ref(EX): import upstream ILU kernels + xllm MoE CUDA sources into ex_engine
Copied from upstream_ref (NOT rewritten — exact upstream code):
ixformer C++ API (the authoritative header):
include/ixformer.h — ixformer::infer namespace: topk_softmax,
moe_compute_token_index_api, moe_w16a16_group_gemm, moe_expand_input,
moe_output_reduce_sum, silu_and_mul, rms_norm, xllm_paged_attention, etc.
include/ilu_ops_api.h — xllm::kernel::ilu namespace: moe_active_topk,
moe_gen_idx, moe_expand_input, group_gemm, moe_combine_result,
batch_prefill, batch_decode, rms_norm, matmul, act_and_mul, etc.
ILU kernel wrappers (call ixformer::infer directly):
csrc/ilu_kernel_fused_moe.cpp — topk routing + gen_idx + expand + combine
csrc/ilu_kernel_group_gemm.cpp — batched expert GEMM
csrc/ilu_kernel_{activation,norm,rope,matmul,attention}.cpp
ILU layer implementations (full pipeline):
csrc/ilu_layer_fused_moe.{cpp,h} — 797 lines, the complete MoE pipeline
that competitor 168 ran as corex_moe.py
csrc/ilu_layer_attention.{cpp,h} — prefill/decode attention dispatch
CUDA MoE kernels (from xllm + ds_vllm):
csrc/moe/moe_topk_softmax_kernels.cuh — CUB BlockReduce + warp topk
csrc/moe/moe_topk_sigmoid_kernels.cuh — sigmoid scoring variant
csrc/moe/moe_topk.cuh + moe_fused_topk.cu — entry points
csrc/moe/moeTopKFuncs.cuh — TRT-LLM derived vllm-compatible topk
csrc/moe/moe_ops.h + moe_align_sum_kernels.cu — alignment kernels
Common layer headers:
csrc/common_fused_moe{,_base}.h + common_moe_fused_topk.{cpp,h}
This commit is contained in:
project6-dev
153
ex_engine/include/ilu_ops_api.h
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153
ex_engine/include/ilu_ops_api.h
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/* 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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#include <ATen/DynamicLibrary.h>
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#include <ATen/core/dispatch/Dispatcher.h>
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#include <cuda_runtime.h>
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#include <glog/logging.h>
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#include <torch/all.h>
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#include <optional>
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#include "ATen/Tensor.h"
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#include "ATen/cuda/CUDAEvent.h"
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#include "c10/core/Device.h"
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#include "c10/core/DeviceGuard.h"
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#include "c10/core/GradMode.h"
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#include "c10/core/InferenceMode.h"
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#include "c10/core/MemoryFormat.h"
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#include "c10/core/ScalarType.h"
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#include "c10/core/TensorOptions.h"
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#include "c10/cuda/CUDAFunctions.h"
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#include "c10/cuda/CUDAGuard.h"
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#include "c10/cuda/CUDAStream.h"
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#include "ixformer.h"
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#include "kernels/kernels.h"
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// #include "utils.h"
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using namespace ixformer;
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namespace xllm::kernel::ilu {
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void apply_rope_pos_ids_cos_sin_cache(torch::Tensor& query,
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torch::Tensor& key,
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torch::Tensor& cos_sin_cache,
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torch::Tensor& positions,
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bool interleave);
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// act_mode only support silu, gelu, gelu_tanh
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void act_and_mul(torch::Tensor out,
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torch::Tensor input,
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const std::string& act_mode);
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void reshape_paged_cache(
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torch::Tensor& key, // (num_tokens, num_heads, head_size)
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std::optional<torch::Tensor>& value, // (num_tokens, num_heads, head_size)
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torch::Tensor& key_cache, // (num_blocks, num_heads, block_size, head_size)
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std::optional<torch::Tensor>&
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value_cache, // (num_blocks, num_heads, block_size, head_size)
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torch::Tensor& slot_mapping); //(num_tokens)
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void batch_prefill(torch::Tensor& query,
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const torch::Tensor& key,
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const std::optional<torch::Tensor>& value,
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torch::Tensor& output,
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std::optional<torch::Tensor>& output_lse,
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const std::optional<torch::Tensor>& q_cu_seq_lens,
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const std::optional<torch::Tensor>& kv_cu_seq_lens,
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const std::optional<torch::Tensor>& alibi_slope,
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const std::optional<torch::Tensor>& attn_bias,
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const std::optional<torch::Tensor>& q_quant_scale,
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const std::optional<torch::Tensor>& k_quant_scale,
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const std::optional<torch::Tensor>& v_quant_scale,
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const torch::Tensor& block_tables,
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int64_t max_query_len,
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int64_t max_seq_len,
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float scale,
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bool is_causal,
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int64_t window_size_left,
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int64_t window_size_right,
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const std::string& compute_dtype,
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bool return_lse);
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void batch_decode(torch::Tensor& query,
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const torch::Tensor& k_cache,
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torch::Tensor& output,
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const torch::Tensor& block_table,
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const torch::Tensor& seq_lens,
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const std::optional<torch::Tensor>& v_cache,
