[torch.compile] auto infer dynamic_arg_dims from type annotation (#9589)

vllm/compilation/decorators.py

@@ -1,24 +1,58 @@
 import inspect
-from typing import Dict, List, Union
+from typing import Dict, List, Optional, Union
 
 import torch
 
 import vllm.envs as envs
 from vllm.compilation.levels import CompilationLevel
 from vllm.compilation.wrapper import TorchCompileWrapperWithCustomDispatcher
+from vllm.logger import init_logger
 from vllm.sequence import IntermediateTensors
 from vllm.utils import supports_dynamo
 
+logger = init_logger(__name__)
 
-def support_torch_compile(dynamic_arg_dims: Dict[str, Union[int, List[int]]]):
+
+def support_torch_compile(
+        cls: Optional[type] = None,
+        dynamic_arg_dims: Optional[Dict[str, Union[int, List[int]]]] = None):
     """
     A decorator to add support for compiling the forward method of a class.
 
+    Usage 1: use directly as a decorator without arguments:
+
+    ```python
+    @support_torch_compile
+    class MyModel(nn.Module):
+        def forward(self, x: torch.Tensor, y: Optional[torch.Tensor]):
+            ...
+    ```
+
+    Usage 2: use as a decorator with arguments:
+
+    ```python
+    @support_torch_compile(dynamic_arg_dims={"x": 0, "y": 0})
+    class MyModel(nn.Module):
+        def forward(self, x: torch.Tensor, y: Optional[torch.Tensor]):
+            ...
+    ```
+
     `dynamic_arg_dims` is a dictionary that maps argument names to the dynamic
     dimensions of the argument. The dynamic dimensions can be either a single
     integer or a list of integers.
 
-    Depending on the value of arguments:
+    if `dynamic_arg_dims` is `None`, it is inferred from the type annotation
+    of the `forward` method, based on the following default rules:
+
+    - if the argument is annotated as `torch.Tensor` or
+        `Optional[torch.Tensor]`, the first dimension will be
+        marked as dynamic.
+    - if the argument is annotated as `IntermediateTensors`, the first
+        dimension of all the tensors in the intermediate tensors
+        will be marked as dynamic.
+
+    During runtime, when we actually mark dimensions of tensors,
+     it depends on the value of arguments:
 
     - if it is a single integer, the corresponding dimension of the argument
         will be marked as dynamic.
@@ -38,11 +72,35 @@ def support_torch_compile(dynamic_arg_dims: Dict[str, Union[int, List[int]]]):
         if not hasattr(cls, 'forward'):
             raise TypeError("decorated class should have a forward method.")
         sig = inspect.signature(cls.forward)
-        for k in dynamic_arg_dims:
+        inferred_dynamic_arg_dims = dynamic_arg_dims
+        if inferred_dynamic_arg_dims is None:
+            inferred_dynamic_arg_dims = {}
+            for k, v in sig.parameters.items():
+                if v.annotation in [
+                        torch.Tensor, Optional[torch.Tensor],
+                        IntermediateTensors, Optional[IntermediateTensors]
+                ]:
+                    inferred_dynamic_arg_dims[k] = 0
+
+            logger.debug(("Inferred dynamic dimensions for "
+                          "forward method of %s: %s"), cls,
+                         list(inferred_dynamic_arg_dims.keys()))
+
+        if len(inferred_dynamic_arg_dims) == 0:
+            raise ValueError(
+                "No dynamic dimensions found in the forward method of "
+                f"{cls}. Please provide dynamic_arg_dims explicitly.")
+
+        for k in inferred_dynamic_arg_dims:
             if k not in sig.parameters:
                 raise ValueError(
                     f"Argument {k} not found in the forward method of {cls}")
-        return _support_torch_compile(cls, dynamic_arg_dims)
+        return _support_torch_compile(cls, inferred_dynamic_arg_dims)
+
+    if cls is not None:
+        # use `support_torch_compile` as a decorator without arguments
+        assert isinstance(cls, type)
+        return cls_decorator_helper(cls)
 
     return cls_decorator_helper
 

vllm/model_executor/models/gemma2.py

@@ -241,13 +241,7 @@ class Gemma2DecoderLayer(nn.Module):
         return hidden_states, residual
 
 
-@support_torch_compile(
+@support_torch_compile
-    dynamic_arg_dims={
-        "input_ids": 0,
-        "positions": 0,
-        "inputs_embeds": 0,
-        "intermediate_tensors": 0,
-    })
 class Gemma2Model(nn.Module):
 
     def __init__(

vllm/model_executor/models/llama.py

@@ -268,13 +268,7 @@ class LlamaDecoderLayer(nn.Module):
         return hidden_states, residual
 
 
-@support_torch_compile(
+@support_torch_compile
-    dynamic_arg_dims={
-        "input_ids": 0,
-        "positions": 0,
-        "inputs_embeds": 0,
-        "intermediate_tensors": 0,
-    })
 class LlamaModel(nn.Module):
 
     def __init__(