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}
148 lines
5.6 KiB
C++
148 lines
5.6 KiB
C++
/* 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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