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std::optional<torch::Tensor>& output_lse,
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const std::optional<torch::Tensor>& q_quant_scale,
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const std::optional<torch::Tensor>& k_cache_quant_scale,
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const std::optional<torch::Tensor>& v_cache_quant_scale,
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const std::optional<torch::Tensor>& out_quant_scale,
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const std::optional<torch::Tensor>& alibi_slope,
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const std::optional<torch::Tensor>& mask,
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const std::string& compute_dtype,
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int64_t max_seq_len,
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int64_t window_size_left,
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int64_t window_size_right,
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float scale,
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bool return_lse,
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bool is_causal,
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int64_t kv_cache_quant_bit_size);
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void residual_layer_norm(torch::Tensor& input,
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torch::Tensor& output,
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std::optional<torch::Tensor>& residual,
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torch::Tensor& weight,
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std::optional<torch::Tensor>& bias,
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std::optional<torch::Tensor>& residual_out,
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double eps);
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void rms_norm(torch::Tensor& output,
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torch::Tensor& input,
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torch::Tensor& weight,
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double eps);
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torch::Tensor matmul(torch::Tensor a,
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torch::Tensor b,
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std::optional<torch::Tensor> bias);
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std::tuple<torch::Tensor, torch::Tensor> moe_active_topk(
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const torch::Tensor& input,
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int64_t topk,
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int64_t num_expert_group,
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int64_t topk_group,
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bool normalize,
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const std::optional<torch::Tensor>& mask,
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const std::string& normed_by,
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const std::string& scoring_func,
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double route_scale,
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const std::optional<torch::Tensor>& e_score_correction_bias);
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std::vector<torch::Tensor> moe_gen_idx(torch::Tensor& expert_id,
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int64_t expert_num);
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torch::Tensor moe_expand_input(const torch::Tensor& input,
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const torch::Tensor& gather_index,
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const torch::Tensor& combine_idx,
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int64_t topk);
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torch::Tensor group_gemm(torch::Tensor& input,
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torch::Tensor& weight,
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torch::Tensor& tokens_per_experts,
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const std::optional<torch::Tensor>& dst_to_src,
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torch::Tensor& output);
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torch::Tensor moe_combine_result(torch::Tensor& input, torch::Tensor& weight);
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} // namespace xllm::kernel::ilu
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63
ex_engine/include/ilu_utils.h
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63
ex_engine/include/ilu_utils.h
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@@ -0,0 +1,63 @@
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/* 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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147
ex_engine/include/ixformer.h
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147
ex_engine/include/ixformer.h
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@@ -0,0 +1,147 @@
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/* 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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#include <torch/all.h>
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#include "ATen/Tensor.h"
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#include "utils.h"
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namespace ixformer::infer {
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torch::Tensor ixinfer_flash_attn_unpad_with_block_tables(
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torch::Tensor& query,
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torch::Tensor& key_cache,
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torch::Tensor& value_cache,
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torch::Tensor& out,
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torch::Tensor& block_tables,
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torch::Tensor& cu_seq_q,
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torch::Tensor& cu_seq_k,
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int64_t max_seq_q,
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int64_t max_seq_k,
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bool is_causal,
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int64_t window_left,
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int64_t window_right,
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double scale,
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double softcap,
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bool sqrt_alibi,
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const std::optional<torch::Tensor>& alibi_slopes,
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const std::optional<torch::Tensor>& sinks,
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std::optional<torch::Tensor>& lse);
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void silu_and_mul(torch::Tensor& input, torch::Tensor& output);
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torch::Tensor xllm_paged_attention(
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torch::Tensor& out,
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torch::Tensor& query,
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torch::Tensor& key_cache,
