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[Bugfix][Hardware][AMD] Fix FP8 dtype in silu_mul quantization #31179

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Merged
yewentao256 merged 1 commit into from
Dec 24, 2025
Merged

[Bugfix][Hardware][AMD] Fix FP8 dtype in silu_mul quantization #31179

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12 changes: 8 additions & 4 deletions vllm/model_executor/layers/quantization/utils/fp8_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -625,8 +625,9 @@ def silu_mul_per_token_group_quant_fp8_colmajor(
M, N = input.size()
N_2 = N // 2

fp8_dtype = current_platform.fp8_dtype()
if output is None:
output = torch.empty((M, N_2), dtype=torch.float8_e4m3fn, device=input.device)
output = torch.empty((M, N_2), dtype=fp8_dtype, device=input.device)

output_scales = torch.empty(
((N_2 // GROUP_SIZE), M), dtype=torch.float32, device=input.device
Expand All @@ -637,9 +638,12 @@ def silu_mul_per_token_group_quant_fp8_colmajor(
assert M % BLOCK_M == 0
assert N_2 % BLOCK_N == 0

finfo = torch.finfo(torch.float8_e4m3fn)
fp8_min = finfo.min
fp8_max = finfo.max
# 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_e4m3fnuz dtype.
finfo = torch.finfo(fp8_dtype)
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

# Force even division so we can avoid edgecases within the kernel.
assert M % BLOCK_M == 0
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