[platform] Move get_cu_count to utils

tests/kernels/quantization/test_rocm_skinny_gemms.py

@@ -8,6 +8,7 @@
 import vllm._custom_ops as ops
 from tests.kernels.quant_utils import ref_dynamic_per_tensor_fp8_quant
 from vllm.platforms import current_platform
+from vllm.utils.platform_utils import get_cu_count
 
 DTYPES = [torch.bfloat16, torch.float16]
 # Specific (N, K, M) combinations for targeted testing
@@ -85,7 +86,7 @@ def test_rocm_llmm1_kernel(n, k, m, dtype, rows_per_block, seed):
 @pytest.mark.skipif(not current_platform.is_rocm(), reason="only test for rocm")
 def test_rocm_wvsplitk_kernel(n, k, m, dtype, seed):
     torch.manual_seed(seed)
-    cu_count = current_platform.get_cu_count()
+    cu_count = get_cu_count()
 
     A = torch.rand(n, k, dtype=dtype, device="cuda") - 0.5
     B = torch.rand(m, k, dtype=dtype, device="cuda") - 0.5
@@ -102,7 +103,7 @@ def test_rocm_wvsplitk_kernel(n, k, m, dtype, seed):
 @pytest.mark.skipif(not current_platform.is_rocm(), reason="only test for rocm")
 def test_rocm_wvsplitk_bias1D_kernel(n, k, m, dtype, seed):
     torch.manual_seed(seed)
-    cu_count = current_platform.get_cu_count()
+    cu_count = get_cu_count()
 
     xavier = math.sqrt(2 / k)  # normalize to avoid large output-bias deltas
     A = (torch.rand(n, k, dtype=dtype, device="cuda") - 0.5) * xavier
@@ -121,7 +122,7 @@ def test_rocm_wvsplitk_bias1D_kernel(n, k, m, dtype, seed):
 @pytest.mark.skipif(not current_platform.is_rocm(), reason="only test for rocm")
 def test_rocm_wvsplitk_bias2D_kernel(n, k, m, dtype, seed):
     torch.manual_seed(seed)
-    cu_count = current_platform.get_cu_count()
+    cu_count = get_cu_count()
 
     xavier = math.sqrt(2 / k)  # normalize to avoid large output-bias deltas
     A = (torch.rand(n, k, dtype=dtype, device="cuda") - 0.5) * xavier
@@ -153,7 +154,14 @@ def test_rocm_wvsplitk_fp8_kernel(n, k, m, dtype, seed):
     ref_out = torch._scaled_mm(
         A, B.t(), out_dtype=dtype, scale_a=scale_a, scale_b=scale_b
     )
-    out = ops.wvSplitKQ(B, A, dtype, scale_a, scale_b, current_platform.get_cu_count())
+    out = ops.wvSplitKQ(
+        B,
+        A,
+        dtype,
+        scale_a,
+        scale_b,
+        get_cu_count(),
+    )
 
     assert torch.allclose(out, ref_out, rtol=0.01)
 
@@ -180,7 +188,13 @@ def test_rocm_wvsplitk_fp8_bias1D_kernel(n, k, m, dtype, seed):
         A, B.t(), out_dtype=dtype, scale_a=scale_a, scale_b=scale_b, bias=BIAS
     )
     out = ops.wvSplitKQ(
-        B, A, dtype, scale_a, scale_b, current_platform.get_cu_count(), BIAS
+        B,
+        A,
+        dtype,
+        scale_a,
+        scale_b,
+        get_cu_count(),
+        BIAS,
     )
 
     assert torch.allclose(out, ref_out, rtol=0.01)

vllm/model_executor/layers/quantization/utils/w8a8_utils.py

@@ -13,6 +13,7 @@
 from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
 from vllm.platforms import current_platform
 from vllm.utils.flashinfer import flashinfer_scaled_fp8_mm, has_flashinfer
+from vllm.utils.platform_utils import get_cu_count
 from vllm.utils.torch_utils import direct_register_custom_op
 
 # Input scaling factors are no longer optional in _scaled_mm starting
@@ -200,7 +201,7 @@ def rocm_per_tensor_w8a8_scaled_mm_impl(
             out_dtype,
             scale_a,
             scale_b,
-            current_platform.get_cu_count(),
+            get_cu_count(),
             bias,
         )
     else:

vllm/model_executor/layers/utils.py

@@ -10,6 +10,7 @@
 from vllm import envs
 from vllm.logger import init_logger
 from vllm.platforms import CpuArchEnum, current_platform
+from vllm.utils.platform_utils import get_cu_count
 from vllm.utils.torch_utils import direct_register_custom_op
 
 logger = init_logger(__name__)
@@ -151,7 +152,7 @@ def rocm_unquantized_gemm_impl(
 
     x_view = x.reshape(-1, x.size(-1))
     if m > 8 and 0 < n <= 4:
-        cu_count = current_platform.get_cu_count()
+        cu_count = get_cu_count()
         out = ops.wvSplitK(weight, x_view, cu_count, bias)
         return out.reshape(*x.shape[:-1], weight.shape[0])
     elif m % 4 == 0 and n == 1 and k <= 8192 and bias is None:

vllm/platforms/interface.py

@@ -542,13 +542,6 @@ def get_global_graph_pool(self) -> Any:
             cls._global_graph_pool = self.graph_pool_handle()
         return cls._global_graph_pool
 
-    @classmethod
-    def get_cu_count(cls, device_id: int = 0) -> int:
-        """
-        Returns the total number of compute units (CU) on single GPU.
-        """
-        raise NotImplementedError
-
     @classmethod
     def get_static_graph_wrapper_cls(cls) -> str:
         """

vllm/platforms/rocm.py

@@ -431,10 +431,6 @@ def use_custom_allreduce(cls) -> bool:
     def opaque_attention_op(cls) -> bool:
         return True
 
-    @classmethod
-    def get_cu_count(cls, device_id: int = 0) -> int:
-        return torch.cuda.get_device_properties(device_id).multi_processor_count
-
     @classmethod
     def is_navi(cls) -> bool:
         return "gfx1" in torch.cuda.get_device_properties(0).gcnArchName

vllm/utils/platform_utils.py

@@ -24,6 +24,11 @@ def xpu_is_initialized() -> bool:
     return torch.xpu.is_initialized()
 
 
+def get_cu_count(cls, device_id: int = 0) -> int:
+    """Returns the total number of compute units (CU) on single GPU."""
+    return torch.cuda.get_device_properties(device_id).multi_processor_count
+
+
 def cuda_get_device_properties(
     device, names: Sequence[str], init_cuda=False
 ) -> tuple[Any, ...]: