231 lines
10 KiB
C++
231 lines
10 KiB
C++
/* Copyright 2025 SGLang Team. 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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http://www.apache.org/licenses/LICENSE-2.0
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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 <ATen/core/dispatch/Dispatcher.h>
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#include <torch/library.h>
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#include "sgl_kernel_ops.h"
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TORCH_LIBRARY_EXPAND(sgl_kernel, m) {
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/*
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* From csrc/elementwise
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*/
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m.def("silu_and_mul(Tensor! out, Tensor input) -> ()");
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m.impl("silu_and_mul", torch::kCUDA, &silu_and_mul);
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m.def("gelu_tanh_and_mul(Tensor! out, Tensor input) -> ()");
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m.impl("gelu_tanh_and_mul", torch::kCUDA, &gelu_tanh_and_mul);
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m.def("gelu_and_mul(Tensor! out, Tensor input) -> ()");
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m.impl("gelu_and_mul", torch::kCUDA, &gelu_and_mul);
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m.def("gelu_quick(Tensor! out, Tensor input) -> ()");
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m.impl("gelu_quick", torch::kCUDA, &gelu_quick);
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m.def("fast_topk(Tensor score, Tensor indices, Tensor lengths, Tensor? row_starts) -> ()");
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m.impl("fast_topk", torch::kCUDA, &fast_topk_interface);
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m.def(
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"fast_topk_transform_fused(Tensor score, Tensor lengths, Tensor dst_page_table, Tensor src_page_table, Tensor "
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"cu_seqlens_q, Tensor? row_starts) -> ()");
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m.impl("fast_topk_transform_fused", torch::kCUDA, &fast_topk_transform_interface);
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m.def(
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"fast_topk_transform_ragged_fused(Tensor score, Tensor lengths, Tensor topk_indices_ragged, Tensor "
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"topk_indices_offset, Tensor ? row_starts) -> ()");
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m.impl("fast_topk_transform_ragged_fused", torch::kCUDA, &fast_topk_transform_ragged_interface);
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/*
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* From csrc/allreduce
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*/
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m.def(
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"init_custom_ar(Tensor meta, Tensor rank_data, "
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"str[] handles, int[] offsets, int rank, "
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"bool full_nvlink) -> int");
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m.impl("init_custom_ar", torch::kCUDA, &init_custom_ar);
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m.def("all_reduce_reg(int fa, Tensor inp, Tensor! out) -> ()");
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m.impl("all_reduce_reg", torch::kCUDA, &all_reduce_reg);
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m.def(
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"all_reduce_unreg(int fa, Tensor inp, Tensor reg_buffer, Tensor! out) -> "
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"()");
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m.impl("all_reduce_unreg", torch::kCUDA, &all_reduce_unreg);
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// Deterministic all-reduce for ROCm
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extern void deterministic_all_reduce_reg(int64_t _fa, torch::Tensor& inp, torch::Tensor& out);
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extern void deterministic_all_reduce_unreg(
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int64_t _fa, torch::Tensor& inp, torch::Tensor& reg_buffer, torch::Tensor& out);
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m.def("deterministic_all_reduce_reg(int fa, Tensor inp, Tensor! out) -> ()");
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m.impl("deterministic_all_reduce_reg", torch::kCUDA, &deterministic_all_reduce_reg);
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m.def("deterministic_all_reduce_unreg(int fa, Tensor inp, Tensor reg_buffer, Tensor! out) -> ()");
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m.impl("deterministic_all_reduce_unreg", torch::kCUDA, &deterministic_all_reduce_unreg);
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m.def("dispose", &dispose);
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m.def("meta_size", &meta_size);
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m.def(
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"register_buffer(int fa, Tensor t, str[] handles, "
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"int[] offsets) -> ()");
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m.impl("register_buffer", torch::kCUDA, ®ister_buffer);
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m.def("get_graph_buffer_ipc_meta", &get_graph_buffer_ipc_meta);
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m.def("register_graph_buffers", ®ister_graph_buffers);
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m.def("allocate_meta_buffer", &allocate_meta_buffer);
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m.impl("allocate_meta_buffer", torch::kCUDA, &allocate_meta_buffer);
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m.def("get_meta_buffer_ipc_handle", &get_meta_buffer_ipc_handle);
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m.impl("get_meta_buffer_ipc_handle", torch::kCPU, &get_meta_buffer_ipc_handle);
