Move bfloat16 check to worker (#1259)

vllm/config.py

@@ -345,15 +345,6 @@ def _get_and_verify_dtype(
             # Casting between float16 and bfloat16 is allowed with a warning.
             logger.warning(f"Casting {config_dtype} to {torch_dtype}.")
 
-    # Check if the GPU supports the dtype.
-    if torch_dtype == torch.bfloat16:
-        compute_capability = torch.cuda.get_device_capability()
-        if compute_capability[0] < 8:
-            gpu_name = torch.cuda.get_device_name()
-            raise ValueError(
-                "Bfloat16 is only supported on GPUs with compute capability "
-                f"of at least 8.0. Your {gpu_name} GPU has compute capability "
-                f"{compute_capability[0]}.{compute_capability[1]}.")
     return torch_dtype
 
 

vllm/worker/worker.py

@@ -59,6 +59,8 @@ class Worker:
             raise ValueError("Invalid or unspecified rank.")
         torch.cuda.set_device(self.device)
 
+        _check_if_gpu_supports_dtype(self.model_config.dtype)
+
         # Initialize the distributed environment.
         _init_distributed_environment(self.parallel_config, self.rank,
                                       self.distributed_init_method)
@@ -385,3 +387,15 @@ def _check_if_can_support_max_seq_len(max_seq_len: int,
             f"(required shared memory {required_shared_mem} > "
             f"available shared memory {max_shared_mem}). "
             "This will be fixed in a future release.")
+
+
+def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype):
+    # Check if the GPU supports the dtype.
+    if torch_dtype == torch.bfloat16:
+        compute_capability = torch.cuda.get_device_capability()
+        if compute_capability[0] < 8:
+            gpu_name = torch.cuda.get_device_name()
+            raise ValueError(
+                "Bfloat16 is only supported on GPUs with compute capability "
+                f"of at least 8.0. Your {gpu_name} GPU has compute capability "
+                f"{compute_capability[0]}.{compute_capability[1]}.")