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[Bugfix][MXFP4] Call trtllm_fp4_block_scale_moe with kwargs (#33104)

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Signed-off-by: Pengchao Wang <wpc@fb.com>
This commit is contained in:
Pengchao Wang
2026-01-26 15:13:18 -08:00
committed by GitHub
parent 58a05b0ca1
commit 8caffd92df
1 changed files with 26 additions and 26 deletions
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52
vllm/model_executor/layers/quantization/mxfp4.py
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@@ -1053,32 +1053,32 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
x_scale = x_scale.view(torch.float8_e4m3fn).reshape(*x.shape[:-1], -1)
trtllm_gen_output = trtllm_fp4_block_scale_moe(
router_logits.to(torch.bfloat16),
None, # routing_bias
x_quant,
x_scale,
layer.w13_weight, # uint8 (e2m1 x 2)
layer.w13_weight_scale, # uint8 (e4m3 x 2)
layer.w13_bias, # fp32 per expert per channel
layer.gemm1_alpha, # fp32 per expert
layer.gemm1_beta, # fp32 per expert
layer.gemm1_clamp_limit, # fp32 per expert
layer.w2_weight, # uint8 (e2m1 x 2)
layer.w2_weight_scale, # ue8m0
layer.w2_bias, # fp32 per expert per channel
None, # output1_scale_scalar
None, # output1_scale_gate_scalar
None, # output2_scale_scalar
layer.global_num_experts,
layer.top_k,
None, # n_group
None, # topk_group
self.intermediate_size, # padded to multiple of 256
layer.ep_rank * layer.local_num_experts, # local_expert_offset
self.num_experts, # local num experts
None, # routed_scaling_factor
1 if layer.renormalize else 0, # routing_method_type, renormalize
True, # do finalize
routing_logits=router_logits.to(torch.bfloat16),
routing_bias=None,
hidden_states=x_quant,
hidden_states_scale=x_scale,
gemm1_weights=layer.w13_weight, # uint8 (e2m1 x 2)
gemm1_weights_scale=layer.w13_weight_scale, # uint8 (e4m3 x 2)
gemm1_bias=layer.w13_bias, # fp32 per expert per channel
gemm1_alpha=layer.gemm1_alpha, # fp32 per expert
gemm1_beta=layer.gemm1_beta, # fp32 per expert
gemm1_clamp_limit=layer.gemm1_clamp_limit, # fp32 per expert
gemm2_weights=layer.w2_weight, # uint8 (e2m1 x 2)
gemm2_weights_scale=layer.w2_weight_scale, # ue8m0
gemm2_bias=layer.w2_bias, # fp32 per expert per channel
output1_scale_scalar=None,
output1_scale_gate_scalar=None,
output2_scale_scalar=None,
num_experts=layer.global_num_experts,
top_k=layer.top_k,
n_group=None,
topk_group=None,
intermediate_size=self.intermediate_size, # padded to multiple of 256
local_expert_offset=layer.ep_rank * layer.local_num_experts,
local_num_experts=self.num_experts,
routed_scaling_factor=None,
routing_method_type=1 if layer.renormalize else 0,
do_finalize=True,
tune_max_num_tokens=max(self.max_capture_size, 1),
)[0]
return trtllm_gen_output