mirror of https://github.com/hpcaitech/ColossalAI
122 lines
5.0 KiB
Python
122 lines
5.0 KiB
Python
from typing import Dict, Union
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import torch.nn as nn
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from transformers import LlamaForCausalLM, LlamaForSequenceClassification
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from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaModel
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from colossalai.shardformer.layer import Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D
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from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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class LlamaPolicy(Policy):
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def preprocess(self):
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# Resize embedding
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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) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
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return {
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LlamaDecoderLayer:
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ModulePolicyDescription(
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attribute_replacement={
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"self_attn.hidden_size":
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self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
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"self_attn.num_heads":
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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="self_attn.q_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="self_attn.k_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="self_attn.v_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="self_attn.o_proj",
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target_module=Linear1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="mlp.gate_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="mlp.up_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="mlp.down_proj",
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target_module=Linear1D_Row,
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)
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],
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),
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LlamaModel:
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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="embed_tokens",
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target_module=VocabParallelEmbedding1D,
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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 None
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def postprocess(self):
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return self.model
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class LlamaForCausalLMPolicy(LlamaPolicy):
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def module_policy(self):
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policy = super().module_policy()
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# add a new item for casual lm
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new_item = {
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LlamaForCausalLM:
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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=Linear1D_Col,
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kwargs=dict(gather_output=True))
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])
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}
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policy.update(new_item)
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return policy
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class LlamaForSequenceClassificationPolicy(LlamaPolicy):
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def module_policy(self):
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policy = super().module_policy()
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# add a new item for sequence classification
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new_item = {
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LlamaForSequenceClassification:
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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="score",
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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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}
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policy.update(new_item)
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return policy
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