[platform] Move get_cu_count to utils (#27005)

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

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

@@ -13,6 +13,7 @@ from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
 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

@@ -11,6 +11,7 @@ from vllm import envs
 from vllm._aiter_ops import rocm_aiter_ops
 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: