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[shardformer] support ChatGLMForConditionalGeneration & add fusedlayernorm for vit

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klhhhhh 2023-07-20 19:14:04 +08:00 committed by Hongxin Liu
parent 4da05052f4
commit 8120eca0c0
4 changed files with 37 additions and 4 deletions
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24
colossalai/shardformer/policies/chatglm.py
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@ -90,7 +90,31 @@ class ChatGLMModelPolicy(Policy):
policy=policy, policy=policy,
target_key=ChatGLMModel) target_key=ChatGLMModel)
else:
self.append_or_create_submodule_replacement(description=[
SubModuleReplacementDescription(suffix="input_layernorm", target_module=col_nn.FusedRMSNorm),
SubModuleReplacementDescription(suffix="post_attention_layernorm",
target_module=col_nn.FusedRMSNorm)
],
policy=policy,
target_key=GLMBlock)
if self.model.config.post_layer_norm:
self.append_or_create_submodule_replacement(description=[
SubModuleReplacementDescription(suffix="encoder.final_layernorm",
target_module=col_nn.FusedRMSNorm)
],
policy=policy,
target_key=ChatGLMModel)
return policy return policy
def postprocess(self): def postprocess(self):
return self.model return self.model
class ChatGLMForConditionalGenerationPolicy(ChatGLMModelPolicy):
def module_policy(self):
policy = super().module_policy()
return policy

2
colossalai/shardformer/policies/vit.py
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@ -23,7 +23,7 @@ class ViTPolicy(Policy):
return self.model return self.model
def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
from transformers.models.vit.modeling_vit import ViTEmbeddings, ViTLayer from transformers.models.vit.modeling_vit import ViTEmbeddings, ViTLayer, ViTModel
policy = {} policy = {}

11
tests/kit/model_zoo/transformers/chatglm.py
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@ -3,7 +3,7 @@ import transformers
from ..registry import ModelAttribute, model_zoo from ..registry import ModelAttribute, model_zoo
from .chatglm2_6b.configuration_chatglm import ChatGLMConfig from .chatglm2_6b.configuration_chatglm import ChatGLMConfig
from .chatglm2_6b.modeling_chatglm import ChatGLMModel from .chatglm2_6b.modeling_chatglm import ChatGLMForConditionalGeneration, ChatGLMModel
# ================================ # ================================
# Register single-sentence ChatGLM # Register single-sentence ChatGLM
@ -21,7 +21,7 @@ output_transform_fn = lambda x: x
# define loss function # define loss function
loss_fn_for_chatglm_model = lambda x: x.last_hidden_state.mean() loss_fn_for_chatglm_model = lambda x: x.last_hidden_state.mean()
loss_fn = lambda x: x.loss loss_fn = lambda x: x.logits.mean()
config = ChatGLMConfig(num_layers=1, config = ChatGLMConfig(num_layers=1,
padded_vocab_size=65024, padded_vocab_size=65024,
hidden_size=64, hidden_size=64,
@ -36,3 +36,10 @@ model_zoo.register(name='transformers_chatglm',
output_transform_fn=output_transform_fn, output_transform_fn=output_transform_fn,
loss_fn=loss_fn_for_chatglm_model, loss_fn=loss_fn_for_chatglm_model,
model_attribute=ModelAttribute(has_control_flow=True)) model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name="transformers_chatglm_for_conditional_generation",
model_fn=lambda: ChatGLMForConditionalGeneration(config, empty_init=False),
data_gen_fn=data_gen,
output_transform_fn=output_transform_fn,
loss_fn=loss_fn,
model_attribute=ModelAttribute(has_control_flow=True))

4
tests/test_shardformer/test_model/test_shard_chatglm.py
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@ -7,7 +7,7 @@ import torch
import colossalai import colossalai
from colossalai.logging import disable_existing_loggers from colossalai.logging import disable_existing_loggers
from colossalai.shardformer import ShardConfig, ShardFormer from colossalai.shardformer import ShardConfig, ShardFormer
from colossalai.shardformer.policies.chatglm import ChatGLMModelPolicy from colossalai.shardformer.policies.chatglm import ChatGLMForConditionalGenerationPolicy, ChatGLMModelPolicy
from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
from colossalai.testing import ( from colossalai.testing import (
assert_hf_output_close, assert_hf_output_close,
@ -85,6 +85,8 @@ def run_chatglm_test(enable_fused_normalization, enable_tensor_parallelism):
shard_former = ShardFormer(shard_config=shard_config) shard_former = ShardFormer(shard_config=shard_config)
if name == "transformers_chatglm": if name == "transformers_chatglm":
sharded_model = shard_former.optimize(model_copy, ChatGLMModelPolicy()).cuda() sharded_model = shard_former.optimize(model_copy, ChatGLMModelPolicy()).cuda()
else:
sharded_model = shard_former.optimize(model_copy, ChatGLMForConditionalGenerationPolicy()).cuda()
check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
torch.cuda.empty_cache() torch.cuda.empty_cache()