mirror of https://github.com/hpcaitech/ColossalAI
327 lines
19 KiB
Python
327 lines
19 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 ..modeling.blip2 import (
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forward_fn,
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get_blip2_flash_attention_forward,
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get_jit_fused_blip2_QFormer_output_forward,
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get_jit_fused_blip2_QFormer_self_output_forward,
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)
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from ..modeling.jit import get_jit_fused_dropout_add_func
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from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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__all__ = ['BlipPolicy', 'BlipModelPolicy']
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class BlipPolicy(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.qformer_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.blip_2.modeling_blip_2 import (
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Blip2Attention,
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Blip2EncoderLayer,
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Blip2QFormerLayer,
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Blip2QFormerModel,
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Blip2QFormerOutput,
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Blip2QFormerSelfOutput,
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Blip2VisionModel,
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)
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from transformers.models.opt.modeling_opt import OPTDecoderLayer, OPTForCausalLM
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policy = {}
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if self.shard_config.enable_tensor_parallelism:
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policy[Blip2EncoderLayer] = ModulePolicyDescription(attribute_replacement={
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"self_attn.num_heads":
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self.model.config.vision_config.num_attention_heads // self.shard_config.tensor_parallel_size,
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"self_attn.embed_dim":
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self.model.config.vision_config.hidden_size // 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.dropout",
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target_module=col_nn.DropoutForParallelInput,
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),
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SubModuleReplacementDescription(
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suffix="self_attn.qkv",
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target_module=col_nn.FusedLinear1D_Col,
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kwargs={
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"n_fused": 3,
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}),
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SubModuleReplacementDescription(
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suffix="self_attn.projection",
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target_module=col_nn.Linear1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="mlp.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="mlp.fc2",
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target_module=col_nn.Linear1D_Row,
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),
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])
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policy[Blip2QFormerModel] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="dropout",
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target_module=col_nn.DropoutForParallelInput,
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),
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])
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policy[Blip2QFormerLayer] = ModulePolicyDescription(attribute_replacement={
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"attention.attention.num_attention_heads":
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self.model.config.qformer_config.num_attention_heads // self.shard_config.tensor_parallel_size,
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"attention.attention.all_head_size":
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self.model.config.qformer_config.hidden_size // self.shard_config.tensor_parallel_size,
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"crossattention.attention.num_attention_heads":
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self.model.config.qformer_config.num_attention_heads // self.shard_config.tensor_parallel_size,
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"crossattention.attention.all_head_size":
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self.model.config.qformer_config.hidden_size // 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="attention.attention.query",
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target_module=col_nn.Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="attention.attention.key",
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target_module=col_nn.Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="attention.attention.value",
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target_module=col_nn.Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="attention.attention.dropout",
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target_module=col_nn.DropoutForParallelInput,
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),
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SubModuleReplacementDescription(
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suffix="attention.output.dense",
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target_module=col_nn.Linear1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="attention.output.dropout",
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target_module=col_nn.DropoutForParallelInput,
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),
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SubModuleReplacementDescription(
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suffix="crossattention.attention.query",
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target_module=col_nn.Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="crossattention.attention.key",
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target_module=col_nn.Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="crossattention.attention.value",
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target_module=col_nn.Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="crossattention.attention.dropout",
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target_module=col_nn.DropoutForParallelInput,
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),
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SubModuleReplacementDescription(
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suffix="crossattention.output.dense",
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target_module=col_nn.Linear1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="crossattention.output.dropout",
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target_module=col_nn.DropoutForParallelInput,
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),
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SubModuleReplacementDescription(
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suffix="intermediate_query.dense",
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target_module=col_nn.Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="output_query.dense",
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target_module=col_nn.Linear1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="output_query.dropout",
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target_module=col_nn.DropoutForParallelInput,
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)
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])
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policy[OPTDecoderLayer] = ModulePolicyDescription(attribute_replacement={
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"self_attn.embed_dim":
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self.model.config.text_config.hidden_size // self.shard_config.tensor_parallel_size,
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"self_attn.num_heads":
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self.model.config.text_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="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[OPTForCausalLM] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="model.decoder.embed_tokens",
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target_module=col_nn.VocabParallelEmbedding1D,
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),
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SubModuleReplacementDescription(
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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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policy[Blip2Attention] = ModulePolicyDescription(method_replacement={"forward": forward_fn()})
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# optimization configuration
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if self.shard_config.enable_fused_normalization:
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# Handle Blip2EncoderLayer layer
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self.append_or_create_submodule_replacement(description=[
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SubModuleReplacementDescription(
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suffix="layer_norm1",
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target_module=col_nn.FusedLayerNorm,
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),
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SubModuleReplacementDescription(
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suffix="layer_norm2",
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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=Blip2EncoderLayer)
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# handle Blip2VisionModel layer
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self.append_or_create_submodule_replacement(description=[
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SubModuleReplacementDescription(
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suffix="post_layernorm",
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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=Blip2VisionModel)
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# handle Blip2VisionModel layer
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self.append_or_create_submodule_replacement(
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description=[SubModuleReplacementDescription(
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suffix="layernorm",
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target_module=col_nn.FusedLayerNorm,
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)],
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policy=policy,
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target_key=Blip2QFormerModel)
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# handle Blip2QFormerLayer layer
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self.append_or_create_submodule_replacement(description=[
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SubModuleReplacementDescription(
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suffix="attention.output.LayerNorm",
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target_module=col_nn.FusedLayerNorm,
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),
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SubModuleReplacementDescription(
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suffix="crossattention.output.LayerNorm",
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target_module=col_nn.FusedLayerNorm,
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),
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SubModuleReplacementDescription(
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suffix="output_query.LayerNorm",
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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=Blip2QFormerLayer)
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# handle OPTForCausalLM layer
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self.append_or_create_submodule_replacement(description=[
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SubModuleReplacementDescription(
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suffix="model.decoder.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=OPTForCausalLM)
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# handle OPTDecoderLayer 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=OPTDecoderLayer)
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# use flash attention
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if self.shard_config.enable_flash_attention:
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self.append_or_create_method_replacement(description={
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'forward': get_blip2_flash_attention_forward(),
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},
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policy=policy,
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target_key=Blip2Attention)
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# use jit operator
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if self.shard_config.enable_jit_fused:
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self.append_or_create_method_replacement(description={
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'forward': get_jit_fused_blip2_QFormer_self_output_forward(),
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'dropout_add': get_jit_fused_dropout_add_func(),
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},
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policy=policy,
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target_key=Blip2QFormerSelfOutput)
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self.append_or_create_method_replacement(description={
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'forward': get_jit_fused_blip2_QFormer_output_forward(),
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'dropout_add': get_jit_fused_dropout_add_func(),
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},
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policy=policy,
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target_key=Blip2QFormerOutput)
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return policy
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def postprocess(self):
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return self.model
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# Blip2Model
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class Blip2ModelPolicy(BlipPolicy):
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def __init__(self) -> None:
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super().__init__()
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# Blip2ForConditionalGeneration
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class Blip2ForConditionalGenerationPolicy(BlipPolicy):
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def __init__(self) -> None:
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super().__init__()
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