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// quick allreduce
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m.def(
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"qr_all_reduce(int fa, Tensor inp, Tensor out, int quant_level, bool "
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"cast_bf2half) -> ()");
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m.impl("qr_all_reduce", torch::kCUDA, &qr_all_reduce);
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m.def("init_custom_qr", &init_custom_qr);
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m.def("qr_destroy", &qr_destroy);
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m.def("qr_get_handle", &qr_get_handle);
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m.def("qr_open_handles(int _fa, Tensor[](b!) handles) -> ()");
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m.impl("qr_open_handles", torch::kCPU, &qr_open_handles);
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// Max input size in bytes
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m.def("qr_max_size", &qr_max_size);
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/*
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* From csrc/moe
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*/
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m.def(
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"moe_align_block_size(Tensor topk_ids, int num_experts, int block_size, Tensor! sorted_token_ids, Tensor! "
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"experts_ids, Tensor! num_tokens_post_pad, Tensor! cumsum_buffer, bool "
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"pad_sorted_token_ids) -> ()");
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m.impl("moe_align_block_size", torch::kCUDA, &moe_align_block_size);
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m.def(
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"topk_softmax(Tensor! topk_weights, Tensor! topk_indices, Tensor gating_output, bool renormalize, float "
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"moe_softcapping, Tensor? correction_bias) -> ()");
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m.impl("topk_softmax", torch::kCUDA, &topk_softmax);
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m.def(
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"topk_sigmoid(Tensor! topk_weights, Tensor! topk_indices, Tensor gating_output, bool renormalize, Tensor? "
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"correction_bias) -> ()");
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m.impl("topk_sigmoid", torch::kCUDA, &topk_sigmoid);
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/*
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* From csrc/speculative
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*/
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m.def(
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"verify_tree_greedy(Tensor! predicts, Tensor! accept_index, Tensor! accept_token_num, "
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"Tensor candidates, Tensor retrive_index, Tensor retrive_next_token, Tensor retrive_next_sibling, "
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"Tensor target_predict) -> ()");
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m.impl("verify_tree_greedy", torch::kCUDA, &verify_tree_greedy);
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m.def(
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"build_tree_kernel_efficient(Tensor parent_list, Tensor selected_index, Tensor verified_seq_len, "
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"Tensor! tree_mask, Tensor! positions, Tensor! retrive_index, Tensor! retrive_next_token, "
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"Tensor! retrive_next_sibling, int topk, int depth, int draft_token_num, int tree_mask_mode) -> "
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"()");
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m.impl("build_tree_kernel_efficient", torch::kCUDA, &build_tree_kernel_efficient);
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/*
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* From csrc/kvcacheio
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*/
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m.def(
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"transfer_kv_per_layer(Tensor src_k, Tensor dst_k, Tensor src_v, Tensor dst_v, Tensor src_indices, Tensor "
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"dst_indices, int item_size, int block_quota, int num_warps_per_block) -> ()");
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m.impl("transfer_kv_per_layer", torch::kCUDA, &transfer_kv_per_layer);
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m.def(
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"transfer_kv_per_layer_pf_lf(Tensor src_k, Tensor dst_k, Tensor src_v, Tensor dst_v, Tensor src_indices, Tensor "
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"dst_indices, int layer_id, int item_size, int src_layout_dim, int block_quota, int num_warps_per_block) -> ()");
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m.impl("transfer_kv_per_layer_pf_lf", torch::kCUDA, &transfer_kv_per_layer_pf_lf);
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m.def(
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"transfer_kv_all_layer(Tensor src_k_layers, Tensor dst_k_layers, Tensor src_v_layers, Tensor dst_v_layers, "
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"Tensor src_indices, Tensor dst_indices, int item_size, int num_layers, int block_quota, int "
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"num_warps_per_block) -> ()");
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m.impl("transfer_kv_all_layer", torch::kCUDA, &transfer_kv_all_layer);
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m.def(
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"transfer_kv_all_layer_lf_pf(Tensor src_k_layers, Tensor dst_k, Tensor src_v_layers, Tensor dst_v, "
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"Tensor src_indices, Tensor dst_indices, int item_size, int dst_layout_dim, int num_layers, int block_quota, int "
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"num_warps_per_block) -> ()");
