=False)
test_3d_plugin(early_stop=False)

18
tests/test_pipeline/test_pipeline_utils/test_t5_pipeline_utils.py
View File

@ -10,9 +10,12 @@ def test_t5_pipeline_distribution():
"decoder_starting_stage": [1, 1, 2, 2, 3, 1, 5, 2],
}
policy = T5BasePolicy()
for i in range(num_test_cases):
_, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
test_dict["num_encoder_layers"][i], test_dict["num_decoder_layers"][i], test_dict["num_stages"][i]
_, decoder_starting_stage = policy.distribute_t5_layers(
test_dict["num_encoder_layers"][i],
test_dict["num_decoder_layers"][i],
test_dict["num_stages"][i],
)
assert test_dict["decoder_starting_stage"][i] == decoder_starting_stage
@ -32,14 +35,15 @@ def test_t5_pipeline_layers():
}
for i in range(num_test_cases):
layers_per_stage, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
test_dict["num_encoder_layers"][i], test_dict["num_decoder_layers"][i], test_dict["num_stages"][i]
policy = T5BasePolicy()
layers_per_stage, decoder_starting_stage = policy.distribute_t5_layers(
test_dict["num_encoder_layers"][i],
test_dict["num_decoder_layers"][i],
test_dict["num_stages"][i],
)
for stage in range(test_dict["num_stages"][i]):
start_idx, end_idx = test_dict["layers_per_stage"][i][stage]
predicted_start, predicted_end = T5BasePolicy.get_t5_stage_index(
layers_per_stage, stage, decoder_starting_stage
)
predicted_start, predicted_end = policy.get_t5_stage_index(layers_per_stage, stage, decoder_starting_stage)
assert start_idx == predicted_start
assert end_idx == predicted_end

16
tests/test_pipeline/test_pipeline_utils/test_whisper_pipeline_utils.py
View File

@ -10,9 +10,12 @@ def test_whisper_pipeline_distribution():
"decoder_starting_stage": [1, 1, 2, 2, 3, 1, 5, 2],
}
policy = WhisperPolicy()
for i in range(num_test_cases):
_, decoder_starting_stage = WhisperPolicy.distribute_whisper_layers(
test_dict["num_encoder_layers"][i], test_dict["num_decoder_layers"][i], test_dict["num_stages"][i]
_, decoder_starting_stage = policy.distribute_whisper_layers(
test_dict["num_encoder_layers"][i],
test_dict["num_decoder_layers"][i],
test_dict["num_stages"][i],
)
assert test_dict["decoder_starting_stage"][i] == decoder_starting_stage
@ -31,14 +34,17 @@ def test_whisper_pipeline_layers():
],
}
policy = WhisperPolicy()
for i in range(num_test_cases):
layers_per_stage, decoder_starting_stage = WhisperPolicy.distribute_whisper_layers(
test_dict["num_encoder_layers"][i], test_dict["num_decoder_layers"][i], test_dict["num_stages"][i]
layers_per_stage, decoder_starting_stage = policy.distribute_whisper_layers(
test_dict["num_encoder_layers"][i],
test_dict["num_decoder_layers"][i],
test_dict["num_stages"][i],
)
for stage in range(test_dict["num_stages"][i]):
start_idx, end_idx = test_dict["layers_per_stage"][i][stage]
predicted_start, predicted_end = WhisperPolicy.get_whisper_stage_index(
predicted_start, predicted_end = policy.get_whisper_stage_index(
layers_per_stage, stage, decoder_starting_stage
)
assert start_idx == predicted_start

5
tests/test_shardformer/test_layer/test_dist_crossentropy.py
View File

@ -38,9 +38,10 @@ def check_dist_crossentropy(rank, world_size, port, ignore_index):
org_loss, dist_loss, atol=1e-5
), f"dist cross entropy loss is not equal to orgin loss\n{org_loss}\n{dist_loss}"
target_grad = torch.chunk(pred.grad, world_size, dim=-1)[rank]
assert torch.allclose(target_grad, dist_pred.grad), f"dist grad is not equal to orgin grad\n{target_grad}\n{dist_pred.grad}"
assert torch.allclose(
target_grad, dist_pred.grad
), f"dist grad is not equal to orgin grad\n{target_grad}\n{dist_pred.grad}"
@pytest.mark.dist