mirror of https://github.com/hpcaitech/ColossalAI
141 lines
6.0 KiB
Python
141 lines
6.0 KiB
Python
from colossalai.shardformer.layer import FusedLayerNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D
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from .._utils import getattr_, setattr_
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from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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__all__ = [
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'OPTPolicy', 'OPTModelPolicy', 'OPTForCausalLMPolicy', 'OPTForSequenceClassificationPolicy',
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'OPTForQuestionAnsweringPolicy'
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]
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class OPTPolicy(Policy):
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def config_sanity_check(self):
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pass
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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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from transformers.models.opt.modeling_opt import OPTAttention, OPTDecoder, OPTDecoderLayer
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policy = {}
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if self.shard_config.enable_tensor_parallelism:
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policy[OPTDecoder] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="embed_tokens",
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target_module=VocabParallelEmbedding1D,
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)
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])
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policy[OPTDecoderLayer] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="fc1",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="fc2",
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target_module=Linear1D_Row,
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)
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])
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policy[OPTAttention] = ModulePolicyDescription(attribute_replacement={
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"embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
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"num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size
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},
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="q_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="k_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="v_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="out_proj",
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target_module=Linear1D_Row,
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),
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])
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# optimization configuration
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if self.shard_config.enable_fused_normalization:
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self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
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suffix="final_layer_norm", target_module=FusedLayerNorm, ignore_if_not_exist=True),
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policy=policy,
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target_key=OPTDecoder)
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self.append_or_create_submodule_replacement(description=[
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SubModuleReplacementDescription(suffix="self_attn_layer_norm",
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target_module=FusedLayerNorm,
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ignore_if_not_exist=True),
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SubModuleReplacementDescription(suffix="final_layer_norm",
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target_module=FusedLayerNorm,
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ignore_if_not_exist=True)
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],
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policy=policy,
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target_key=OPTDecoderLayer)
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return policy
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def postprocess(self):
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return self.model
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class OPTModelPolicy(OPTPolicy):
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def __init__(self) -> None:
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super().__init__()
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class OPTForCausalLMPolicy(OPTPolicy):
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def module_policy(self):
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from transformers.models.opt.modeling_opt import OPTForCausalLM
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policy = super().module_policy()
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if self.shard_config.enable_tensor_parallelism:
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self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
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suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True)),
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policy=policy,
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target_key=OPTForCausalLM)
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return policy
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def postprocess(self):
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binding_map = {
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'model.decoder.embed_tokens': 'lm_head',
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}
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for k, v in binding_map.items():
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src_mod = getattr_(self.model, k)
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dst_mod = getattr_(self.model, v)
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dst_mod.weight = src_mod.weight
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return self.model
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class OPTForSequenceClassificationPolicy(OPTPolicy):
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def __init__(self) -> None:
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super().__init__()
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class OPTForQuestionAnsweringPolicy(OPTPolicy):
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def __init__(self) -> None:
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super().__init__()
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