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m.impl("transfer_kv_all_layer_lf_pf", torch::kCUDA, &transfer_kv_all_layer_lf_pf);
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m.def(
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"transfer_kv_per_layer_mla(Tensor src, Tensor dst, Tensor src_indices, Tensor dst_indices, int item_size, int "
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"block_quota, int num_warps_per_block) -> ()");
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m.impl("transfer_kv_per_layer_mla", torch::kCUDA, &transfer_kv_per_layer_mla);
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m.def(
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"transfer_kv_per_layer_mla_pf_lf(Tensor src, Tensor dst, Tensor src_indices, Tensor dst_indices, int layer_id, "
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"int item_size, int src_layout_dim, int block_quota, int num_warps_per_block) -> ()");
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m.impl("transfer_kv_per_layer_mla_pf_lf", torch::kCUDA, &transfer_kv_per_layer_mla_pf_lf);
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m.def(
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"transfer_kv_all_layer_mla(Tensor src_layers, Tensor dst_layers, Tensor src_indices, Tensor dst_indices, int "
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"item_size, int num_layers, int block_quota, int num_warps_per_block) -> ()");
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m.impl("transfer_kv_all_layer_mla", torch::kCUDA, &transfer_kv_all_layer_mla);
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m.def(
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"transfer_kv_all_layer_mla_lf_pf(Tensor src_layers, Tensor dst, Tensor src_indices, Tensor dst_indices, "
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"int item_size, int dst_layout_dim, int num_layers, int block_quota, int num_warps_per_block) -> ()");
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m.impl("transfer_kv_all_layer_mla_lf_pf", torch::kCUDA, &transfer_kv_all_layer_mla_lf_pf);
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m.def(
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"transfer_kv_direct(Tensor[] src_layers, Tensor[] dst_layers, Tensor src_indices, Tensor dst_indices, int "
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"page_size) -> ()");
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m.impl("transfer_kv_direct", torch::kCUDA, &transfer_kv_direct);
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m.def(
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"transfer_kv_per_layer_direct_pf_lf(Tensor[] src_ptrs, Tensor[] dst_ptrs, Tensor src_indices, "
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"Tensor dst_indices, int layer_id, int page_size)->() ");
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m.impl("transfer_kv_per_layer_direct_pf_lf", torch::kCUDA, &transfer_kv_per_layer_direct_pf_lf);
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m.def(
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"transfer_kv_all_layer_direct_lf_pf(Tensor[] src_ptrs, Tensor[] dst_ptrs, Tensor src_indices, "
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"Tensor dst_indices, int page_size) ->() ");
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m.impl("transfer_kv_all_layer_direct_lf_pf", torch::kCUDA, &transfer_kv_all_layer_direct_lf_pf);
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m.def(
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"transfer_kv_all_layer_lf_ph(Tensor src_k_layers, Tensor dst_k, Tensor src_v_layers, Tensor dst_v, "
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"Tensor src_indices, Tensor dst_indices, int item_size, int dst_layout_dim, int num_layers, int page_size, int "
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"head_num, int block_quota, int num_warps_per_block) -> ()");
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m.impl("transfer_kv_all_layer_lf_ph", torch::kCUDA, &transfer_kv_all_layer_lf_ph);
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m.def(
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"transfer_kv_per_layer_ph_lf(Tensor src_k, Tensor dst_k, Tensor src_v, Tensor dst_v, Tensor src_indices, Tensor "
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"dst_indices, int layer_id, int item_size, int src_layout_dim, int page_size, int head_num, int block_quota, int "
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"num_warps_per_block) -> ()");
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m.impl("transfer_kv_per_layer_ph_lf", torch::kCUDA, &transfer_kv_per_layer_ph_lf);
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/*
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* From csrc/grammar
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*/
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m.def("apply_token_bitmask_inplace_cuda(Tensor logits, Tensor bitmask, Tensor? indices=None) -> ()");
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m.impl("apply_token_bitmask_inplace_cuda", &ApplyTokenBitmaskInplace);
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/*
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* From csrc/elementwise
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*/
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m.def(
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"rotary_embedding(Tensor positions, Tensor! query,"
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" Tensor!? key, int head_size,"
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" Tensor cos_sin_cache, bool is_neox) -> ()");
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m.impl("rotary_embedding", torch::kCUDA, &rotary_embedding);
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/*
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* From csrc/memory
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*/
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m.def("weak_ref_tensor(Tensor tensor) -> Tensor");
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m.impl("weak_ref_tensor", torch::kCUDA, &weak_ref_tensor);
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}
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REGISTER_EXTENSION(common_ops)
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