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A high-throughput and memory-efficient inference and serving engine for LLMs

Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git

Synced Nov 11, 15:00 UTC

[CI/Build][AMD] Fix ref_dynamic_per_token_quant reference implementation on ROCm. (#30291)

Signed-off-by: Randall Smith <ransmith@amd.com> Co-authored-by: Randall Smith <ransmith@amd.com>

rasmith committed Dec 12, 2025 at 03:30 UTC 302b2c1eb968711abe3e765f7a936dea66535907
1 file changed +4 -9
tests/kernels/quant_utils.py
+4 -9
@@ -30,16 +30,11 @@ def ref_dynamic_per_token_quant(
30 if quant_dtype == torch.int8
31 else torch.finfo(quant_dtype)
32 )
33 - qtype_traits_max = (
34 - ROCM_FP8FNUZ_MAX
35 - if current_platform.is_rocm() and current_platform.is_fp8_fnuz()
36 - else qtype_traits.max
37 - )
38 - qtype_traits_min = (
39 - -ROCM_FP8FNUZ_MAX
40 - if current_platform.is_rocm() and current_platform.is_fp8_fnuz()
41 - else qtype_traits.min
33 + use_fp8fnuz = (
34 + current_platform.is_fp8_fnuz() and quant_dtype == current_platform.fp8_dtype()
35 )
36 + qtype_traits_max = ROCM_FP8FNUZ_MAX if use_fp8fnuz else qtype_traits.max
37 + qtype_traits_min = -ROCM_FP8FNUZ_MAX if use_fp8fnuz else qtype_traits.min
38 qtype_max = as_float32_tensor(qtype_traits_max)
39 s_1 = as_float32_tensor(1.0)
40 s_512 = as_float32_tensor(512.0)