002f9879b2
Replaces cherry-picked upstream_ref with complete source trees. xllm/ — Iluvatar official C++ inference engine (15MB, 1470 files) Complete: kernels → layers → models → runtime → scheduler → api Excluded: .git, binary images, third_party submodule checkouts ds_vllm/ — Iluvatar official vllm fork (8MB, 703 files) Included: csrc/ (ALL CUDA kernels), fused_moe/, qwen3_5 model, _custom_ops Excluded: tests, benchmarks, docs, examples (not needed for reference) Critical call chains now fully traceable: MoE: moe_topk_softmax_kernels.cuh → ixformer.h → fused_moe.cpp → layer GDN: qwen3_gated_delta_net_base.cpp → qwen3_5_gated_delta_net.cpp Attention: ixformer.h → xllm_paged_attention → attention.cpp
66 lines
1.7 KiB
Plaintext
66 lines
1.7 KiB
Plaintext
/*
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* Adapted from
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* https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention_utils.h
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* Copyright (c) 2023, The vLLM team.
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* Copyright (c) 2020-2023, NVIDIA CORPORATION. All rights reserved.
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*
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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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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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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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#include <stdint.h>
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namespace vllm {
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// A vector type to store Q, K, V elements.
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template <typename T, int VEC_SIZE>
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struct Vec {};
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// A vector type to store FP32 accumulators.
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template <typename T>
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struct FloatVec {};
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// Template vector operations.
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template <typename Acc, typename A, typename B>
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inline __device__ Acc mul(A a, B b);
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template <typename T>
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inline __device__ float sum(T v);
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template <typename T>
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inline __device__ float dot(T a, T b) {
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return sum(mul<T, T, T>(a, b));
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}
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template <typename A, typename T>
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inline __device__ float dot(T a, T b) {
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return sum(mul<A, T, T>(a, b));
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}
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template <typename T>
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inline __device__ void zero(T& dst) {
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constexpr int WORDS = sizeof(T) / 4;
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union {
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T raw;
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uint32_t words[WORDS];
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} tmp;
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#pragma unroll
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for (int ii = 0; ii < WORDS; ++ii) {
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tmp.words[ii] = 0u;
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}
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dst = tmp.raw;
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}
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} // namespace vllm
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