[Hotfix] Fix model policy matching strategy in ShardFormer (#5064)

* hotfix/Fix get model policy strategy in ShardFormer

* fix bug in auto policy

colossalai/inference/README.md

@@ -32,7 +32,7 @@ Colossal Inference is composed of three main components:
 
 In this section we discuss how the colossal inference works and integrates with the `Shardformer` . The details can be found in our codes.
 
-![Colossal-Inference](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/inference/inference-arch.png)
+<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/inference/inference-arch.png" alt="Colossal-Inference" style="zoom: 33%;"/>
 
 ## Roadmap of our implementation
 

colossalai/shardformer/policies/auto_policy.py

 import importlib
 from dataclasses import dataclass
-from typing import Optional
 
 import torch.nn as nn
 
-from ..shard.shard_config import ShardConfig
 from .base_policy import Policy
 
 __all__ = ["PolicyLocation", "get_autopolicy", "import_policy"]
@@ -150,38 +148,11 @@ _POLICY_LIST = {
     ),
 }
 
-_INFER_POLICY_LIST = {
-    # LlaMa
-    "transformers.models.llama.modeling_llama.LlamaModel": PolicyLocation(
-        file_name="llama", class_name="LlamaModelInferPolicy"
-    ),
-    "transformers.models.llama.modeling_llama.LlamaForCausalLM": PolicyLocation(
-        file_name="llama", class_name="LlamaModelInferPolicy"
-    ),
-    # Bloom
-    "transformers.models.bloom.modeling_bloom.BloomModel": PolicyLocation(
-        file_name="bloom", class_name="BloomModelInferPolicy"
-    ),
-    "transformers.models.bloom.modeling_bloom.BloomForCausalLM": PolicyLocation(
-        file_name="bloom", class_name="BloomModelInferPolicy"
-    ),
-    # ChatGLM2
-    "colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm.ChatGLMModel": PolicyLocation(
-        file_name="chatglm2", class_name="ChatGLM2InferPolicy"
-    ),
-    "colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm.ChatGLMForConditionalGeneration": PolicyLocation(
-        file_name="chatglm2", class_name="ChatGLM2ForConditionalGenerationInferPolicy"
-    ),
-}
-
 
-def import_policy(policy_location: PolicyLocation, inference_only: Optional[bool] = False) -> Policy:
+def import_policy(policy_location: PolicyLocation) -> Policy:
     """
     Dynamically import a Policy class based on the policy location.
     """
-    if inference_only:
-        module_name = f"colossalai.inference.tensor_parallel.policies.{policy_location.file_name}"
-    else:
     module_name = f"colossalai.shardformer.policies.{policy_location.file_name}"
     module = importlib.import_module(module_name)
     return getattr(module, policy_location.class_name)
@@ -198,7 +169,7 @@ def _fullname(obj):
     return module + "." + klass.__qualname__
 
 
-def get_autopolicy(model: nn.Module, shard_config: ShardConfig = None) -> Policy:
+def get_autopolicy(model: nn.Module) -> Policy:
     r"""
     Return the auto policy for the model
 
@@ -209,16 +180,12 @@ def get_autopolicy(model: nn.Module, shard_config: ShardConfig = None) -> Policy
         :class:`Policy`: The auto policy for the model
     """
     full_name = _fullname(model)
-    inference_only = shard_config.extra_kwargs.get("inference_only", None)
-    if inference_only:
-        policy_location = _INFER_POLICY_LIST.get(full_name, None)
-    else:
     policy_location = _POLICY_LIST.get(full_name, None)
 
     if policy_location is None:
         raise NotImplementedError(
-            f"Auto policy for {model.__class__.__qualname__} is not implemented\n. Supported models are {list(_POLICY_LIST.keys())} and {list(_INFER_POLICY_LIST.keys())}"
+            f"Auto policy for {model.__class__.__qualname__} is not implemented\n. Supported models are {list(_POLICY_LIST.keys())}"
         )
     else:
-        policy = import_policy(policy_location, inference_only)
+        policy = import_policy(policy_location)
     return policy()

colossalai/shardformer/shard/sharder.py

@@ -28,7 +28,7 @@ class ModelSharder(object):
     def __init__(self, model: nn.Module, policy: Policy, shard_config: ShardConfig = None) -> None:
         self.model = model
         self.shard_config = shard_config
-        self.policy = get_autopolicy(self.model, shard_config) if policy is None else policy
+        self.policy = get_autopolicy(self.model) if policy is None else policy
 
     def shard(self) -> List[Dict[int, Tensor]]:
         r"""

tests/test_infer/_utils.py

@@ -19,7 +19,6 @@ def build_model(
         enable_tensor_parallelism=enable_tensor_parallelism,
         enable_flash_attention=enable_flash_attention,
         enable_jit_fused=enable_jit_fused,
-        extra_kwargs={"inference_only": True},
     )
     model_copy = copy.deepcopy(org_model)
     shard_former = ShardFormer(shard_config=shard_config)