mirror of https://github.com/hpcaitech/ColossalAI
449 lines
21 KiB
Python
449 lines
21 KiB
Python
import warnings
|
|
from functools import partial
|
|
from typing import Callable, Dict, List, Optional, Tuple
|
|
|
|
import numpy as np
|
|
from torch import Tensor, nn
|
|
|
|
from colossalai.shardformer.layer import (
|
|
DropoutForParallelInput,
|
|
Embedding1D,
|
|
FusedRMSNorm,
|
|
Linear1D_Col,
|
|
Linear1D_Row,
|
|
VocabParallelEmbedding1D,
|
|
)
|
|
from colossalai.shardformer.policies.base_policy import ModulePolicyDescription
|
|
|
|
from .._utils import getattr_, setattr_
|
|
from ..modeling.jit import get_jit_fused_dropout_add_func
|
|
from ..modeling.t5 import (
|
|
T5PipelineForwards,
|
|
get_jit_fused_T5_layer_ff_forward,
|
|
get_t5_flash_attention_forward,
|
|
get_T5_layer_cross_attention_forward,
|
|
get_T5_layer_self_attention_forward,
|
|
)
|
|
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
|
|
|
|
__all__ = ["distribute_t5_layers", "T5ModelPolicy", "T5ForConditionalGenerationPolicy", "T5EncoderPolicy"]
|
|
|
|
|
|
class T5BasePolicy(Policy):
|
|
|
|
def config_sanity_check(self):
|
|
pass
|
|
|
|
def preprocess(self):
|
|
# reshape the embedding layer
|
|
r"""
|
|
Reshape the Embedding layer to make the embedding dimension divisible by world_size
|
|
"""
|
|
if self.shard_config.enable_tensor_parallelism:
|
|
vocab_size = self.model.config.vocab_size
|
|
world_size = self.shard_config.tensor_parallel_size
|
|
if vocab_size % world_size != 0:
|
|
new_vocab_size = vocab_size + world_size - vocab_size % world_size
|
|
self.model.resize_token_embeddings(new_vocab_size)
|
|
return self.model
|
|
|
|
def module_policy(self):
|
|
from transformers.models.t5.modeling_t5 import (
|
|
T5Attention,
|
|
T5DenseActDense,
|
|
T5DenseGatedActDense,
|
|
T5LayerCrossAttention,
|
|
T5LayerFF,
|
|
T5LayerSelfAttention,
|
|
T5Stack,
|
|
)
|
|
|
|
policy = {}
|
|
|
|
if self.shard_config.enable_sequence_parallelism:
|
|
self.shard_config.enable_sequence_parallelism = False
|
|
warnings.warn("T5 dosen't support sequence parallelism now, will ignore the sequence parallelism flag.")
|
|
|
|
if self.shard_config.enable_tensor_parallelism:
|
|
policy[T5Stack] = ModulePolicyDescription(sub_module_replacement=[
|
|
SubModuleReplacementDescription(
|
|
suffix="dropout",
|
|
target_module=DropoutForParallelInput,
|
|
),
|
|
SubModuleReplacementDescription(
|
|
suffix="embed_tokens",
|
|
target_module=VocabParallelEmbedding1D,
|
|
)
|
|
])
|
|
policy[T5LayerSelfAttention] = ModulePolicyDescription(sub_module_replacement=[
|
|
SubModuleReplacementDescription(
|
|
suffix="dropout",
|
|
target_module=DropoutForParallelInput,
|
|
),
|
|
])
|
|
policy[T5LayerCrossAttention] = ModulePolicyDescription(sub_module_replacement=[
|
|
SubModuleReplacementDescription(
|
|
suffix="dropout",
|
|
target_module=DropoutForParallelInput,
|
|
)
|
|
])
|
|
policy[T5Attention] = ModulePolicyDescription(attribute_replacement={
|
|
"d_model":
|
|
self.model.config.d_model // self.shard_config.tensor_parallel_size,
|
|
"n_heads":
|
|
self.model.config.num_heads // self.shard_config.tensor_parallel_size,
|
|
"inner_dim":
|
|
self.model.config.num_heads * self.model.config.d_kv // self.shard_config.tensor_parallel_size
|
|
},
|
|
sub_module_replacement=[
|
|
SubModuleReplacementDescription(
|
|
suffix="q",
|
|
target_module=Linear1D_Col,
|
|
),
|
|
SubModuleReplacementDescription(
|
|
suffix="k",
|
|
target_module=Linear1D_Col,
|
|
),
|
|
SubModuleReplacementDescription(
|
|
suffix="v",
|
|
target_module=Linear1D_Col,
|
|
),
|
|
SubModuleReplacementDescription(
|
|
suffix="o",
|
|
target_module=Linear1D_Row,
|
|
),
|
|
SubModuleReplacementDescription(
|
|
suffix="relative_attention_bias",
|
|
target_module=Embedding1D,
|
|
kwargs=dict(gather_output=False),
|
|
ignore_if_not_exist=True)
|
|
])
|
|
policy[T5LayerFF] = ModulePolicyDescription(sub_module_replacement=[
|
|
SubModuleReplacementDescription(
|
|
suffix="dropout",
|
|
target_module=DropoutForParallelInput,
|
|
),
|
|
])
|
|
policy[T5DenseGatedActDense] = ModulePolicyDescription(sub_module_replacement=[
|
|
SubModuleReplacementDescription(
|
|
suffix="wi_0 ",
|
|
target_module=Linear1D_Col,
|
|
),
|
|
SubModuleReplacementDescription(
|
|
suffix="wi_1",
|
|
target_module=Linear1D_Row,
|
|
),
|
|
SubModuleReplacementDescription(
|
|
suffix="wo", target_module=Linear1D_Col, kwargs=dict(gather_output=True)),
|
|
SubModuleReplacementDescription(
|
|
suffix="dropout",
|
|
target_module=DropoutForParallelInput,
|
|
)
|
|
])
|
|
policy[T5DenseActDense] = ModulePolicyDescription(sub_module_replacement=[
|
|
SubModuleReplacementDescription(
|
|
suffix="wi",
|
|
target_module=Linear1D_Col,
|
|
),
|
|
SubModuleReplacementDescription(
|
|
suffix="wo",
|
|
target_module=Linear1D_Row,
|
|
),
|
|
SubModuleReplacementDescription(
|
|
suffix="dropout",
|
|
target_module=DropoutForParallelInput,
|
|
)
|
|
])
|
|
|
|
# optimization configuration
|
|
if self.shard_config.enable_fused_normalization:
|
|
self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
|
|
suffix="layer_norm",
|
|
target_module=FusedRMSNorm,
|
|
),
|
|
policy=policy,
|
|
target_key=T5LayerFF)
|
|
self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
|
|
suffix="layer_norm",
|
|
target_module=FusedRMSNorm,
|
|
),
|
|
policy=policy,
|
|
target_key=T5LayerFF)
|
|
self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
|
|
suffix="layer_norm", target_module=FusedRMSNorm),
|
|
policy=policy,
|
|
target_key=T5LayerSelfAttention)
|
|
self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
|
|
suffix="layer_norm", target_module=FusedRMSNorm),
|
|
policy=policy,
|
|
target_key=T5LayerCrossAttention)
|
|
self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
|
|
suffix="final_layer_norm", target_module=FusedRMSNorm),
|
|
policy=policy,
|
|
target_key=T5Stack)
|
|
|
|
# use flash attention
|
|
if self.shard_config.enable_flash_attention:
|
|
policy[T5Attention] = ModulePolicyDescription(method_replacement={
|
|
'forward': get_t5_flash_attention_forward(),
|
|
})
|
|
|
|
# use jit operator
|
|
if self.shard_config.enable_jit_fused:
|
|
policy[T5LayerFF] = ModulePolicyDescription(method_replacement={
|
|
'forward': get_jit_fused_T5_layer_ff_forward(),
|
|
'dropout_add': get_jit_fused_dropout_add_func(),
|
|
})
|
|
policy[T5LayerSelfAttention] = ModulePolicyDescription(method_replacement={
|
|
'forward': get_T5_layer_self_attention_forward(),
|
|
'dropout_add': get_jit_fused_dropout_add_func(),
|
|
})
|
|
policy[T5LayerCrossAttention] = ModulePolicyDescription(method_replacement={
|
|
'forward': get_T5_layer_cross_attention_forward(),
|
|
'dropout_add': get_jit_fused_dropout_add_func(),
|
|
})
|
|
return policy
|
|
|
|
def postprocess(self):
|
|
return self.model
|
|
|
|
@staticmethod
|
|
def distribute_t5_layers(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.
|
|
Return the layer distribution as a list and the starting stage of decoder.
|
|
If decoder doesn't exist, returned decoder starting stage is set to num_encoder_layers.
|
|
"""
|
|
|
|
# number of encoder layers must be a positive integer
|
|
if num_encoder_layers <= 0:
|
|
raise ValueError("The number of encoder layers for T5 must be a positive integer.")
|
|
|
|
# number of layers should be large enough to fill in every stage
|
|
if num_encoder_layers + num_decoder_layers < num_stages:
|
|
raise ValueError("The total number of layers can't be smaller than number of stages.")
|
|
|
|
# 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
|
|
|
|
# the number of stages distributed between encoder and decoder is optmized in this way:
|
|
# num_encoder_stages = argmin(abs(num_encoder_layers / encoder_stages - num_decoder_layers / decoder_stages))
|
|
# s.t. num_encoder_stages + num_decoder_stages = num_stages, num_encoder_stages >= 1, num_decoder_stages >= 1
|
|
def objective(num_encoder_stages):
|
|
return abs(num_encoder_layers / num_encoder_stages - num_decoder_layers / (num_stages - num_encoder_stages))
|
|
|
|
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)
|
|
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) -> 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)
|
|
else:
|
|
return Policy.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."""
|
|
assert self.pipeline_stage_manager is not None
|
|
stage_manager = self.pipeline_stage_manager
|
|
|
|
model = self.model
|
|
encoder = self.model.encoder
|
|
decoder = getattr(self.model, 'decoder', None)
|
|
|
|
num_encoder_layers = len(encoder.block)
|
|
num_decoder_layers = len(decoder.block) if decoder else 0
|
|
|
|
held_layers = []
|
|
layers_per_stage, decoder_starting_stage = T5BasePolicy.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)
|
|
|
|
if stage_manager.stage < decoder_starting_stage:
|
|
# current stage is in t5's encoder
|
|
if stage_manager.is_first_stage():
|
|
held_layers.append(model.shared)
|
|
held_layers.append(encoder.embed_tokens)
|
|
held_layers.append(encoder.dropout)
|
|
if stage_manager.stage == decoder_starting_stage - 1:
|
|
held_layers.append(encoder.final_layer_norm)
|
|
held_layers.append(encoder.dropout)
|
|
held_layers.extend(encoder.block[start_idx:end_idx])
|
|
else:
|
|
# current stage is in t5's decoder
|
|
if stage_manager.stage == decoder_starting_stage:
|
|
held_layers.append(decoder.embed_tokens)
|
|
held_layers.append(decoder.dropout)
|
|
if stage_manager.is_last_stage():
|
|
held_layers.append(decoder.final_layer_norm)
|
|
held_layers.append(decoder.dropout)
|
|
held_layers.extend(decoder.block[start_idx:end_idx])
|
|
return held_layers
|
|
|
|
def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None:
|
|
"""If under pipeline parallel setting, replacing the original forward method of huggingface
|
|
to customized forward method, and add this changing to policy."""
|
|
if not self.pipeline_stage_manager:
|
|
raise ValueError("set_pipeline_forward method can only be called when pipeline parallel is enabled.")
