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
88 lines
3.8 KiB
C++
88 lines
3.8 KiB
C++
#pragma once
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#include <torch/csrc/stable/tensor.h>
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#include <optional>
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#include <tuple>
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void topk_softmax(torch::stable::Tensor& topk_weights,
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torch::stable::Tensor& topk_indices,
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torch::stable::Tensor& token_expert_indices,
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torch::stable::Tensor& gating_output, bool renormalize,
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std::optional<torch::stable::Tensor> bias);
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void topk_sigmoid(torch::stable::Tensor& topk_weights,
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torch::stable::Tensor& topk_indices,
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torch::stable::Tensor& token_expert_indices,
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torch::stable::Tensor& gating_output, bool renormalize,
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std::optional<torch::stable::Tensor> bias);
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void topk_softplus_sqrt(
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torch::stable::Tensor& topk_weights, torch::stable::Tensor& topk_indices,
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torch::stable::Tensor& token_expert_indices,
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torch::stable::Tensor& gating_output, bool renormalize,
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double routed_scaling_factor,
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const std::optional<torch::stable::Tensor>& correction_bias,
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const std::optional<torch::stable::Tensor>& input_ids,
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const std::optional<torch::stable::Tensor>& tid2eid);
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void moe_sum(torch::stable::Tensor& input, torch::stable::Tensor& output);
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void moe_align_block_size(
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torch::stable::Tensor topk_ids, int64_t num_experts, int64_t block_size,
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torch::stable::Tensor sorted_token_ids, torch::stable::Tensor experts_ids,
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torch::stable::Tensor num_tokens_post_pad,
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std::optional<torch::stable::Tensor> maybe_expert_map);
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void batched_moe_align_block_size(
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int64_t max_tokens_per_batch, int64_t block_size,
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const torch::stable::Tensor& expert_num_tokens,
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torch::stable::Tensor sorted_ids, torch::stable::Tensor expert_ids,
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torch::stable::Tensor num_tokens_post_pad);
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void moe_lora_align_block_size(
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torch::stable::Tensor topk_ids, torch::stable::Tensor token_lora_mapping,
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int64_t num_experts, int64_t block_size, int64_t max_loras,
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int64_t max_num_tokens_padded, int64_t max_num_m_blocks,
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torch::stable::Tensor sorted_token_ids, torch::stable::Tensor expert_ids,
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torch::stable::Tensor num_tokens_post_pad,
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torch::stable::Tensor adapter_enabled, torch::stable::Tensor lora_ids,
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std::optional<torch::stable::Tensor> maybe_expert_map);
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#ifndef USE_ROCM
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torch::stable::Tensor moe_wna16_gemm(
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torch::stable::Tensor input, torch::stable::Tensor output,
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torch::stable::Tensor b_qweight, torch::stable::Tensor b_scales,
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std::optional<torch::stable::Tensor> b_qzeros,
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std::optional<torch::stable::Tensor> topk_weights,
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torch::stable::Tensor sorted_token_ids, torch::stable::Tensor expert_ids,
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torch::stable::Tensor num_tokens_post_pad, int64_t top_k,
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int64_t BLOCK_SIZE_M, int64_t BLOCK_SIZE_N, int64_t BLOCK_SIZE_K,
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int64_t bit);
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std::tuple<torch::stable::Tensor, torch::stable::Tensor> grouped_topk(
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const torch::stable::Tensor& scores, int64_t n_group, int64_t topk_group,
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int64_t topk, bool renormalize, double routed_scaling_factor,
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const torch::stable::Tensor& bias, int64_t scoring_func);
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#endif
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bool moe_permute_unpermute_supported();
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int64_t moe_permute_sort_workspace_size(int64_t num_expanded_rows,
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int64_t num_expert);
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void shuffle_rows(const torch::stable::Tensor& input_tensor,
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const torch::stable::Tensor& dst2src_map,
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torch::stable::Tensor& output_tensor);
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#ifndef USE_ROCM
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// DeepSeek V3 optimized router GEMM kernel for SM90+
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// Computes output = mat_a @ mat_b.T where:
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// mat_a: [num_tokens, hidden_dim] in bf16
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// mat_b: [num_experts, hidden_dim] in bf16
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// output: [num_tokens, num_experts] in bf16 or fp32
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// Supports num_tokens in [1, 16], num_experts in {256, 384}, hidden_dim = 7168
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void dsv3_router_gemm(torch::stable::Tensor& output,
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const torch::stable::Tensor& mat_a,
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const torch::stable::Tensor& mat_b);
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#endif
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