forked from Karylab-cklius/vllm
91 lines
4.0 KiB
C++
91 lines
4.0 KiB
C++
#pragma once
|
|
|
|
#include <torch/csrc/stable/tensor.h>
|
|
|
|
#include <optional>
|
|
#include <tuple>
|
|
|
|
void topk_softmax(torch::stable::Tensor& topk_weights,
|
|
torch::stable::Tensor& topk_indices,
|
|
torch::stable::Tensor& token_expert_indices,
|
|
torch::stable::Tensor& gating_output, bool renormalize,
|
|
std::optional<torch::stable::Tensor> bias);
|
|
|
|
void topk_sigmoid(torch::stable::Tensor& topk_weights,
|
|
torch::stable::Tensor& topk_indices,
|
|
torch::stable::Tensor& token_expert_indices,
|
|
torch::stable::Tensor& gating_output, bool renormalize,
|
|
std::optional<torch::stable::Tensor> bias,
|
|
double routed_scaling_factor);
|
|
|
|
void topk_softplus_sqrt(
|
|
torch::stable::Tensor& topk_weights, torch::stable::Tensor& topk_indices,
|
|
torch::stable::Tensor& token_expert_indices,
|
|
torch::stable::Tensor& gating_output, bool renormalize,
|
|
double routed_scaling_factor,
|
|
const std::optional<torch::stable::Tensor>& correction_bias,
|
|
const std::optional<torch::stable::Tensor>& input_ids,
|
|
const std::optional<torch::stable::Tensor>& tid2eid);
|
|
|
|
void moe_sum(torch::stable::Tensor& input, torch::stable::Tensor& output,
|
|
std::optional<torch::stable::Tensor> topk_ids,
|
|
std::optional<torch::stable::Tensor> expert_map);
|
|
|
|
void moe_align_block_size(
|
|
torch::stable::Tensor topk_ids, int64_t num_experts, int64_t block_size,
|
|
torch::stable::Tensor sorted_token_ids, torch::stable::Tensor experts_ids,
|
|
torch::stable::Tensor num_tokens_post_pad,
|
|
std::optional<torch::stable::Tensor> maybe_expert_map);
|
|
|
|
void batched_moe_align_block_size(
|
|
int64_t max_tokens_per_batch, int64_t block_size,
|
|
const torch::stable::Tensor& expert_num_tokens,
|
|
torch::stable::Tensor sorted_ids, torch::stable::Tensor expert_ids,
|
|
torch::stable::Tensor num_tokens_post_pad);
|
|
|
|
void moe_lora_align_block_size(
|
|
torch::stable::Tensor topk_ids, torch::stable::Tensor token_lora_mapping,
|
|
int64_t num_experts, int64_t block_size, int64_t max_loras,
|
|
int64_t max_num_tokens_padded, int64_t max_num_m_blocks,
|
|
torch::stable::Tensor sorted_token_ids, torch::stable::Tensor expert_ids,
|
|
torch::stable::Tensor num_tokens_post_pad,
|
|
torch::stable::Tensor adapter_enabled, torch::stable::Tensor lora_ids,
|
|
std::optional<torch::stable::Tensor> maybe_expert_map);
|
|
#ifndef USE_ROCM
|
|
torch::stable::Tensor moe_wna16_gemm(
|
|
torch::stable::Tensor input, torch::stable::Tensor output,
|
|
torch::stable::Tensor b_qweight, torch::stable::Tensor b_scales,
|
|
std::optional<torch::stable::Tensor> b_qzeros,
|
|
std::optional<torch::stable::Tensor> topk_weights,
|
|
torch::stable::Tensor sorted_token_ids, torch::stable::Tensor expert_ids,
|
|
torch::stable::Tensor num_tokens_post_pad, int64_t top_k,
|
|
int64_t BLOCK_SIZE_M, int64_t BLOCK_SIZE_N, int64_t BLOCK_SIZE_K,
|
|
int64_t bit);
|
|
|
|
std::tuple<torch::stable::Tensor, torch::stable::Tensor> grouped_topk(
|
|
const torch::stable::Tensor& scores, int64_t n_group, int64_t topk_group,
|
|
int64_t topk, bool renormalize, double routed_scaling_factor,
|
|
const torch::stable::Tensor& bias, int64_t scoring_func);
|
|
#endif
|
|
|
|
bool moe_permute_unpermute_supported();
|
|
|
|
int64_t moe_permute_sort_workspace_size(int64_t num_expanded_rows,
|
|
int64_t num_expert);
|
|
|
|
void shuffle_rows(const torch::stable::Tensor& input_tensor,
|
|
const torch::stable::Tensor& dst2src_map,
|
|
torch::stable::Tensor& output_tensor);
|
|
|
|
#ifndef USE_ROCM
|
|
// DeepSeek V3 optimized router GEMM kernel for SM90+
|
|
// Computes output = mat_a @ mat_b.T where:
|
|
// mat_a: [num_tokens, hidden_dim] in bf16
|
|
// mat_b: [num_experts, hidden_dim] in bf16
|
|
// output: [num_tokens, num_experts] in bf16 or fp32
|
|
// Supports num_tokens in [1, 16], num_experts in {256, 384}, hidden_dim = 7168
|
|
void dsv3_router_gemm(torch::stable::Tensor& output,
|
|
const torch::stable::Tensor& mat_a,
|
|
const torch::stable::Tensor& mat_b);
|
|
#endif
|