ColossalAI/colossalai/shardformer/policies/sam.py

238 lines
9.2 KiB
Python

import colossalai.shardformer.layer as col_nn
from ..modeling.sam import forward_fn, get_sam_flash_attention_forward, get_sam_vision_flash_attention_forward
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
__all__ = ["SamPolicy", "SamModelPolicy"]
class SamPolicy(Policy):
def config_sanity_check(self):
pass
def preprocess(self):
return self.model
def module_policy(self):
from transformers.models.sam.modeling_sam import (
SamAttention,
SamTwoWayAttentionBlock,
SamTwoWayTransformer,
SamVisionAttention,
SamVisionLayer,
)
policy = {}
if self.shard_config.enable_fused_normalization:
norm_cls = col_nn.FusedLayerNorm
else:
norm_cls = col_nn.LayerNorm
if self.shard_config.enable_tensor_parallelism:
policy[SamVisionLayer] = ModulePolicyDescription(
attribute_replacement={
"attn.num_attention_heads": self.model.config.vision_config.num_attention_heads
// self.shard_config.tensor_parallel_size,
},
sub_module_replacement=[
SubModuleReplacementDescription(
suffix="attn.qkv",
target_module=col_nn.FusedLinear1D_Col,
kwargs={
"n_fused": 3,
},
),
SubModuleReplacementDescription(
suffix="attn.proj",
target_module=col_nn.Linear1D_Row,
),
SubModuleReplacementDescription(
suffix="mlp.lin1",
target_module=col_nn.Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="mlp.lin2",
target_module=col_nn.Linear1D_Row,
),
],
)
policy[SamTwoWayAttentionBlock] = ModulePolicyDescription(
attribute_replacement={
"self_attn.num_attention_heads": self.model.config.mask_decoder_config.num_attention_heads
// self.shard_config.tensor_parallel_size,
},
sub_module_replacement=[
SubModuleReplacementDescription(
suffix="self_attn.q_proj",
target_module=col_nn.Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="self_attn.k_proj",
target_module=col_nn.Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="self_attn.v_proj",
target_module=col_nn.Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="self_attn.out_proj",
target_module=col_nn.Linear1D_Row,
),
SubModuleReplacementDescription(
suffix="cross_attn_token_to_image.q_proj",
target_module=col_nn.Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="cross_attn_token_to_image.k_proj",
target_module=col_nn.Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="cross_attn_token_to_image.v_proj",
target_module=col_nn.Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="cross_attn_token_to_image.out_proj",
target_module=col_nn.Linear1D_Row,
),
SubModuleReplacementDescription(
suffix="mlp.lin1",
target_module=col_nn.Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="mlp.lin2",
target_module=col_nn.Linear1D_Row,
),
SubModuleReplacementDescription(
suffix="cross_attn_image_to_token.q_proj",
target_module=col_nn.Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="cross_attn_image_to_token.k_proj",
target_module=col_nn.Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="cross_attn_image_to_token.v_proj",
target_module=col_nn.Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="cross_attn_image_to_token.out_proj",
target_module=col_nn.Linear1D_Row,
),
],
)
policy[SamTwoWayTransformer] = ModulePolicyDescription(
attribute_replacement={
"final_attn_token_to_image.num_attention_heads": self.model.config.mask_decoder_config.num_attention_heads
// self.shard_config.tensor_parallel_size,
},
sub_module_replacement=[
SubModuleReplacementDescription(
suffix="final_attn_token_to_image.q_proj",
target_module=col_nn.Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="final_attn_token_to_image.k_proj",
target_module=col_nn.Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="final_attn_token_to_image.v_proj",
target_module=col_nn.Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="final_attn_token_to_image.out_proj",
target_module=col_nn.Linear1D_Row,
),
],
)
# add `DropoutForParallelInput` layer to replace the useage of `nn.functional.dropout`
policy[SamVisionAttention] = ModulePolicyDescription(
attribute_replacement={
"dropout_layer": col_nn.DropoutForParallelInput(self.model.config.vision_config.attention_dropout)
},
method_replacement={"forward": forward_fn()},
sub_module_replacement=[],
)
# optimization configuration
# Handle SamVisionLayer
self.append_or_create_submodule_replacement(
description=[
SubModuleReplacementDescription(
suffix="layer_norm1",
target_module=norm_cls,
),
SubModuleReplacementDescription(
suffix="layer_norm2",
target_module=norm_cls,
),
],
policy=policy,
target_key=SamVisionLayer,
)
# Handle SamTwoWayAttentionBlock
self.append_or_create_submodule_replacement(
description=[
SubModuleReplacementDescription(
suffix="layer_norm1",
target_module=norm_cls,
),
SubModuleReplacementDescription(
suffix="layer_norm2",
target_module=norm_cls,
),
SubModuleReplacementDescription(
suffix="layer_norm3",
target_module=norm_cls,
),
SubModuleReplacementDescription(
suffix="layer_norm4",
target_module=norm_cls,
),
],
policy=policy,
target_key=SamTwoWayAttentionBlock,
)
# Handle SamTwoWayTransformer
self.append_or_create_submodule_replacement(
description=[
SubModuleReplacementDescription(
suffix="layer_norm_final_attn",
target_module=norm_cls,
)
],
policy=policy,
target_key=SamTwoWayTransformer,
)
# use flash attention
if self.shard_config.enable_flash_attention:
self.append_or_create_method_replacement(
description={
"forward": get_sam_flash_attention_forward(),
},
policy=policy,
target_key=SamAttention,
)
self.append_or_create_method_replacement(
description={
"forward": get_sam_vision_flash_attention_forward(),
},
policy=policy,
target_key=SamVisionAttention,
)
return policy
def postprocess(self):
return self.model
# SamModel
class SamModelPolicy(SamPolicy):
def __init__(self) -> None:
super().__init__()