[Misc] Fix flashinfer related tests (#33462)

Signed-off-by: esmeetu <jasonailu87@gmail.com>

tests/kernels/moe/test_moe.py

@@ -412,7 +412,7 @@ def test_naive_block_assignment_moe(
     monkeypatch,
     workspace_init,
 ):
-    current_platform.seed_everything(7)
+    set_random_seed(7)
 
     monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", str(chunk_size))
 

tests/kernels/quantization/test_flashinfer_nvfp4_scaled_mm.py

@@ -74,7 +74,7 @@ def get_ref_results(
 @pytest.mark.parametrize("shape", SHAPES)
 @pytest.mark.parametrize("seed", SEEDS)
 @pytest.mark.parametrize("device", CUDA_DEVICES)
-@pytest.mark.parametrize("backend", ["cutlass", "trtllm"])
+@pytest.mark.parametrize("backend", ["cutlass", "cudnn", "trtllm"])
 @pytest.mark.parametrize("autotune", [False, True])
 @torch.inference_mode()
 def test_flashinfer_nvfp4_gemm(

tests/kernels/quantization/test_fp8_quant.py

@@ -174,7 +174,7 @@ def test_static_fp8_quant_group_2d(
             f"group_shape ({group_shape[0]}, {group_shape[1]})"
         )
 
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
 
     x = torch.rand(num_tokens, hidden_size, dtype=dtype, device="cuda")
     ref_out, scale = scaled_quantize(
@@ -202,7 +202,7 @@ def test_static_fp8_quant_1d_scale(
     group_shape: tuple[int, int],
 ) -> None:
     """Test static FP8 quantization with 1D scale (per-token or per-channel)."""
-    current_platform.seed_everything(seed)
+    set_random_seed(seed)
 
     x = torch.rand(num_tokens, hidden_size, dtype=dtype, device="cuda")
     ref_out, scale_2d = scaled_quantize(

vllm/model_executor/layers/quantization/utils/nvfp4_utils.py

@@ -154,9 +154,10 @@ def convert_to_nvfp4_linear_kernel_format(
         )
         layer.weight = torch.nn.Parameter(weight, requires_grad=False)
         layer.weight_scale = torch.nn.Parameter(weight_scale, requires_grad=False)
-    elif (
-        backend == NvFp4LinearBackend.VLLM_CUTLASS
-        or backend == NvFp4LinearBackend.FLASHINFER_CUTLASS
+    elif backend in (
+        NvFp4LinearBackend.VLLM_CUTLASS,
+        NvFp4LinearBackend.FLASHINFER_CUTLASS,
+        NvFp4LinearBackend.FLASHINFER_CUDNN,
     ):
         weight, weight_scale, weights_padding_cols = prepare_weights_for_nvfp4_cutlass(
             layer.weight.data, layer.weight_scale.data

vllm/utils/flashinfer.py

@@ -521,7 +521,7 @@ def flashinfer_scaled_fp4_mm(
     assert a.stride(-1) == 1 and b.stride(-1) == 1
     assert a.shape[1] == b.shape[1]
 
-    if backend == "cutlass":
+    if backend in ("cutlass", "cudnn"):
         block_scale_a = block_scale_a.view(torch.uint8)
         block_scale_b = block_scale_b.view(torch.uint8)