ea82b00e54
Sources (Apache 2.0, cloned 2026-08-09): - Deep-Spark/xllm: Iluvatar's official C++ inference engine - Deep-Spark/vllm: Iluvatar's vllm fork Key files for our EX Engine development: MoE topk_softmax (fixes 2304 calls/token PyTorch fallback): - xllm/kernels/cuda/moe/moe_topk_softmax_kernels.cuh CUB-based fused softmax+topk, power-of-2 expert count optimized For 64 experts: topk_gating_softmax<T,VPT=2,64,WARPS=4,BYTES=4> - xllm/kernels/ilu/ixformer.h Official ixformer C++ API: topk_softmax(), paged_attention(), etc. - xllm/kernels/ilu/fused_moe.cpp How xllm calls ixformer::infer::topk_softmax() - ds_vllm/csrc/moe/topk_softmax_kernels.cu vllm-native topk_softmax (TensorRT-LLM derived, 874 lines) GatedDeltaNet (fixes NaN in 4 GDN layers): - xllm/layers/npu_torch/qwen3_gated_delta_net_base.cpp fp32 state accumulation, proper recurrent update Complete FusedMoE pipeline reference: - xllm/layers/ilu/fused_moe.cpp gate -> topk -> expand -> gemm1 -> act -> gemm2 -> combine
100 lines
3.9 KiB
C++
100 lines
3.9 KiB
C++
/* Copyright 2025 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 <glog/logging.h>
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#include "ilu_ops_api.h"
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namespace xllm::kernel::ilu {
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std::tuple<torch::Tensor, torch::Tensor> moe_active_topk(
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const torch::Tensor& input,
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int64_t topk,
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int64_t num_expert_group,
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int64_t topk_group,
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bool normalize,
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const std::optional<torch::Tensor>& mask,
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const std::string& normed_by,
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const std::string& scoring_func,
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double route_scale,
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const std::optional<torch::Tensor>& e_score_correction_bias) {
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torch::Tensor input_ = input.to(torch::kFloat32);
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auto reduce_weight =
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torch::empty({input.size(0), topk},
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torch::dtype(torch::kFloat).device(input.device()));
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auto topk_indices =
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torch::empty({input.size(0), topk},
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torch::dtype(torch::kInt32).device(input.device()));
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auto token_expert_indices =
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torch::empty({input.size(0), topk},
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torch::dtype(torch::kInt32).device(input.device()));
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infer::topk_softmax(
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reduce_weight, topk_indices, token_expert_indices, input_, false);
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auto tt = reduce_weight.sum(-1);
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if (normalize) {
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reduce_weight = reduce_weight / reduce_weight.sum(-1).unsqueeze(-1);
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}
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return std::make_tuple(reduce_weight, topk_indices);
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}
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std::vector<torch::Tensor> moe_gen_idx(torch::Tensor& expert_id,
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int64_t expert_num) {
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auto src_dst = expert_id.new_empty({expert_id.numel()});
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auto dst_src = torch::empty_like(src_dst);
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auto expert_sizes_gpu = expert_id.new_empty({expert_num});
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auto expert_sizes_gpu_cumsum = expert_id.new_zeros({expert_id.numel() + 1});
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infer::moe_compute_token_index_api(expert_id,
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src_dst,
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dst_src,
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expert_sizes_gpu,
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/*expert_mask=*/std::nullopt,
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/*expert_sizes_cpu*/ std::nullopt,
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/*expert_sizes_gpu*/ std::nullopt,
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0,
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expert_num,
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expert_num);
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expert_sizes_gpu_cumsum = expert_sizes_gpu.cumsum(-1);
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return {src_dst, dst_src, expert_sizes_gpu, expert_sizes_gpu_cumsum};
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}
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torch::Tensor moe_expand_input(const torch::Tensor& input,
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const torch::Tensor& gather_index,
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const torch::Tensor& combine_idx,
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int64_t topk) {
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int64_t dst_tokens = input.size(0) * topk;
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auto output = input.new_empty({dst_tokens, input.size(1)});
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infer::moe_expand_input(
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output, input, combine_idx, gather_index, dst_tokens, topk);
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return output;
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}
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torch::Tensor moe_combine_result(torch::Tensor& input, torch::Tensor& weight) {
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input = input.view({-1, weight.size(1), input.size(1)});
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auto output = input.new_empty({input.size(0), input.size(2)});
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infer::moe_output_reduce_sum(output,
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input,
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weight,
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/*mask=*/std::nullopt,
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/*extra_residual*/ std::nullopt,
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/*scaling_factor=*/1.0);
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return output;
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}
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} // namespace xllm::kernel::ilu
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