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
63 lines
3.0 KiB
C++
63 lines
3.0 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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#pragma once
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namespace xllm::kernel::ilu {
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#undef check_tensor_contiguous
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#define check_tensor_contiguous(x, type) \
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TORCH_CHECK(x.scalar_type() == type); \
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TORCH_CHECK(x.is_cuda()); \
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TORCH_CHECK(x.is_contiguous());
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#undef check_tensor_half_bf_float
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#define check_tensor_half_bf_float(x) \
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TORCH_CHECK(x.scalar_type() == at::ScalarType::Half || \
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x.scalar_type() == at::ScalarType::Float || \
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x.scalar_type() == at::ScalarType::BFloat16); \
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TORCH_CHECK(x.is_cuda());
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// from torchCheckMsgImpl
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inline const char* ixformer_check_msg_impl(const char* msg) { return msg; }
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// // If there is just 1 user-provided C-string argument, use it.
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#define IXFORMER_CHECK_MSG(cond, type, ...) \
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(ixformer_check_msg_impl( \
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"Expected " #cond \
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" to be true, but got false. " \
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"(Could this error message be improved? If so, " \
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"please report an enhancement request to ixformer.)", \
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##__VA_ARGS__))
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#define IXFORMER_CHECK(cond, ...) \
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{ \
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if (!(cond)) { \
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std::cerr << __FILE__ << " (" << __LINE__ << ")" \
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<< "-" << __FUNCTION__ << " : " \
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<< IXFORMER_CHECK_MSG(cond, "", ##__VA_ARGS__) << std::endl; \
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throw std::runtime_error("IXFORMER_CHECK ERROR"); \
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} \
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}
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#undef CUINFER_CHECK
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#define CUINFER_CHECK(func) \
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do { \
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cuinferStatus_t status = (func); \
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if (status != CUINFER_STATUS_SUCCESS) { \
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std::cerr << "Error in file " << __FILE__ << " on line " << __LINE__ \
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<< ": " << cuinferGetErrorString(status) << std::endl; \
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throw std::runtime_error("CUINFER_CHECK ERROR"); \
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} \
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} while (0)
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} // namespace xllm::kernel::ilu
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