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torch::Tensor& value_cache,
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int64_t num_kv_heads,
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double scale,
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torch::Tensor& block_tables,
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torch::Tensor& context_lens,
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int64_t block_size,
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int64_t max_context_len,
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const std::optional<torch::Tensor>& alibi_slopes,
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bool causal,
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int32_t window_left,
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int32_t window_right,
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double softcap,
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bool enable_cuda_graph,
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bool use_sqrt_alibi,
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const std::optional<torch::Tensor>& sinks);
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torch::Tensor ixformer_linear(torch::Tensor& input,
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torch::Tensor& weight,
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int64_t act_type,
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const std::optional<torch::Tensor>& bias,
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const std::optional<torch::Tensor>& out,
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const std::optional<bool> persistent);
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torch::Tensor ixformer_linear_ex(torch::Tensor& input,
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torch::Tensor& weight,
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const c10::optional<torch::Tensor>& bias,
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const c10::optional<torch::Tensor>& out);
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void xllm_reshape_and_cache(torch::Tensor& key,
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torch::Tensor& value,
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torch::Tensor& key_cache,
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torch::Tensor& value_cache,
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torch::Tensor& slot_mapping,
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int64_t key_token_stride,
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int64_t value_token_stride);
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void xllm_rotary_embedding(torch::Tensor& positions,
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torch::Tensor& query,
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torch::Tensor& key,
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int64_t head_size,
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torch::Tensor& cos_sin_cache,
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bool is_neox);
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void residual_rms_norm(torch::Tensor& input,
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torch::Tensor& residual,
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torch::Tensor& weight,
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torch::Tensor& output,
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torch::Tensor& residual_output,
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const std::optional<torch::Tensor>& fused_bias,
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double alpha,
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double eps,
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bool is_post);
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void rms_norm(torch::Tensor& input,
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torch::Tensor& weight,
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torch::Tensor& output,
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const std::optional<torch::Tensor>& fused_bias,
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double eps);
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void topk_softmax(torch::Tensor& topk_weights,
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torch::Tensor& topk_indices,
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torch::Tensor& token_expert_indices,
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torch::Tensor& gating_output,
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bool renormalize);
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void moe_compute_token_index_api(
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torch::Tensor& topk_ids,
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torch::Tensor& src_dst,
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torch::Tensor& dst_src,
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torch::Tensor& expert_sizes_gpu,
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const c10::optional<torch::Tensor>& expert_mask,
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const c10::optional<torch::Tensor>& expert_sizes_cpu,
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const c10::optional<torch::Tensor>& expand_tokens_gpu,
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int64_t start_expert_id,
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int64_t end_expert_id,
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int64_t num_experts);
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void moe_expand_input(torch::Tensor outputs,
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torch::Tensor inputs,
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torch::Tensor dst_to_src,
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const c10::optional<torch::Tensor>& src_to_dst,
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int64_t dst_tokens,
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int64_t expand_factor);
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void moe_w16a16_group_gemm(torch::Tensor output,
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torch::Tensor inputs,
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torch::Tensor weights,
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torch::Tensor tokens_per_experts,
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const c10::optional<torch::Tensor>& dst_to_src,
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const c10::optional<torch::Tensor>& bias,
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std::string format,
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int64_t persistent,
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int64_t output_n);
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void moe_output_reduce_sum(torch::Tensor outputs,
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torch::Tensor inputs,
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const c10::optional<torch::Tensor>& mul_weight,
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const c10::optional<torch::Tensor>& mask,
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const c10::optional<torch::Tensor>& extra_residual,
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double scaling_factor);
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} // namespace ixformer::infer
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