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update aiter fused_moe interface #720

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6a9f54c
update aiter fused_moe interface
zhiding512 Sep 28, 2025
a5b8510
update
zhiding512 Sep 29, 2025
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62 changes: 20 additions & 42 deletions vllm/model_executor/layers/quantization/mxfp4.py
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Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,7 @@ def should_use_flashinfer_mxfp4():

if current_platform.is_rocm() and envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_FUSED_MOE_A16W4:
import aiter
from aiter.fused_moe import fused_topk, moe_sorting
from aiter.fused_moe import fused_moe, fused_topk, moe_sorting
from aiter.ops.shuffle import shuffle_mxfp4_weight, shuffle_mxfp4_scale

class Mxfp4Config(QuantizationConfig):
Expand Down Expand Up @@ -690,51 +690,29 @@ def apply(
token_num = x.shape[0]
BLOCKM = 16 if token_num < 2048 else 32
topk_weights, topk_ids = fused_topk(x, router_logits, top_k, True)
sorted_ids, sorted_weights, sorted_expert_ids, num_valid_ids, moe_out = moe_sorting(
topk_ids,
topk_weights,
self.num_experts,
x.shape[1],
torch.bfloat16,
BLOCKM
)
_, n1, k1 = self.w13_weight_aiter_tensor.shape
_, k2, n2 = self.w2_weight_aiter_tensor.shape
D = n2 if k2 == k1 else n2*2
cktile_moe_out1 = torch.empty((token_num, top_k, D), dtype=torch.bfloat16, device=x.device)
aiter.moe_cktile2stages_gemm1(
return fused_moe(
x,
self.w13_weight_aiter_tensor,
cktile_moe_out1,
sorted_ids,
sorted_expert_ids,
num_valid_ids,
top_k,
self.intermediate_pad // 64 * 64 * 2,
self.hidden_pad // 128 * 128, # k_pad_zeros
None, # sorted_weights
None,
self.w13_scale_aiter_tensor,
self.w13_bias_aiter_tensor,
BLOCKM, # block_size
)
aiter.moe_cktile2stages_gemm2(
cktile_moe_out1,
self.w2_weight_aiter_tensor,
moe_out,
sorted_ids,
sorted_expert_ids,
num_valid_ids,
top_k,
self.hidden_pad // 64 * 64, # n_pad_zeros
self.intermediate_pad // 128 * 128,
sorted_weights, # sorted_weights
None,
self.w2_scale_aiter_tensor,
layer.w2_bias,
BLOCKM, # block_size
topk_weights,
topk_ids,
expert_mask=None,
activation=aiter.ActivationType.Swiglu,
quant_type=aiter.QuantType.per_1x32,
doweight_stage1=False,
w1_scale=self.w13_scale_aiter_tensor,
w2_scale=self.w2_scale_aiter_tensor,
a1_scale=None,
a2_scale=None,
block_size_M=BLOCKM,
num_local_tokens=None,
moe_sorting_dispatch_policy=0,
dtype=None,
hidden_pad=self.hidden_pad,
intermediate_pad=self.intermediate_pad,
bias1=self.w13_bias_aiter_tensor,
bias2=layer.w2_bias,
)
return moe_out

from vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe import ( # noqa: E501
triton_kernel_moe_forward)
Expand Down
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