ColossalAI/colossalai/shardformer/policies/gptj.py

319 lines
12 KiB
Python

import warnings
from functools import partial
from typing import Callable, Dict, List
from torch import Tensor, nn
import colossalai.shardformer.layer as col_nn
from ..modeling.gptj import GPTJPipelineForwards, get_gptj_flash_attention_forward
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
__all__ = [
"GPTJPolicy",
"GPTJModelPolicy",
"GPTJForCausalLMPolicy",
"GPTJForSequenceClassificationPolicy",
"GPTJForQuestionAnsweringPolicy",
"FlaxGPTJPolicy",
"FlaxGPTJForCausalLMPolicy",
]
class GPTJPolicy(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.gptj.modeling_gptj import GPTJAttention, GPTJBlock, GPTJModel
policy = {}
if self.shard_config.enable_sequence_parallelism:
self.shard_config.enable_sequence_parallelism = False
warnings.warn("GPTJ doesn't support sequence parallelism now, will ignore the sequence parallelism flag.")
use_sequence_parallel = self.shard_config.enable_sequence_parallelism
overlap = self.shard_config.enable_sequence_overlap
if self.shard_config.enable_tensor_parallelism:
policy[GPTJModel] = ModulePolicyDescription(
sub_module_replacement=[
SubModuleReplacementDescription(
suffix="wte",
target_module=col_nn.VocabParallelEmbedding1D,
),
SubModuleReplacementDescription(
suffix="drop",
target_module=col_nn.DropoutForParallelInput,
),
]
)
policy[GPTJBlock] = ModulePolicyDescription(
attribute_replacement={
"attn.embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
"attn.num_attention_heads": self.model.config.num_attention_heads
// self.shard_config.tensor_parallel_size,
},
sub_module_replacement=[
SubModuleReplacementDescription(
suffix="attn.k_proj",
target_module=col_nn.Linear1D_Col,
kwargs={"seq_parallel": use_sequence_parallel, "overlap": overlap},
),
SubModuleReplacementDescription(
suffix="attn.q_proj",
target_module=col_nn.Linear1D_Col,
kwargs={"seq_parallel": use_sequence_parallel, "overlap": overlap},
),
SubModuleReplacementDescription(
suffix="attn.v_proj",
target_module=col_nn.Linear1D_Col,
kwargs={"seq_parallel": use_sequence_parallel, "overlap": overlap},
),
SubModuleReplacementDescription(
suffix="attn.out_proj",
target_module=col_nn.Linear1D_Row,
kwargs={"seq_parallel": use_sequence_parallel},
),
SubModuleReplacementDescription(
suffix="mlp.fc_in",
target_module=col_nn.Linear1D_Col,
kwargs={"seq_parallel": use_sequence_parallel},
),
SubModuleReplacementDescription(
suffix="mlp.fc_out",
target_module=col_nn.Linear1D_Row,
kwargs={"seq_parallel": use_sequence_parallel},
),
SubModuleReplacementDescription(
suffix="attn.attn_dropout",
target_module=col_nn.DropoutForParallelInput,
),
SubModuleReplacementDescription(
suffix="attn.resid_dropout",
target_module=col_nn.DropoutForParallelInput,
),
SubModuleReplacementDescription(
suffix="mlp.dropout",
target_module=col_nn.DropoutForParallelInput,
),
],
)
# optimization configuration
if self.shard_config.enable_fused_normalization:
self.append_or_create_submodule_replacement(
description=SubModuleReplacementDescription(
suffix="ln_f",
target_module=col_nn.FusedLayerNorm,
),
policy=policy,
target_key=GPTJModel,
)
self.append_or_create_submodule_replacement(
description=[
SubModuleReplacementDescription(
suffix="ln_1",
target_module=col_nn.FusedLayerNorm,
)
],
policy=policy,
target_key=GPTJBlock,
)
if self.shard_config.enable_flash_attention:
self.append_or_create_method_replacement(
description={
"forward": get_gptj_flash_attention_forward(),
},
policy=policy,
target_key=GPTJAttention,
)
return policy
def postprocess(self):
return self.model
def get_held_layers(self) -> List[nn.Module]:
"""Get pipeline layers for current stage."""
assert self.pipeline_stage_manager is not None
if self.model.__class__.__name__ == "GPTJModel":
module = self.model
else:
module = self.model.transformer
stage_manager = self.pipeline_stage_manager
held_layers = []
layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.wte)
held_layers.append(module.drop)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.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
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
if self.model.__class__.__name__ == "GPTJModel":
module = self.model
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)
method_replacement = {
"forward": partial(
new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config
)
}
self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls)
# GPTJModel
class GPTJModelPolicy(GPTJPolicy):
def __init__(self) -> None:
super().__init__()
def module_policy(self):
from transformers.models.gptj.modeling_gptj import GPTJModel
policy = super().module_policy()
if self.pipeline_stage_manager is not None:
self.set_pipeline_forward(
model_cls=GPTJModel, new_forward=GPTJPipelineForwards.gptj_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]]:
"""No shared params in GPT2Model."""
return []
# GPTJForCausalLM
class GPTJForCausalLMPolicy(GPTJPolicy):
def __init__(self) -> None:
super().__init__()
def module_policy(self):
from transformers.models.gptj.modeling_gptj import GPTJForCausalLM
policy = super().module_policy()
if self.shard_config.enable_tensor_parallelism:
addon_module = {
GPTJForCausalLM: ModulePolicyDescription(
sub_module_replacement=[
SubModuleReplacementDescription(
suffix="lm_head", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}
)
]
)
}
policy.update(addon_module)
if self.pipeline_stage_manager is not None:
self.set_pipeline_forward(
model_cls=GPTJForCausalLM, new_forward=GPTJPipelineForwards.gptj_causallm_model_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]]:
"""The weights of wte and lm_head are shared."""
module = self.model
stage_manager = self.pipeline_stage_manager
if stage_manager is not None:
if stage_manager.num_stages > 1 and id(module.transformer.wte.weight) == id(module.lm_head.weight):
first_stage, last_stage = 0, stage_manager.num_stages - 1
return [{first_stage: module.transformer.wte.weight, last_stage: module.lm_head.weight}]
return []
# GPTJForSequenceClassification
class GPTJForSequenceClassificationPolicy(GPTJPolicy):
def __init__(self) -> None:
super().__init__()
def module_policy(self):
from transformers.models.gptj.modeling_gptj import GPTJForSequenceClassification
policy = super().module_policy()
if self.pipeline_stage_manager is not None:
self.set_pipeline_forward(
model_cls=GPTJForSequenceClassification,
new_forward=GPTJPipelineForwards.gptj_for_sequence_classification_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.score)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
"""No shared params in GPTJForSequenceClassification."""
return []
# GPTJForQuestionAnswering
class GPTJForQuestionAnsweringPolicy(GPTJPolicy):
def __init__(self) -> None:
super().__init__()
def module_policy(self):
from transformers.models.gptj.modeling_gptj import GPTJForQuestionAnswering
policy = super().module_policy()
if self.pipeline_stage_manager is not None:
self.set_pipeline_forward(
model_cls=GPTJForQuestionAnswering,
new_forward=GPTJPipelineForwards.gptj_for_question_answering_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.qa_outputs)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
"""No shared params in GPT2ForQuestionAnswering."""
return []