mirror of https://github.com/hpcaitech/ColossalAI
168 lines
7.2 KiB
Python
168 lines
7.2 KiB
Python
import torch.nn as nn
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from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2DoubleHeadsModel, GPT2LMHeadModel, GPT2Model
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import colossalai.shardformer.layer as col_nn
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from .._utils import getattr_, setattr_
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from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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class GPT2Policy(Policy):
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def preprocess(self):
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# reshape the embedding layer
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r"""
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Reshape the Embedding layer to make the embedding dimension divisible by world_size
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"""
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vocab_size = self.model.config.vocab_size
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world_size = self.shard_config.tensor_parallel_size
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if vocab_size % world_size != 0:
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new_vocab_size = vocab_size + world_size - vocab_size % world_size
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self.model.resize_token_embeddings(new_vocab_size)
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return self.model
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def module_policy(self):
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return {
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GPT2Model:
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ModulePolicyDescription(attribute_replacement={},
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param_replacement=[],
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="wte",
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target_module=col_nn.VocabParallelEmbedding1D,
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),
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]),
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GPT2Block:
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ModulePolicyDescription(attribute_replacement={
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"attn.embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
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"attn.split_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
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"attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
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},
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param_replacement=[],
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="attn.c_attn",
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target_module=col_nn.GPT2FusedLinearConv1D_Col,
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kwargs={
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"n_fused": 3,
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},
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),
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SubModuleReplacementDescription(
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suffix="attn.c_proj",
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target_module=col_nn.GPT2FusedLinearConv1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="mlp.c_fc",
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target_module=col_nn.GPT2FusedLinearConv1D_Col,
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kwargs={
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"n_fused": 1,
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},
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),
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SubModuleReplacementDescription(
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suffix="mlp.c_proj",
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target_module=col_nn.GPT2FusedLinearConv1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="attn.attn_dropout",
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target_module=col_nn.DropoutForParallelInput,
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),
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SubModuleReplacementDescription(
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suffix="attn.resid_dropout",
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target_module=col_nn.DropoutForParallelInput,
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),
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SubModuleReplacementDescription(
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suffix="mlp.dropout",
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target_module=col_nn.DropoutForParallelInput,
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),
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])
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}
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def new_model_class(self):
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return self.model
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def postprocess(self):
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return self.model
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# GPT2Model
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class GPT2ModelPolicy(GPT2Policy):
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def __init__(self) -> None:
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super().__init__()
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# GPT2LMHeadModel
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class GPT2LMHeadModelPolicy(GPT2Policy):
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def __init__(self) -> None:
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super().__init__()
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def module_policy(self):
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module_policy = super().module_policy()
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addon_module = {
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GPT2LMHeadModel:
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ModulePolicyDescription(attribute_replacement={},
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param_replacement=[],
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sub_module_replacement=[
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SubModuleReplacementDescription(suffix="lm_head",
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target_module=col_nn.Linear1D_Col,
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kwargs={"gather_output": True})
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])
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}
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module_policy.update(addon_module)
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return module_policy
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def postprocess(self):
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binding_map = {"transformer.wte.weight": "lm_head.weight"}
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for k, v in binding_map.items():
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param = getattr_(self.model, k)
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param = nn.Parameter(param)
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setattr_(self.model, k, param)
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setattr_(self.model, v, param)
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return self.model
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# GPT22DoubleHeadsModel
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class GPT2DoubleHeadsModelPolicy(GPT2Policy):
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def __init__(self) -> None:
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super().__init__()
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def module_policy(self):
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module_policy = super().module_policy()
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addon_module = {
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GPT2DoubleHeadsModel:
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ModulePolicyDescription(attribute_replacement={},
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param_replacement=[],
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sub_module_replacement=[
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SubModuleReplacementDescription(suffix="lm_head",
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target_module=col_nn.Linear1D_Col,
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kwargs={"gather_output": True})
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])
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}
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module_policy.update(addon_module)
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return module_policy
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def postprocess(self):
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binding_map = {"transformer.wte.weight": "lm_head.weight"}
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for k, v in binding_map.items():
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param = getattr_(self.model, k)
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param = nn.Parameter(param)
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setattr_(self.model, k, param)
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setattr_(self.model, v, param)
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return self.model
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# GPT2ForTokenClassification
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class GPT2ForTokenClassificationPolicy(GPT2Policy):
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def __init__(self) -> None:
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super().__init__()
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# GPT2ForSequenceClassification
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class GPT2ForSequenceClassificationPolicy(GPT2Policy):
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def __init__(self) -> None:
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super().__init__()
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