mirror of https://github.com/hpcaitech/ColossalAI
250 lines
11 KiB
Python
250 lines
11 KiB
Python
from colossalai.shardformer.layer import (
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DropoutForParallelInput,
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Embedding1D,
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FusedRMSNorm,
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Linear1D_Col,
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Linear1D_Row,
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VocabParallelEmbedding1D,
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)
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from colossalai.shardformer.policies.basepolicy import ModulePolicyDescription
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from .._utils import getattr_, setattr_
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from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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__all__ = ["T5ModelPolicy", "T5ForConditionalGenerationPolicy", "T5EncoderPolicy"]
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class T5BasePolicy(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.t5.modeling_t5 import (
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T5Attention,
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T5DenseActDense,
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T5DenseGatedActDense,
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T5LayerCrossAttention,
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T5LayerFF,
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T5LayerSelfAttention,
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T5Stack,
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)
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policy = {}
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if self.shard_config.enable_tensor_parallelism:
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policy[T5Stack] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="dropout",
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target_module=DropoutForParallelInput,
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),
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SubModuleReplacementDescription(
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suffix="embed_tokens",
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target_module=Embedding1D,
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)
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])
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policy[T5LayerSelfAttention] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="dropout",
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target_module=DropoutForParallelInput,
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),
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])
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policy[T5LayerCrossAttention] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="dropout",
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target_module=DropoutForParallelInput,
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)
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])
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policy[T5Attention] = ModulePolicyDescription(attribute_replacement={
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"d_model":
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self.model.config.d_model // self.shard_config.tensor_parallel_size,
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"n_heads":
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self.model.config.num_heads // self.shard_config.tensor_parallel_size,
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"inner_dim":
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self.model.config.num_heads * self.model.config.d_kv // 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",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="k",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="v",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="o",
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target_module=Linear1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="relative_attention_bias",
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target_module=Embedding1D,
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kwargs=dict(gather_output=False),
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ignore_if_not_exist=True)
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])
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policy[T5LayerFF] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="dropout",
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target_module=DropoutForParallelInput,
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),
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])
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policy[T5DenseGatedActDense] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="wi_0",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="wi_1",
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target_module=Linear1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="wo", target_module=Linear1D_Col, kwargs=dict(gather_output=True)),
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SubModuleReplacementDescription(
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suffix="dropout",
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target_module=DropoutForParallelInput,
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)
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])
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policy[T5DenseActDense] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="wi",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="wo",
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target_module=Linear1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="dropout",
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target_module=DropoutForParallelInput,
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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="layer_norm",
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target_module=FusedRMSNorm,
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),
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policy=policy,
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target_key=T5LayerFF)
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self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
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suffix="layer_norm",
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target_module=FusedRMSNorm,
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),
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policy=policy,
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target_key=T5LayerFF)
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self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
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suffix="layer_norm", target_module=FusedRMSNorm),
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policy=policy,
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target_key=T5LayerSelfAttention)
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self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
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suffix="layer_norm", target_module=FusedRMSNorm),
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policy=policy,
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target_key=T5LayerCrossAttention)
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self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
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suffix="final_layer_norm", target_module=FusedRMSNorm),
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policy=policy,
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target_key=T5Stack)
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return policy
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def postprocess(self):
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binding_map = [["shared", "encoder.embed_tokens"], ["shared", "decoder.embed_tokens"]]
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for k, v in binding_map:
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mod = getattr_(self.model, k)
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setattr_(self.model, v, mod)
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return self.model
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class T5ModelPolicy(T5BasePolicy):
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def module_policy(self):
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from transformers import T5Model
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base_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="shared",
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target_module=VocabParallelEmbedding1D,
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),
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policy=base_policy,
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target_key=T5Model)
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return base_policy
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class T5ForConditionalGenerationPolicy(T5BasePolicy):
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def module_policy(self):
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from transformers import T5ForConditionalGeneration
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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=[
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SubModuleReplacementDescription(
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suffix="shared",
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target_module=VocabParallelEmbedding1D,
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),
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SubModuleReplacementDescription(suffix="lm_head",
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target_module=Linear1D_Col,
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kwargs=dict(gather_output=True))
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],
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policy=policy,
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target_key=T5ForConditionalGeneration)
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return policy
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def postprocess(self):
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super().postprocess()
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binding_map = {"shared": "lm_head"}
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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 T5EncoderPolicy(T5BasePolicy):
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def module_policy(self):
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from transformers import T5EncoderModel
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base_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="shared",
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target_module=VocabParallelEmbedding1D,
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),
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policy=base_policy,
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target_key=T5EncoderModel)
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return base_policy
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def postprocess(self):
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binding_map = [
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["shared", "encoder.embed_tokens"],
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]
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for k, v in binding_map:
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mod = getattr_(self.model, k)
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setattr_(self.model, v, mod)
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return self.model
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