mirror of https://github.com/hpcaitech/ColossalAI
233 lines
12 KiB
Python
233 lines
12 KiB
Python
import torch.nn as nn
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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 .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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__all__ = [
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'WhisperPolicy', 'WhisperModelPolicy', 'WhisperForConditionalGenerationPolicy', 'WhisperForAudioClassification'
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]
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class WhisperPolicy(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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# TODO:
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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.whisper.modeling_whisper import (
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WhisperDecoder,
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WhisperDecoderLayer,
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WhisperEncoder,
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WhisperEncoderLayer,
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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[WhisperEncoderLayer] = ModulePolicyDescription(attribute_replacement={
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"self_attn.embed_dim":
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self.model.config.d_model // self.shard_config.tensor_parallel_size,
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"self_attn.num_heads":
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self.model.config.encoder_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="self_attn.q_proj",
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target_module=col_nn.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=col_nn.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=col_nn.Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="self_attn.out_proj",
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target_module=col_nn.Linear1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="fc1",
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target_module=col_nn.Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="fc2",
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target_module=col_nn.Linear1D_Row,
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),
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])
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policy[WhisperDecoderLayer] = ModulePolicyDescription(attribute_replacement={
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"self_attn.embed_dim":
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self.model.config.d_model // self.shard_config.tensor_parallel_size,
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"self_attn.num_heads":
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self.model.config.decoder_attention_heads // self.shard_config.tensor_parallel_size,
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"encoder_attn.embed_dim":
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self.model.config.d_model // self.shard_config.tensor_parallel_size,
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"encoder_attn.num_heads":
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self.model.config.encoder_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="self_attn.q_proj",
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target_module=col_nn.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=col_nn.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=col_nn.Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="self_attn.out_proj",
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target_module=col_nn.Linear1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="encoder_attn.q_proj",
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target_module=col_nn.Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="encoder_attn.k_proj",
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target_module=col_nn.Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="encoder_attn.v_proj",
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target_module=col_nn.Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="encoder_attn.out_proj",
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target_module=col_nn.Linear1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="fc1",
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target_module=col_nn.Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="fc2",
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target_module=col_nn.Linear1D_Row,
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),
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])
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policy[WhisperDecoder] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="embed_tokens",
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target_module=col_nn.VocabParallelEmbedding1D,
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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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# Handle encoder layer
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self.append_or_create_submodule_replacement(description=[
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SubModuleReplacementDescription(
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suffix="self_attn_layer_norm",
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target_module=col_nn.FusedLayerNorm,
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),
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SubModuleReplacementDescription(
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suffix="final_layer_norm",
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target_module=col_nn.FusedLayerNorm,
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)
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],
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policy=policy,
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target_key=WhisperEncoderLayer)
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# Handle decoder layer
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self.append_or_create_submodule_replacement(description=[
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SubModuleReplacementDescription(
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suffix="self_attn_layer_norm",
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target_module=col_nn.FusedLayerNorm,
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),
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SubModuleReplacementDescription(
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suffix="final_layer_norm",
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target_module=col_nn.FusedLayerNorm,
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)
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],
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policy=policy,
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target_key=WhisperDecoderLayer)
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# handle encoder layer
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self.append_or_create_submodule_replacement(description=[
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SubModuleReplacementDescription(
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suffix="layer_norm",
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target_module=col_nn.FusedLayerNorm,
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)
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],
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policy=policy,
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target_key=WhisperEncoder)
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# handle decoder layer
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self.append_or_create_submodule_replacement(description=[
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SubModuleReplacementDescription(
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suffix="layer_norm",
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target_module=col_nn.FusedLayerNorm,
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)
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],
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policy=policy,
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target_key=WhisperDecoder)
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return policy
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def add_lm_head_policy(self, base_policy):
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from transformers.models.whisper.modeling_whisper import WhisperForConditionalGeneration
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# optimize for tensor parallelism
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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="proj_out", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}),
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policy=base_policy,
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target_key=WhisperForConditionalGeneration)
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return base_policy
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def postprocess(self):
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return self.model
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# WhisperModel
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class WhisperModelPolicy(WhisperPolicy):
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def __init__(self) -> None:
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super().__init__()
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# WhisperForConditionalGeneration
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class WhisperForConditionalGenerationPolicy(WhisperPolicy):
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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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module_policy = self.add_lm_head_policy(module_policy)
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return module_policy
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def postprocess(self):
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binding_map = {"model.decoder.embed_tokens.weight": "proj_out.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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setattr_(self.model, v, param)
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return self.model
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# WhisperForAudioClassification
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class WhisperForAudioClassificationPolicy(WhisperPolicy):
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def __init__(self) -> None:
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super().__init__()
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