f4e2264a83
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}
124 lines
4.4 KiB
C++
124 lines
4.4 KiB
C++
/* Copyright 2026 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 "kernels/cuda/cuda_ops_api.h"
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#include "kernels/cuda/utils.h"
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#include "platform/device.h"
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namespace xllm::kernel::cuda {
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torch::Tensor cutlass_fused_moe(
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const torch::Tensor& input, // [num_tokens, hidden]
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const torch::Tensor& token_selected_experts, // [num_tokens, top_k]
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const torch::Tensor& token_final_scales, // [num_tokens, top_k]
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const torch::Tensor&
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fc1_expert_weights, // [num_experts, inter_dim, hidden]
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const torch::Tensor&
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fc2_expert_weights, // [num_experts, hidden, inter_dim]
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torch::ScalarType output_dtype,
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const std::vector<torch::Tensor>& quant_scales,
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int32_t tp_size,
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int32_t tp_rank,
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int32_t ep_size,
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int32_t ep_rank,
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int32_t cluster_size,
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int32_t cluster_rank,
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const std::optional<torch::Tensor>& fc1_expert_biases,
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const std::optional<torch::Tensor>& fc2_expert_biases,
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const std::optional<torch::Tensor>& input_sf,
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const std::optional<torch::Tensor>& swiglu_alpha,
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const std::optional<torch::Tensor>& swiglu_beta,
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const std::optional<torch::Tensor>& swiglu_limit,
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const std::optional<torch::Tensor>& output,
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bool enable_alltoall,
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bool use_deepseek_fp8_block_scale,
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bool use_w4_group_scaling,
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bool use_mxfp8_act_scaling,
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bool min_latency_mode,
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bool use_packed_weights,
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int32_t tune_max_num_tokens,
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ActivationType activation_type) {
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int64_t num_rows = input.size(0);
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int64_t hidden_size = fc2_expert_weights.size(1);
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if (min_latency_mode) {
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num_rows *= fc2_expert_weights.size(0);
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}
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std::vector<int64_t> output_shape = {num_rows, hidden_size};
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torch::Tensor result_output;
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if (output.has_value() && output.value().defined()) {
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result_output = output.value();
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} else {
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torch::TensorOptions options = input.options().dtype(output_dtype);
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result_output = torch::empty(output_shape, options);
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}
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std::string fused_moe_uri = "fused_moe";
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if (Device::is_support_sm90a()) {
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fused_moe_uri += "_90";
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} else if (Device::is_support_sm100a() || Device::is_support_sm100f()) {
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fused_moe_uri += "_100";
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} else if (Device::is_support_sm120a()) {
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fused_moe_uri += "_120";
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} else {
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LOG(FATAL) << "FusedMoE is only supported on sm90, sm100, sm120.";
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}
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bind_tvmffi_stream_to_current_torch_stream(input.device());
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ffi::Module fused_moe_runner =
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get_function(fused_moe_uri, "init")(
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to_dl_data_type(input.scalar_type()),
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to_dl_data_type(fc1_expert_weights.scalar_type()),
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to_dl_data_type(output_dtype),
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use_deepseek_fp8_block_scale,
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use_w4_group_scaling,
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use_mxfp8_act_scaling,
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use_packed_weights)
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.cast<ffi::Module>();
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fused_moe_runner->GetFunction("run_moe").value()(
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to_ffi_tensor(result_output),
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to_ffi_tensor(input),
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to_ffi_tensor(token_selected_experts),
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to_ffi_optional_tensor(token_final_scales),
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to_ffi_tensor(fc1_expert_weights),
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to_ffi_optional_tensor(fc1_expert_biases),
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to_ffi_tensor(fc2_expert_weights),
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to_ffi_optional_tensor(fc2_expert_biases),
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to_ffi_optional_array_tensors(quant_scales),
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to_ffi_optional_tensor(input_sf),
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to_ffi_optional_tensor(swiglu_alpha),
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to_ffi_optional_tensor(swiglu_beta),
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to_ffi_optional_tensor(swiglu_limit),
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tp_size,
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tp_rank,
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ep_size,
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ep_rank,
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cluster_size,
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cluster_rank,
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enable_alltoall,
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min_latency_mode,
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/*profile_ids=*/ffi::Optional<ffi::Array<int64_t>>(), // TODO: support
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// auto tuning
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// profile ids
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support_pdl(),
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activation_type);
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return result_output;
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}
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} // namespace xllm::kernel::cuda
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