From 2891603efdbd38ac7197d5f41b0e245fb6a53b82 Mon Sep 17 00:00:00 2001
From: Gregory Shtrasberg <156009573+gshtras@users.noreply.github.com>
Date: Mon, 15 Sep 2025 22:05:12 -0400
Subject: [PATCH] [ROCm][Bugfix] Fix the case where there's bias (#24895)

Signed-off-by: Gregory Shtrasberg <Gregory.Shtrasberg@amd.com>
---
 .../quantization/test_rocm_skinny_gemms.py    | 31 +++++++++++++++++++
 .../layers/quantization/utils/w8a8_utils.py   |  2 +-
 2 files changed, 32 insertions(+), 1 deletion(-)

diff --git a/tests/kernels/quantization/test_rocm_skinny_gemms.py b/tests/kernels/quantization/test_rocm_skinny_gemms.py
index 03d5d98739c5..a9b1c71ef071 100644
--- a/tests/kernels/quantization/test_rocm_skinny_gemms.py
+++ b/tests/kernels/quantization/test_rocm_skinny_gemms.py
@@ -5,6 +5,8 @@ import torch
 
 import vllm._custom_ops as ops
 from tests.kernels.quant_utils import ref_dynamic_per_tensor_fp8_quant
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    rocm_per_tensor_w8a8_scaled_mm_impl)
 from vllm.platforms import current_platform
 
 DTYPES = [torch.bfloat16, torch.float16]
@@ -116,3 +118,32 @@ def test_rocm_wvsplitk_fp8_kernel(n, k, m, dtype, seed):
                         current_platform.get_cu_count())
 
     assert torch.allclose(out, ref_out, rtol=0.01)
+
+
+@pytest.mark.parametrize("n,k,m", NKM_FACTORS_WVSPLITK_FP8)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.parametrize("use_bias", [True, False])
+@pytest.mark.skipif(
+    not (current_platform.is_rocm() and current_platform.supports_fp8()),
+    reason="only test for rocm fp8")
+def test_rocm_per_tensor_w8a8_scaled_mm_impl(n, k, m, dtype, seed, use_bias):
+    torch.manual_seed(seed)
+
+    A = torch.rand(n, k, device="cuda")
+    B = torch.rand(m, k, device="cuda")
+
+    A, scale_a = ref_dynamic_per_tensor_fp8_quant(A)
+    B, scale_b = ref_dynamic_per_tensor_fp8_quant(B)
+
+    bias = torch.rand(1, m, dtype=dtype, device="cuda") if use_bias else None
+
+    output = rocm_per_tensor_w8a8_scaled_mm_impl(A, B.t(), dtype, scale_a,
+                                                 scale_b, bias)
+    ref_out = torch._scaled_mm(A,
+                               B.t(),
+                               out_dtype=dtype,
+                               scale_a=scale_a,
+                               scale_b=scale_b,
+                               bias=bias)
+    assert torch.allclose(output, ref_out, rtol=0.01)
diff --git a/vllm/model_executor/layers/quantization/utils/w8a8_utils.py b/vllm/model_executor/layers/quantization/utils/w8a8_utils.py
index e89a5e643b0e..8cda1789e6c9 100644
--- a/vllm/model_executor/layers/quantization/utils/w8a8_utils.py
+++ b/vllm/model_executor/layers/quantization/utils/w8a8_utils.py
@@ -179,7 +179,7 @@ def rocm_per_tensor_w8a8_scaled_mm_impl(qinput: torch.Tensor,
                                         bias: torch.Tensor) -> torch.Tensor:
     from vllm.platforms.rocm import on_mi3xx
     if envs.VLLM_ROCM_USE_SKINNY_GEMM and on_mi3xx(
-    ) and qinput.shape[0] == 1 and qinput.shape[1] % 16 == 0:
+    ) and qinput.shape[0] == 1 and qinput.shape[1] % 16 == 0 and bias is None:
         output = ops.wvSplitKQ(weight.t(), qinput, out_dtype, scale_a, scale_b,
                                current_platform.get_cu_count())
     else: