[Compile] Fix torch warning `TensorFloat32 tensor cores for float32 matrix...

[Compile] Fix torch warning `TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled` (#29897)
Signed-off-by: yewentao256 <zhyanwentao@126.com>

tests/v1/e2e/test_async_scheduling.py

@@ -124,6 +124,8 @@ def run_tests(
     with monkeypatch.context() as m:
         # avoid precision errors
         m.setenv("VLLM_ATTENTION_BACKEND", "FLEX_ATTENTION")
+        # lock matmul precision to full FP32
+        m.setenv("VLLM_FLOAT32_MATMUL_PRECISION", "highest")
         # m.setenv("VLLM_BATCH_INVARIANT", "1")
         outputs: list[tuple[str, list, list]] = []
         for n, (

vllm/envs.py

@@ -75,6 +75,7 @@ if TYPE_CHECKING:
     VLLM_MM_INPUT_CACHE_GIB: int = 4
     VLLM_TARGET_DEVICE: str = "cuda"
     VLLM_MAIN_CUDA_VERSION: str = "12.9"
+    VLLM_FLOAT32_MATMUL_PRECISION: Literal["highest", "high", "medium"] = "highest"
     MAX_JOBS: str | None = None
     NVCC_THREADS: str | None = None
     VLLM_USE_PRECOMPILED: bool = False
@@ -452,6 +453,14 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # Main CUDA version of vLLM. This follows PyTorch but can be overridden.
     "VLLM_MAIN_CUDA_VERSION": lambda: os.getenv("VLLM_MAIN_CUDA_VERSION", "").lower()
     or "12.9",
+    # Controls PyTorch float32 matmul precision mode within vLLM workers.
+    # Valid options mirror torch.set_float32_matmul_precision
+    "VLLM_FLOAT32_MATMUL_PRECISION": env_with_choices(
+        "VLLM_FLOAT32_MATMUL_PRECISION",
+        "highest",
+        ["highest", "high", "medium"],
+        case_sensitive=False,
+    ),
     # Maximum number of compilation jobs to run in parallel.
     # By default this is the number of CPUs
     "MAX_JOBS": lambda: os.getenv("MAX_JOBS", None),

vllm/v1/worker/gpu_worker.py

@@ -79,6 +79,10 @@ class Worker(WorkerBase):
             is_driver_worker=is_driver_worker,
         )
 
+        # configure float32 matmul precision according to vLLM env.
+        precision = envs.VLLM_FLOAT32_MATMUL_PRECISION
+        torch.set_float32_matmul_precision(precision)
+
         if self.model_config.trust_remote_code:
             # note: lazy import to avoid importing torch before initializing
             from vllm.utils.import_utils import init_cached_hf_modules