|
|
stage_manager = self.pipeline_stage_manager
|
|
|
|
encoder = self.model.encoder
|
|
decoder = getattr(self.model, 'decoder', None)
|
|
|
|
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(
|
|
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)
|
|
|
|
method_replacement = {
|
|
'forward':
|
|
partial(new_forward,
|
|
stage_manager=stage_manager,
|
|
stage_index=stage_index,
|
|
decoder_starting_stage=decoder_starting_stage)
|
|
}
|
|
self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls)
|
|
|
|
|
|
class T5ModelPolicy(T5BasePolicy):
|
|
|
|
def __init__(self) -> None:
|
|
super().__init__()
|
|
|
|
def module_policy(self):
|
|
from transformers import T5Model
|
|
policy = super().module_policy()
|
|
|
|
if self.shard_config.enable_tensor_parallelism:
|
|
self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
|
|
suffix="shared",
|
|
target_module=VocabParallelEmbedding1D,
|
|
),
|
|
policy=policy,
|
|
target_key=T5Model)
|
|
if self.pipeline_stage_manager is not None:
|
|
self.set_pipeline_forward(model_cls=T5Model, new_forward=T5PipelineForwards.t5_model_forward, policy=policy)
|
|
|
|
return policy
|
|
|
|
def get_held_layers(self) -> List[nn.Module]:
|
|
return super().get_held_layers()
|
|
|
|
def get_shared_params(self) -> List[Dict[int, Tensor]]:
|
|
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(len(module.encoder.block),
|
|
len(module.decoder.block),
|
|
stage_manager.num_stages)
|
|
|
|
if id(module.decoder.embed_tokens.weight) == id(module.shared.weight):
|
|
return [{0: module.shared.weight, decoder_starting_stage: module.decoder.embed_tokens.weight}]
|
|
return []
|
|
|
|
|
|
class T5ForConditionalGenerationPolicy(T5BasePolicy):
|
|
|
|
def __init__(self) -> None:
|
|
super().__init__()
|
|
|
|
def module_policy(self):
|
|
from transformers import T5ForConditionalGeneration
|
|
|
|
policy = super().module_policy()
|
|
|
|
if self.shard_config.enable_tensor_parallelism:
|
|
self.append_or_create_submodule_replacement(description=[
|
|
SubModuleReplacementDescription(
|
|
suffix="shared",
|
|
target_module=VocabParallelEmbedding1D,
|
|
),
|
|
SubModuleReplacementDescription(suffix="lm_head",
|
|
target_module=Linear1D_Col,
|
|
kwargs=dict(gather_output=True))
|
|
],
|
|
policy=policy,
|
|
target_key=T5ForConditionalGeneration)
|
|
|
|
if self.pipeline_stage_manager is not None:
|
|
self.set_pipeline_forward(model_cls=T5ForConditionalGeneration,
|
|
new_forward=T5PipelineForwards.t5_for_conditional_generation_forward,
|
|
policy=policy)
|
|
return policy
|
|
|
|
def get_held_layers(self) -> List[nn.Module]:
|
|
held_layers = super().get_held_layers()
|
|
if self.pipeline_stage_manager.is_last_stage():
|
|
held_layers.append(self.model.lm_head)
|
|
return held_layers
|
|
|
|
def get_shared_params(self) -> List[Dict[int, Tensor]]:
|
|
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(len(module.encoder.block),
|
|
len(module.decoder.block),
|
|
stage_manager.num_stages)
|
|
|
|
shared_params = []
|
|
shared_embedding = {}
|
|
if id(module.decoder.embed_tokens.weight) == id(module.shared.weight):
|
|
shared_embedding[0] = module.shared.weight
|
|
shared_embedding[decoder_starting_stage] = module.decoder.embed_tokens.weight
|
|
|
|
if id(module.lm_head.weight) == id(module.shared.weight):
|
|
shared_embedding[0] = module.shared.weight
|
|
shared_embedding[stage_manager.num_stages - 1] = module.lm_head.weight
|
|
|
|
if len(shared_embedding) > 0:
|
|
shared_params.append(shared_embedding)
|
|
|
|
return shared_params
|
|
|
|
return []
|
|
|
|
|
|
class T5EncoderPolicy(T5BasePolicy):
|
|
|
|
def __init__(self) -> None:
|
|
super().__init__()
|
|
|
|
def module_policy(self):
|
|
from transformers import T5EncoderModel
|
|
|
|
policy = super().module_policy()
|
|
|
|
if self.shard_config.enable_tensor_parallelism:
|
|
self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
|
|
suffix="shared",
|
|
target_module=VocabParallelEmbedding1D,
|
|
),
|
|
policy=policy,
|
|
target_key=T5EncoderModel)
|
|
|
|
if self.pipeline_stage_manager is not None:
|
|
self.set_pipeline_forward(model_cls=T5EncoderModel,
|
|
new_forward=T5PipelineForwards.t5_encoder_model_forward,
|
|
policy=policy)
|
|
return policy
|
|
|
|
def get_held_layers(self) -> List[nn.Module]:
|
|
return super().get_held_layers()
|
|
|
|
def get_shared_params(self) -> List[Dict[int, Tensor]]:
|
|
return []
|