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
252 lines
9.8 KiB
Plaintext
252 lines
9.8 KiB
Plaintext
/* Copyright 2025 The vLLM Authors and 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 <ATen/cuda/CUDAContext.h>
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#include <c10/cuda/CUDAGuard.h>
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#include <torch/all.h>
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#include "cuda_ops_api.h"
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// ref to:
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// https://github.com/vllm-project/vllm/blob/main/csrc/pos_encoding_kernels.cu
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namespace {
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template <typename scalar_t, bool IS_NEOX>
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inline __device__ void apply_token_rotary_embedding(
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scalar_t* __restrict__ arr,
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const scalar_t* __restrict__ cos_ptr,
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const scalar_t* __restrict__ sin_ptr,
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int rot_offset,
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int embed_dim) {
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int x_index, y_index;
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scalar_t cos, sin;
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if (IS_NEOX) {
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// GPT-NeoX style rotary embedding.
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x_index = rot_offset;
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y_index = embed_dim + rot_offset;
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cos = *(cos_ptr + x_index);
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sin = *(sin_ptr + x_index);
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} else {
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// GPT-J style rotary embedding.
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x_index = 2 * rot_offset;
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y_index = 2 * rot_offset + 1;
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cos = *(cos_ptr + x_index / 2);
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sin = *(sin_ptr + x_index / 2);
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}
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const scalar_t x = arr[x_index];
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const scalar_t y = arr[y_index];
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arr[x_index] = x * cos - y * sin;
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arr[y_index] = y * cos + x * sin;
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}
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template <typename scalar_t, bool IS_NEOX>
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inline __device__ void apply_rotary_embedding(
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scalar_t* __restrict__ query, // [batch_size, seq_len, num_heads,
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// head_size] or [num_tokens, num_heads,
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// head_size]
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scalar_t* __restrict__ key, // nullptr or
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// [batch_size, seq_len, num_kv_heads,
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// head_size] or [num_tokens, num_kv_heads,
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// head_size]
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const scalar_t* cache_ptr,
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const int head_size,
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const int num_heads,
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const int num_kv_heads,
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const int rot_dim,
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const int token_idx,
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const int64_t query_stride,
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const int64_t key_stride,
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const int64_t head_stride) {
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const int embed_dim = rot_dim / 2;
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const scalar_t* cos_ptr = cache_ptr;
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const scalar_t* sin_ptr = cache_ptr + embed_dim;
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const int nq = num_heads * embed_dim;
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for (int i = threadIdx.x; i < nq; i += blockDim.x) {
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const int head_idx = i / embed_dim;
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const int64_t token_head =
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token_idx * query_stride + head_idx * head_stride;
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const int rot_offset = i % embed_dim;
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apply_token_rotary_embedding<scalar_t, IS_NEOX>(
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query + token_head, cos_ptr, sin_ptr, rot_offset, embed_dim);
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}
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if (key != nullptr) {
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const int nk = num_kv_heads * embed_dim;
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for (int i = threadIdx.x; i < nk; i += blockDim.x) {
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const int head_idx = i / embed_dim;
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const int64_t token_head =
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token_idx * key_stride + head_idx * head_stride;
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const int rot_offset = i % embed_dim;
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apply_token_rotary_embedding<scalar_t, IS_NEOX>(
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key + token_head, cos_ptr, sin_ptr, rot_offset, embed_dim);
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}
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}
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}
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template <typename scalar_t, bool IS_NEOX>
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__global__ void rotary_embedding_kernel(
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const int64_t* __restrict__ positions, // [batch_size, seq_len] or
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// [num_tokens]
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scalar_t* __restrict__ query, // [batch_size, seq_len, num_heads,
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// head_size] or [num_tokens, num_heads,
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// head_size]
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scalar_t* __restrict__ key, // nullptr or
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// [batch_size, seq_len, num_kv_heads,
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// head_size] or [num_tokens, num_kv_heads,
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// head_size]
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const scalar_t* __restrict__ cos_sin_cache, // [max_position, 2, rot_dim //
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// 2]
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const int rot_dim,
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const int64_t query_stride,
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const int64_t key_stride,
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const int64_t head_stride,
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const int num_heads,
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const int num_kv_heads,
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const int head_size) {
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// Each thread block is responsible for one token.
