ColossalAI/colossalai/shardformer/policies/gpt2.py

168 lines
7.2 KiB
Python

import torch.nn as nn
from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2DoubleHeadsModel, GPT2LMHeadModel, GPT2Model
import colossalai.shardformer.layer as col_nn
from .._utils import getattr_, setattr_
from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
class GPT2Policy(Policy):
def preprocess(self):
# reshape the embedding layer
r"""
Reshape the Embedding layer to make the embedding dimension divisible by world_size
"""
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):
return {
GPT2Model:
ModulePolicyDescription(attribute_replacement={},
param_replacement=[],
sub_module_replacement=[
SubModuleReplacementDescription(
suffix="wte",
target_module=col_nn.VocabParallelEmbedding1D,
),
]),
GPT2Block:
ModulePolicyDescription(attribute_replacement={
"attn.embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
"attn.split_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
"attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
},
param_replacement=[],
sub_module_replacement=[
SubModuleReplacementDescription(
suffix="attn.c_attn",
target_module=col_nn.GPT2FusedLinearConv1D_Col,
kwargs={
"n_fused": 3,
},
),
SubModuleReplacementDescription(
suffix="attn.c_proj",
target_module=col_nn.GPT2FusedLinearConv1D_Row,
),
SubModuleReplacementDescription(
suffix="mlp.c_fc",
target_module=col_nn.GPT2FusedLinearConv1D_Col,
kwargs={
"n_fused": 1,
},
),
SubModuleReplacementDescription(
suffix="mlp.c_proj",
target_module=col_nn.GPT2FusedLinearConv1D_Row,
),
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,
),
])
}
def new_model_class(self):
return self.model
def postprocess(self):
return self.model
# GPT2Model
class GPT2ModelPolicy(GPT2Policy):
def __init__(self) -> None:
super().__init__()
# GPT2LMHeadModel
class GPT2LMHeadModelPolicy(GPT2Policy):
def __init__(self) -> None:
super().__init__()
def module_policy(self):
module_policy = super().module_policy()
addon_module = {
GPT2LMHeadModel:
ModulePolicyDescription(attribute_replacement={},
param_replacement=[],
sub_module_replacement=[
SubModuleReplacementDescription(suffix="lm_head",
target_module=col_nn.Linear1D_Col,
kwargs={"gather_output": True})
])
}
module_policy.update(addon_module)
return module_policy
def postprocess(self):
binding_map = {"transformer.wte.weight": "lm_head.weight"}
for k, v in binding_map.items():
param = getattr_(self.model, k)
param = nn.Parameter(param)
setattr_(self.model, k, param)
setattr_(self.model, v, param)
return self.model
# GPT22DoubleHeadsModel
class GPT2DoubleHeadsModelPolicy(GPT2Policy):
def __init__(self) -> None:
super().__init__()
def module_policy(self):
module_policy = super().module_policy()
addon_module = {
GPT2DoubleHeadsModel:
ModulePolicyDescription(attribute_replacement={},
param_replacement=[],
sub_module_replacement=[
SubModuleReplacementDescription(suffix="lm_head",
target_module=col_nn.Linear1D_Col,
kwargs={"gather_output": True})
])
}
module_policy.update(addon_module)
return module_policy
def postprocess(self):
binding_map = {"transformer.wte.weight": "lm_head.weight"}
for k, v in binding_map.items():
param = getattr_(self.model, k)
param = nn.Parameter(param)
setattr_(self.model, k, param)
setattr_(self.model, v, param)
return self.model
# GPT2ForTokenClassification
class GPT2ForTokenClassificationPolicy(GPT2Policy):
def __init__(self) -> None:
super().__init__()
# GPT2ForSequenceClassification
class GPT2ForSequenceClassificationPolicy(GPT2Policy):
def __init__(self) -> None:
super().__init__()