f4e2264a83
Copied from upstream_ref (NOT rewritten — exact upstream code):
ixformer C++ API (the authoritative header):
include/ixformer.h — ixformer::infer namespace: topk_softmax,
moe_compute_token_index_api, moe_w16a16_group_gemm, moe_expand_input,
moe_output_reduce_sum, silu_and_mul, rms_norm, xllm_paged_attention, etc.
include/ilu_ops_api.h — xllm::kernel::ilu namespace: moe_active_topk,
moe_gen_idx, moe_expand_input, group_gemm, moe_combine_result,
batch_prefill, batch_decode, rms_norm, matmul, act_and_mul, etc.
ILU kernel wrappers (call ixformer::infer directly):
csrc/ilu_kernel_fused_moe.cpp — topk routing + gen_idx + expand + combine
csrc/ilu_kernel_group_gemm.cpp — batched expert GEMM
csrc/ilu_kernel_{activation,norm,rope,matmul,attention}.cpp
ILU layer implementations (full pipeline):
csrc/ilu_layer_fused_moe.{cpp,h} — 797 lines, the complete MoE pipeline
that competitor 168 ran as corex_moe.py
csrc/ilu_layer_attention.{cpp,h} — prefill/decode attention dispatch
CUDA MoE kernels (from xllm + ds_vllm):
csrc/moe/moe_topk_softmax_kernels.cuh — CUB BlockReduce + warp topk
csrc/moe/moe_topk_sigmoid_kernels.cuh — sigmoid scoring variant
csrc/moe/moe_topk.cuh + moe_fused_topk.cu — entry points
csrc/moe/moeTopKFuncs.cuh — TRT-LLM derived vllm-compatible topk
csrc/moe/moe_ops.h + moe_align_sum_kernels.cu — alignment kernels
Common layer headers:
csrc/common_fused_moe{,_base}.h + common_moe_fused_topk.{cpp,h}
57 lines
2.0 KiB
Plaintext
57 lines
2.0 KiB
Plaintext
/* Copyright 2026 The xLLM Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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https://github.com/jd-opensource/xllm/blob/main/LICENSE
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "kernels/cuda/cuda_ops_api.h"
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#include "moe_topk_sigmoid_kernels.cuh"
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#include "moe_topk_softmax_kernels.cuh"
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namespace xllm::kernel::cuda {
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std::tuple<torch::Tensor, torch::Tensor> moe_fused_topk(
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torch::Tensor& gating_output,
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int64_t topk,
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bool renormalize,
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const std::optional<torch::Tensor>& correction_bias,
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const std::string& scoring_func) {
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int64_t num_tokens = gating_output.size(0);
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torch::Tensor topk_weights = torch::empty(
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{num_tokens, topk},
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torch::dtype(torch::kFloat32).device(gating_output.device()));
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torch::Tensor topk_ids =
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torch::empty({num_tokens, topk},
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torch::dtype(torch::kInt32).device(gating_output.device()));
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if (scoring_func == "softmax") {
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std::optional<torch::Tensor> none_correction_bias = std::nullopt;
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topk_softmax(topk_weights,
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topk_ids,
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gating_output,
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renormalize,
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/*moe_softcapping=*/0.0,
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none_correction_bias);
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} else if (scoring_func == "sigmoid") {
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topk_sigmoid(
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topk_weights, topk_ids, gating_output, renormalize, correction_bias);
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} else {
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LOG(FATAL) << "Unsupported scoring function for moe topk: " << scoring_func
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<< "only softmax and sigmoid are supported";
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}
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return std::make_tuple(topk_weights, topk_ids);
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}
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} // namespace xllm::kernel::cuda
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