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const int token_idx = blockIdx.x;
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int64_t pos = positions[token_idx];
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const scalar_t* cache_ptr = cos_sin_cache + pos * rot_dim;
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apply_rotary_embedding<scalar_t, IS_NEOX>(query,
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key,
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cache_ptr,
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head_size,
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num_heads,
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num_kv_heads,
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rot_dim,
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token_idx,
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query_stride,
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key_stride,
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head_stride);
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}
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} // namespace
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namespace xllm::kernel::cuda {
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// flashinfer rope ops
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// void apply_rope_pos_ids_cos_sin_cache(torch::Tensor q,
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// torch::Tensor k,
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// torch::Tensor cos_sin_cache,
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// torch::Tensor pos_ids,
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// bool interleave) {
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// const int64_t head_dim = cos_sin_cache.size(-1) / 2;
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// q = q.view({q.size(0), -1, head_dim});
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// k = k.view({k.size(0), -1, head_dim});
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// FunctionFactory::get_instance().rope_func("rope").call(
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// q, k, q, k, cos_sin_cache, pos_ids, interleave);
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// }
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void rotary_embedding(
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torch::Tensor& positions, // [batch_size, seq_len] or [num_tokens]
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torch::Tensor& query, // [batch_size, seq_len, num_heads * head_size] or
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// [num_tokens, num_heads * head_size] or
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// [batch_size, seq_len, num_heads, head_size] or
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// [num_tokens, num_heads, head_size]
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std::optional<torch::Tensor> key,
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// null or
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// [batch_size, seq_len, num_kv_heads * head_size] or
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// [num_tokens, num_kv_heads * head_size] or
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// [batch_size, seq_len, num_heads, head_size] or
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// [num_tokens, num_heads, head_size]
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// int64_t head_size,
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torch::Tensor& cos_sin_cache, // [max_position, rot_dim]
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bool is_neox) {
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// num_tokens = batch_size * seq_len
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int64_t head_size = cos_sin_cache.size(-1);
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int64_t num_tokens = positions.numel();
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int positions_ndim = positions.dim();
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// Make sure num_tokens dim is consistent across positions, query, and key
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CHECK(positions_ndim == 1 || positions_ndim == 2)
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<< "positions must have shape [num_tokens] or [batch_size, seq_len]";
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if (positions_ndim == 1) {
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CHECK(query.size(0) == positions.size(0) &&
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(!key.has_value() || key->size(0) == positions.size(0)))
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<< "query, key and positions must have the same number of tokens";
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}
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if (positions_ndim == 2) {
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CHECK(query.size(0) == positions.size(0) &&
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(!key.has_value() || key->size(0) == positions.size(0)) &&
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query.size(1) == positions.size(1) &&
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(!key.has_value() || key->size(1) == positions.size(1)))
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<< "query, key and positions must have the same batch_size and seq_len";
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}
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// Make sure head_size is valid for query and key
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// hidden_size = num_heads * head_size
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int query_hidden_size = query.numel() / num_tokens;
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int key_hidden_size = key.has_value() ? key->numel() / num_tokens : 0;
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CHECK(query_hidden_size % head_size == 0);
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CHECK(key_hidden_size % head_size == 0);
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// Make sure query and key have consistent number of heads
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int num_heads = query_hidden_size / head_size;
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int num_kv_heads = key.has_value() ? key_hidden_size / head_size : num_heads;
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CHECK(num_heads % num_kv_heads == 0);
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int rot_dim = cos_sin_cache.size(1);
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int seq_dim_idx = positions_ndim - 1;
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int64_t query_stride = query.stride(seq_dim_idx);
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int64_t key_stride = key.has_value() ? key->stride(seq_dim_idx) : 0;
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// Determine head stride: for [*, heads, head_size] use stride of last dim;
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// for flat [*, heads*head_size], heads blocks are contiguous of size
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// head_size
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int query_ndim = query.dim();
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int64_t head_stride =
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(query_ndim == positions_ndim + 2) ? query.stride(-2) : head_size;
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dim3 grid(num_tokens);
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dim3 block(std::min<int64_t>(num_heads * rot_dim / 2, 512));
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const at::cuda::OptionalCUDAGuard device_guard(device_of(query));
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const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
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DISPATCH_FLOATING_TYPES(
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query.scalar_type(), "apply_rope_pos_ids_cos_sin_cache", [&] {
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if (is_neox) {
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rotary_embedding_kernel<scalar_t, true><<<grid, block, 0, stream>>>(
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positions.data_ptr<int64_t>(),
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query.data_ptr<scalar_t>(),
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key.has_value() ? key->data_ptr<scalar_t>() : nullptr,
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cos_sin_cache.data_ptr<scalar_t>(),
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rot_dim,
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query_stride,
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key_stride,
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head_stride,
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num_heads,
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num_kv_heads,
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head_size);
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} else {
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rotary_embedding_kernel<scalar_t, false><<<grid, block, 0, stream>>>(
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positions.data_ptr<int64_t>(),
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query.data_ptr<scalar_t>(),
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key.has_value() ? key->data_ptr<scalar_t>() : nullptr,
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cos_sin_cache.data_ptr<scalar_t>(),
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rot_dim,
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query_stride,
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key_stride,
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head_stride,
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num_heads,
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num_kv_heads,
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head_size);
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}
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});
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}
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} // namespace xllm::kernel::cuda
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