[Bugfix][ROCm] Fixing the skinny gemm dispatch logic from #32831

tests/kernels/quantization/test_rocm_skinny_gemms.py

@@ -87,6 +87,13 @@
 SEEDS = [0]
 
 
+def pad_weights_fp8(weight):
+    num_pad = 256 // weight.element_size()
+    import torch.nn.functional as F
+
+    return F.pad(weight, (0, num_pad), "constant", 0)[..., :-num_pad]
+
+
 @pytest.mark.parametrize("n,k,m", NKM_FACTORS_WVSPLITKRC)
 @pytest.mark.parametrize("dtype", DTYPES)
 @pytest.mark.parametrize("seed", SEEDS)
@@ -191,18 +198,21 @@ def test_rocm_wvsplitk_bias2D_kernel(n, k, m, dtype, seed):
 @pytest.mark.parametrize("n,k,m", NKM_FACTORS_WVSPLITK_FP8)
 @pytest.mark.parametrize("dtype", DTYPES)
 @pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.parametrize("padded", [False, True])
 @pytest.mark.skipif(
     not (current_platform.is_rocm() and current_platform.supports_fp8()),
     reason="only test for rocm fp8",
 )
-def test_rocm_wvsplitk_fp8_kernel(n, k, m, dtype, seed):
+def test_rocm_wvsplitk_fp8_kernel(n, k, m, dtype, seed, padded):
     torch.manual_seed(seed)
 
     A = torch.rand(n, k, device="cuda") - 0.5
     B = torch.rand(m, k, device="cuda") - 0.5
 
     A, scale_a = ref_dynamic_per_tensor_fp8_quant(A)
     B, scale_b = ref_dynamic_per_tensor_fp8_quant(B)
+    if padded:
+        B = pad_weights_fp8(B)
 
     ref_out = torch._scaled_mm(
         A, B.t(), out_dtype=dtype, scale_a=scale_a, scale_b=scale_b
@@ -222,11 +232,12 @@ def test_rocm_wvsplitk_fp8_kernel(n, k, m, dtype, seed):
 @pytest.mark.parametrize("n,k,m", NKM_FACTORS_WVSPLITK_FP8)
 @pytest.mark.parametrize("dtype", DTYPES)
 @pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.parametrize("padded", [False, True])
 @pytest.mark.skipif(
     not (current_platform.is_rocm() and current_platform.supports_fp8()),
     reason="only test for rocm fp8",
 )
-def test_rocm_wvsplitk_fp8_bias1D_kernel(n, k, m, dtype, seed):
+def test_rocm_wvsplitk_fp8_bias1D_kernel(n, k, m, dtype, seed, padded):
     torch.manual_seed(seed)
 
     xavier = math.sqrt(2 / k)  # normalize to avoid large output-bias deltas
@@ -236,6 +247,8 @@ def test_rocm_wvsplitk_fp8_bias1D_kernel(n, k, m, dtype, seed):
 
     A, scale_a = ref_dynamic_per_tensor_fp8_quant(A)
     B, scale_b = ref_dynamic_per_tensor_fp8_quant(B)
+    if padded:
+        B = pad_weights_fp8(B)
 
     ref_out = torch._scaled_mm(
         A, B.t(), out_dtype=dtype, scale_a=scale_a, scale_b=scale_b, bias=BIAS

vllm/_custom_ops.py

@@ -2027,35 +2027,20 @@ def selective_scan_fwd(
     )
 
 
-# NOTE: The wvSplitK kernel (and all of the kernels in skinny_gemms.cu)
-# are unable to properly handle non-contiguous
-# tensors.  It might be a good TODO(rasmith) to augment these kernels
-# to be able to handle non-contiguous kernels for better performance.
-def rocm_enforce_contiguous_skinny_gemm_inputs(
-    a: torch.Tensor, b: torch.Tensor
-) -> tuple[torch.Tensor, torch.Tensor]:
-    a = a.contiguous()  # no-op if already contiguous, else clone
-    b = b.contiguous()  # no-op if already contiguous, else clone
-    return a, b
-
-
 # ROCm skinny gemms
 def LLMM1(a: torch.Tensor, b: torch.Tensor, rows_per_block: int) -> torch.Tensor:
-    a, b = rocm_enforce_contiguous_skinny_gemm_inputs(a, b)
     return torch.ops._rocm_C.LLMM1(a, b, rows_per_block)
 
 
 def wvSplitK(
     a: torch.Tensor, b: torch.Tensor, cu_count: int, bias: torch.Tensor = None
 ) -> torch.Tensor:
-    a, b = rocm_enforce_contiguous_skinny_gemm_inputs(a, b)
     return torch.ops._rocm_C.wvSplitK(a, b, bias, cu_count)
 
 
 def wvSplitKrc(
     a: torch.Tensor, b: torch.Tensor, cu_count: int, bias: torch.Tensor = None
 ) -> torch.Tensor:
-    a, b = rocm_enforce_contiguous_skinny_gemm_inputs(a, b)
     return torch.ops._rocm_C.wvSplitKrc(a, b, bias, cu_count)
 
 
@@ -2068,7 +2053,6 @@ def wvSplitKQ(
     cu_count: int,
     bias: torch.Tensor = None,
 ) -> torch.Tensor:
-    a, b = rocm_enforce_contiguous_skinny_gemm_inputs(a, b)
     out = torch.empty((b.shape[0], a.shape[0]), dtype=out_dtype, device=b.device)
     torch.ops._rocm_C.wvSplitKQ(a, b, bias, out, scale_a, scale_b, cu_count)
     return out

vllm/model_executor/layers/quantization/kernels/scaled_mm/rocm.py

@@ -28,6 +28,7 @@ def rocm_per_tensor_float_w8a8_scaled_mm_impl(
         A.shape[0] == 1
         and B.shape[1] % 16 == 0
         and ((bias is None) or (bias.dtype == out_dtype))
+        and A.is_contiguous()
     ):
         output = ops.wvSplitKQ(
             B.t(),

vllm/model_executor/layers/utils.py

@@ -165,6 +165,7 @@ def rocm_unquantized_gemm_impl(
             and n <= 128
             and k > 512
             and math.ceil(k / 512) * math.ceil(m / 16) < get_cu_count()
+            and x.is_contiguous()
         )
         # k == 2880 and (m == 640 or m == 128))
     )
@@ -179,6 +180,7 @@ def rocm_unquantized_gemm_impl(
         and on_gfx9()
         and x.dtype in [torch.float16, torch.bfloat16]
         and k % 8 == 0
+        and x.is_contiguous()
     )
 
     if use_skinny is not True: