[CI/Build][Kernel][BugFix][AMD] Fix per_token_group_quant_fp8 to use correct...

[CI/Build][Kernel][BugFix][AMD] Fix per_token_group_quant_fp8 to use correct fp8 min/max values and update atol/rtol in test_quantfp8_group_functionality  (#30292)
Signed-off-by: Randall Smith <ransmith@amd.com>
Co-authored-by: Randall Smith <ransmith@amd.com>

tests/kernels/quantization/test_fp8_quant_group.py

@@ -62,7 +62,7 @@ def test_quantfp8_group_functionality(
     assert scales_col.stride(1) == batch_size
 
     # Test column-major scales consistency
-    assert torch.allclose(scales_col, scales_native, rtol=1e-9, atol=1e-8)
+    torch.testing.assert_close(scales_col, scales_native, rtol=1e-9, atol=1e-8)
 
     # 3. Test CUDA implementation (only for divisible dimensions)
     if is_divisible:
@@ -71,7 +71,7 @@ def test_quantfp8_group_functionality(
         assert scales_cuda.shape == (batch_size, expected_num_groups)
 
         # Verify CUDA/native consistency
-        assert torch.allclose(scales_cuda, scales_native, rtol=1e-9, atol=1e-8)
+        torch.testing.assert_close(scales_cuda, scales_native, rtol=2e-7, atol=2e-8)
 
         # Quantized values should mostly match
         diff_count = (x_quant_cuda != x_quant_native).sum().item()

vllm/model_executor/layers/quantization/utils/fp8_utils.py

@@ -762,9 +762,12 @@ def per_token_group_quant_fp8(
     )
     assert x.stride(-1) == 1, "`x` groups must be contiguous"
 
+    # Using the default value (240.0) from pytorch will cause accuracy
+    # issue on dynamic quantization models. Here use 224.0 for fnuz on ROCm
+    # platforms that use the torch.float8_e4mefnuz dtype.
     finfo = torch.finfo(dtype)
-    fp8_min = finfo.min
-    fp8_max = finfo.max
+    fp8_min = -224.0 if current_platform.is_fp8_fnuz() else finfo.min
+    fp8_max = 224.0 if current_platform.is_fp8_fnuz() else finfo.max
 
     assert out_q is None or out_q.shape == x.shape
     x_q = out_q