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[ROCm][LoRA] Fix MoE accuracy regression by preserving float32 router weight scaling #31931

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Merged
tjtanaa merged 1 commit into from
Jan 8, 2026
Merged

[ROCm][LoRA] Fix MoE accuracy regression by preserving float32 router weight scaling #31931

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12 changes: 8 additions & 4 deletions vllm/model_executor/layers/fused_moe/fused_moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -519,6 +519,12 @@ def fused_moe_kernel(
a_ptrs += BLOCK_SIZE_K * stride_ak
b_ptrs += BLOCK_SIZE_K * stride_bk

# Router weight multiplication MUST happen in float32 before precision
# conversion for numerical stability (especially critical on ROCm).
if MUL_ROUTED_WEIGHT:
moe_weight = tl.load(topk_weights_ptr + offs_token, mask=token_mask, other=0)
accumulator = accumulator * moe_weight[:, None]

if use_int8_w8a16:
accumulator = (accumulator * b_scale).to(compute_type)
elif use_fp8_w8a8 or use_int8_w8a8:
Expand All @@ -529,12 +535,10 @@ def fused_moe_kernel(
else:
accumulator = accumulator.to(compute_type)

# Since bias is typically not quantized, it's added after dequantization.
# Bias is added AFTER dequantization since bias is typically stored in
# the output dtype and should not be scaled by quantization factors.
if HAS_BIAS:
accumulator = accumulator + bias[None, :]
if MUL_ROUTED_WEIGHT:
moe_weight = tl.load(topk_weights_ptr + offs_token, mask=token_mask, other=0)
accumulator = accumulator * moe_weight[:, None]

# -----------------------------------------------------------
# Write back the block of the output
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