6415249693
MoE call chain from ds_vllm (vllm-project/vllm latest): ex_engine/moe/ — 20 files, 8736 lines - modular_kernel.py (1630 lines) — base classes for modular MoE - experts/fused_batched_moe.py (972 lines) — NaiveBatchedExperts - prepare_finalize/batched.py (171 lines) — token grouping by expert - topk_weight_and_reduce.py (176 lines) — scatter-add finalize - fused_moe.py (1740 lines) — main fused_moe dispatch - config.py (1407 lines) — FusedMoEQuantConfig - activation.py, utils.py, layer.py, etc. xllm layer code (jd-opensource/xllm): ex_engine/xllm_layers/ — 39 files, 5859 lines - ilu/fused_moe.cpp (797 lines) — production ixformer 7-step MoE pipeline - ilu/attention.cpp (189 lines) — paged_attention + flash_attn bridge - npu_torch/qwen3_gated_delta_net_base.cpp (576 lines) — GDN reference - common/rms_norm.cpp, rotary_embedding.cpp, activation.cpp, dense_mlp.cpp xllm ILU kernels — synced 10 files to upstream (diffs from prior edits) These are reference implementations, NOT hand-written. Source repos: vllm-project/vllm, jd-opensource/xllm
154 lines
6.2 KiB
C++
154 lines
6.2 KiB
C++
/* Copyright 2025 The xLLM Authors. All Rights Reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
https://github.com/jd-opensource/xllm/blob/main/LICENSE
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License.
|
|
==============================================================================*/
|
|
|
|
#pragma once
|
|
|
|
#include <ATen/DynamicLibrary.h>
|
|
#include <ATen/core/dispatch/Dispatcher.h>
|
|
#include <cuda_runtime.h>
|
|
#include <glog/logging.h>
|
|
#include <torch/all.h>
|
|
|
|
#include <optional>
|
|
|
|
#include "ATen/Tensor.h"
|
|
#include "ATen/cuda/CUDAEvent.h"
|
|
#include "c10/core/Device.h"
|
|
#include "c10/core/DeviceGuard.h"
|
|
#include "c10/core/GradMode.h"
|
|
#include "c10/core/InferenceMode.h"
|
|
#include "c10/core/MemoryFormat.h"
|
|
#include "c10/core/ScalarType.h"
|
|
#include "c10/core/TensorOptions.h"
|
|
#include "c10/cuda/CUDAFunctions.h"
|
|
#include "c10/cuda/CUDAGuard.h"
|
|
#include "c10/cuda/CUDAStream.h"
|
|
#include "ixformer.h"
|
|
#include "kernels/kernels.h"
|
|
|
|
// #include "utils.h"
|
|
using namespace ixformer;
|
|
|
|
namespace xllm::kernel::ilu {
|
|
|
|
void apply_rope_pos_ids_cos_sin_cache(torch::Tensor& query,
|
|
torch::Tensor& key,
|
|
torch::Tensor& cos_sin_cache,
|
|
torch::Tensor& positions,
|
|
bool interleave);
|
|
|
|
// act_mode only support silu, gelu, gelu_tanh
|
|
void act_and_mul(torch::Tensor out,
|
|
torch::Tensor input,
|
|
const std::string& act_mode);
|
|
|
|
void reshape_paged_cache(
|
|
torch::Tensor& key, // (num_tokens, num_heads, head_size)
|
|
std::optional<torch::Tensor>& value, // (num_tokens, num_heads, head_size)
|
|
torch::Tensor& key_cache, // (num_blocks, num_heads, block_size, head_size)
|
|
std::optional<torch::Tensor>&
|
|
value_cache, // (num_blocks, num_heads, block_size, head_size)
|
|
torch::Tensor& slot_mapping); //(num_tokens)
|
|
|
|
void batch_prefill(torch::Tensor& query,
|
|
const torch::Tensor& key,
