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
163 lines
6.9 KiB
C++
163 lines
6.9 KiB
C++
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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 "ilu_ops_api.h"
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#include "ixinfer.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 reshape_paged_cache(torch::Tensor& key,
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std::optional<torch::Tensor>& value,
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torch::Tensor& key_cache,
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std::optional<torch::Tensor>& value_cache,
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torch::Tensor& slot_mapping) {
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auto value_ = value.value_or(torch::Tensor());
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auto value_cache_ = value_cache.value_or(torch::Tensor());
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int64_t key_token_stride = key.stride(0);
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int64_t value_token_stride = 0;
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if (value_.defined()) {
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value_token_stride = value_.stride(0);
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}
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slot_mapping = slot_mapping.to(at::kLong);
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infer::xllm_reshape_and_cache(key,
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value_,
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key_cache,
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value_cache_,
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slot_mapping,
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key_token_stride,
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value_token_stride);
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}
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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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double softcap = 0.0;
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bool sqrt_alibi = false;
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auto q_cu_seq_lens_ = q_cu_seq_lens.value_or(torch::Tensor());
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auto kv_cu_seq_lens_ = kv_cu_seq_lens.value_or(torch::Tensor());
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auto q_quant_scale_ = q_quant_scale.value_or(torch::Tensor());
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auto k_quant_scale_ = k_quant_scale.value_or(torch::Tensor());
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auto v_quant_scale_ = v_quant_scale.value_or(torch::Tensor());
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auto block_tables_ = block_tables;
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auto key_ = key;
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auto value_ = value.value();
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infer::ixinfer_flash_attn_unpad_with_block_tables(query,
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key_,
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value_,
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output,
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block_tables_,
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q_cu_seq_lens_,
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kv_cu_seq_lens_,
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max_query_len,
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max_seq_len,
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is_causal,
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window_size_left,
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window_size_right,
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static_cast<double>(scale),
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softcap,
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sqrt_alibi,
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alibi_slope,
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c10::nullopt,
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output_lse);
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}
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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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if (query.dim() == 4) {
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query =
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query
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.view({query.size(0) * query.size(1), query.size(2), query.size(3)})
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.contiguous();
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}
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if (output.dim() == 4) {
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output = output
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.view({output.size(0) * output.size(1),
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output.size(2),
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output.size(3)})
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.contiguous();
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;
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}
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auto v_cache_ = v_cache.value_or(torch::Tensor());
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int64_t num_kv_heads = k_cache.size(1);
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int64_t page_block_size = k_cache.size(2);
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double softcap = 0.0;
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bool enable_cuda_graph = false;
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bool use_sqrt_alibi = false;
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auto block_table_ = block_table;
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auto k_cache_ = k_cache;
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auto seq_lens_ = seq_lens;
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infer::xllm_paged_attention(output,
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query,
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k_cache_,
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v_cache_,
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num_kv_heads,
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scale,
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block_table_,
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seq_lens_,
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page_block_size,
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max_seq_len,
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alibi_slope,
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is_causal,
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(int32_t)window_size_left,
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(int32_t)window_size_right,
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softcap,
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enable_cuda_graph,
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use_sqrt_alibi,
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c10::nullopt);
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}
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} // namespace xllm::kernel::ilu
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