[Misc] Add a wrapper for torch.inference_mode (#6618)

vllm/platforms/__init__.py

@@ -2,7 +2,9 @@ from typing import Optional
 
 import torch
 
-from .interface import Platform, PlatformEnum
+from vllm.utils import is_tpu
+
+from .interface import Platform, PlatformEnum, UnspecifiedPlatform
 
 current_platform: Optional[Platform]
 
@@ -12,7 +14,10 @@ if torch.version.cuda is not None:
 elif torch.version.hip is not None:
     from .rocm import RocmPlatform
     current_platform = RocmPlatform()
+elif is_tpu():
+    from .tpu import TpuPlatform
+    current_platform = TpuPlatform()
 else:
-    current_platform = None
+    current_platform = UnspecifiedPlatform()
 
 __all__ = ['Platform', 'PlatformEnum', 'current_platform']

vllm/platforms/interface.py

@@ -1,10 +1,14 @@
 import enum
 from typing import Tuple
 
+import torch
+
 
 class PlatformEnum(enum.Enum):
     CUDA = enum.auto()
     ROCM = enum.auto()
+    TPU = enum.auto()
+    UNSPECIFIED = enum.auto()
 
 
 class Platform:
@@ -16,6 +20,23 @@ class Platform:
     def is_rocm(self) -> bool:
         return self._enum == PlatformEnum.ROCM
 
+    def is_tpu(self) -> bool:
+        return self._enum == PlatformEnum.TPU
+
     @staticmethod
     def get_device_capability(device_id: int = 0) -> Tuple[int, int]:
         raise NotImplementedError
+
+    @staticmethod
+    def inference_mode():
+        """A device-specific wrapper of `torch.inference_mode`.
+
+        This wrapper is recommended because some hardware backends such as TPU
+        do not support `torch.inference_mode`. In such a case, they will fall
+        back to `torch.no_grad` by overriding this method.
+        """
+        return torch.inference_mode(mode=True)
+
+
+class UnspecifiedPlatform(Platform):
+    _enum = PlatformEnum.UNSPECIFIED

vllm/platforms/tpu.py

@@ -0,0 +1,17 @@
+from typing import Tuple
+
+import torch
+
+from .interface import Platform, PlatformEnum
+
+
+class TpuPlatform(Platform):
+    _enum = PlatformEnum.TPU
+
+    @staticmethod
+    def get_device_capability(device_id: int = 0) -> Tuple[int, int]:
+        raise RuntimeError("TPU does not have device capability.")
+
+    @staticmethod
+    def inference_mode():
+        return torch.no_grad()

vllm/worker/model_runner_base.py

@@ -5,6 +5,7 @@ from typing import (TYPE_CHECKING, Any, Dict, Generic, List, Optional, Type,
 
 import torch
 
+from vllm.platforms import current_platform
 from vllm.sequence import (IntermediateTensors, SamplerOutput,
                            SequenceGroupMetadata)
 
@@ -163,7 +164,7 @@ class ModelRunnerBase(ABC, Generic[T]):
         """
         raise NotImplementedError
 
-    @torch.inference_mode()
+    @current_platform.inference_mode()
     def execute_model(
         self,
         model_input: T,

vllm/worker/worker_base.py

@@ -9,6 +9,7 @@ import torch
 from vllm.distributed import broadcast_tensor_dict, get_pp_group
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
+from vllm.platforms import current_platform
 from vllm.sequence import (ExecuteModelRequest, IntermediateTensors,
                            SamplerOutput)
 from vllm.utils import (enable_trace_function_call_for_thread,
@@ -53,7 +54,7 @@ class WorkerBase(ABC):
         """
         raise NotImplementedError
 
-    @torch.inference_mode()
+    @current_platform.inference_mode()
     def start_worker_execution_loop(self) -> None:
         """Execute model loop in parallel worker.