|
|
const std::optional<torch::Tensor>& value,
|
|
torch::Tensor& output,
|
|
std::optional<torch::Tensor>& output_lse,
|
|
const std::optional<torch::Tensor>& q_cu_seq_lens,
|
|
const std::optional<torch::Tensor>& kv_cu_seq_lens,
|
|
const std::optional<torch::Tensor>& alibi_slope,
|
|
const std::optional<torch::Tensor>& attn_bias,
|
|
const std::optional<torch::Tensor>& q_quant_scale,
|
|
const std::optional<torch::Tensor>& k_quant_scale,
|
|
const std::optional<torch::Tensor>& v_quant_scale,
|
|
const torch::Tensor& block_tables,
|
|
int64_t max_query_len,
|
|
int64_t max_seq_len,
|
|
float scale,
|
|
bool is_causal,
|
|
int64_t window_size_left,
|
|
int64_t window_size_right,
|
|
const std::string& compute_dtype,
|
|
bool return_lse);
|
|
|
|
void batch_decode(torch::Tensor& query,
|
|
const torch::Tensor& k_cache,
|
|
torch::Tensor& output,
|
|
const torch::Tensor& block_table,
|
|
const torch::Tensor& seq_lens,
|
|
const std::optional<torch::Tensor>& v_cache,
|
|
std::optional<torch::Tensor>& output_lse,
|
|
const std::optional<torch::Tensor>& q_quant_scale,
|
|
const std::optional<torch::Tensor>& k_cache_quant_scale,
|
|
const std::optional<torch::Tensor>& v_cache_quant_scale,
|
|
const std::optional<torch::Tensor>& out_quant_scale,
|
|
const std::optional<torch::Tensor>& alibi_slope,
|
|
const std::optional<torch::Tensor>& mask,
|
|
const std::string& compute_dtype,
|
|
int64_t max_seq_len,
|
|
int64_t window_size_left,
|
|
int64_t window_size_right,
|
|
float scale,
|
|
bool return_lse,
|
|
bool is_causal,
|
|
int64_t kv_cache_quant_bit_size);
|
|
|
|
void residual_layer_norm(torch::Tensor& input,
|
|
torch::Tensor& output,
|
|
std::optional<torch::Tensor>& residual,
|
|
torch::Tensor& weight,
|
|
std::optional<torch::Tensor>& bias,
|
|
std::optional<torch::Tensor>& residual_out,
|
|
double eps);
|
|
|
|
void rms_norm(torch::Tensor& output,
|
|
torch::Tensor& input,
|
|
torch::Tensor& weight,
|
|
double eps);
|
|
|
|
torch::Tensor matmul(torch::Tensor a,
|
|
torch::Tensor b,
|
|
std::optional<torch::Tensor> bias);
|
|
|
|
std::tuple<torch::Tensor, torch::Tensor> moe_active_topk(
|
|
const torch::Tensor& input,
|
|
int64_t topk,
|
|
int64_t num_expert_group,
|
|
int64_t topk_group,
|
|
bool normalize,
|
|
const std::optional<torch::Tensor>& mask,
|
|
const std::string& normed_by,
|
|
const std::string& scoring_func,
|
|
double route_scale,
|
|
const std::optional<torch::Tensor>& e_score_correction_bias);
|
|
|
|
std::vector<torch::Tensor> moe_gen_idx(torch::Tensor& expert_id,
|
|
int64_t expert_num);
|
|
|
|
torch::Tensor moe_expand_input(const torch::Tensor& input,
|
|
const torch::Tensor& gather_index,
|
|
const torch::Tensor& combine_idx,
|
|
int64_t topk);
|
|
|
|
torch::Tensor group_gemm(torch::Tensor& input,
|
|
torch::Tensor& weight,
|
|
torch::Tensor& tokens_per_experts,
|
|
const std::optional<torch::Tensor>& dst_to_src,
|
|
torch::Tensor& output);
|
|
|
|
torch::Tensor moe_combine_result(torch::Tensor& input, torch::Tensor& weight);
|
|
} // namespace xllm::kernel::ilu
|