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ops.h
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#pragma once
#include <optional>
#include <torch/library.h>
void paged_attention_v1(
torch::Tensor& out, torch::Tensor& query, torch::Tensor& key_cache,
torch::Tensor& value_cache, int64_t num_kv_heads, double scale,
torch::Tensor& block_tables, torch::Tensor& seq_lens, int64_t block_size,
int64_t max_seq_len, const c10::optional<torch::Tensor>& alibi_slopes,
const std::string& kv_cache_dtype, double k_scale, double v_scale,
const int64_t tp_rank, const int64_t blocksparse_local_blocks,
const int64_t blocksparse_vert_stride, const int64_t blocksparse_block_size,
const int64_t blocksparse_head_sliding_step);
void paged_attention_v2(
torch::Tensor& out, torch::Tensor& exp_sums, torch::Tensor& max_logits,
torch::Tensor& tmp_out, torch::Tensor& query, torch::Tensor& key_cache,
torch::Tensor& value_cache, int64_t num_kv_heads, double scale,
torch::Tensor& block_tables, torch::Tensor& seq_lens, int64_t block_size,
int64_t max_seq_len, const c10::optional<torch::Tensor>& alibi_slopes,
const std::string& kv_cache_dtype, double k_scale, double v_scale,
const int64_t tp_rank, const int64_t blocksparse_local_blocks,
const int64_t blocksparse_vert_stride, const int64_t blocksparse_block_size,
const int64_t blocksparse_head_sliding_step);
void rms_norm(torch::Tensor& out, torch::Tensor& input, torch::Tensor& weight,
double epsilon);
void fused_add_rms_norm(torch::Tensor& input, torch::Tensor& residual,
torch::Tensor& weight, double epsilon);
void rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
torch::Tensor& key, int64_t head_size,
torch::Tensor& cos_sin_cache, bool is_neox);
void batched_rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
torch::Tensor& key, int64_t head_size,
torch::Tensor& cos_sin_cache, bool is_neox,
int64_t rot_dim,
torch::Tensor& cos_sin_cache_offsets);
void silu_and_mul(torch::Tensor& out, torch::Tensor& input);
void gelu_and_mul(torch::Tensor& out, torch::Tensor& input);
void gelu_tanh_and_mul(torch::Tensor& out, torch::Tensor& input);
void gelu_new(torch::Tensor& out, torch::Tensor& input);
void gelu_fast(torch::Tensor& out, torch::Tensor& input);
void gelu_quick(torch::Tensor& out, torch::Tensor& input);
void advance_step(int64_t num_seqs, int64_t num_queries, int64_t block_size,
torch::Tensor& input_tokens, torch::Tensor& sampled_token_ids,
torch::Tensor& input_positions, torch::Tensor& seq_lens,
torch::Tensor& slot_mapping, torch::Tensor& block_tables);
#ifndef USE_ROCM
torch::Tensor aqlm_gemm(const torch::Tensor& input, const torch::Tensor& codes,
const torch::Tensor& codebooks,
const torch::Tensor& scales,
const torch::Tensor& codebook_partition_sizes,
const std::optional<torch::Tensor>& bias);
torch::Tensor aqlm_dequant(const torch::Tensor& codes,
const torch::Tensor& codebooks,
const torch::Tensor& codebook_partition_sizes);
torch::Tensor awq_gemm(torch::Tensor _in_feats, torch::Tensor _kernel,
torch::Tensor _scaling_factors, torch::Tensor _zeros,
int64_t split_k_iters);
torch::Tensor awq_dequantize(torch::Tensor _kernel,
torch::Tensor _scaling_factors,
torch::Tensor _zeros, int64_t split_k_iters,
int64_t thx, int64_t thy);
torch::Tensor marlin_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
torch::Tensor& b_scales, torch::Tensor& workspace,
int64_t size_m, int64_t size_n, int64_t size_k);
torch::Tensor gptq_marlin_24_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
torch::Tensor& b_meta,
torch::Tensor& b_scales,
torch::Tensor& workspace, int64_t num_bits,
int64_t size_m, int64_t size_n,
int64_t size_k);
torch::Tensor gptq_marlin_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
torch::Tensor& b_scales, torch::Tensor& b_zeros,
torch::Tensor& g_idx, torch::Tensor& perm,
torch::Tensor& workspace, int64_t num_bits,
int64_t size_m, int64_t size_n, int64_t size_k,
bool is_k_full, bool has_zp,
bool use_fp32_reduce);
torch::Tensor gptq_marlin_repack(torch::Tensor& b_q_weight, torch::Tensor& perm,
int64_t size_k, int64_t size_n,
int64_t num_bits);
torch::Tensor awq_marlin_repack(torch::Tensor& b_q_weight, int64_t size_k,
int64_t size_n, int64_t num_bits);
torch::Tensor fp8_marlin_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
torch::Tensor& b_scales, torch::Tensor& workspace,
int64_t num_bits, int64_t size_m, int64_t size_n,
int64_t size_k);
bool cutlass_scaled_mm_supports_fp8(int64_t cuda_device_capability);
void cutlass_scaled_mm(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& b, torch::Tensor const& a_scales,
torch::Tensor const& b_scales,
c10::optional<torch::Tensor> const& bias);
torch::Tensor marlin_qqq_gemm(torch::Tensor const& a,
torch::Tensor const& b_q_weight,
torch::Tensor const& s_tok,
torch::Tensor const& s_ch,
torch::Tensor const& s_group,
torch::Tensor& workspace, int64_t size_m,
int64_t size_n, int64_t size_k);
#endif
void static_scaled_int8_quant(torch::Tensor& out, torch::Tensor const& input,
torch::Tensor const& scale);
void dynamic_scaled_int8_quant(torch::Tensor& out, torch::Tensor const& input,
torch::Tensor& scales);
void squeezellm_gemm(torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
torch::Tensor lookup_table);
torch::Tensor gptq_gemm(torch::Tensor a, torch::Tensor b_q_weight,
torch::Tensor b_gptq_qzeros,
torch::Tensor b_gptq_scales, torch::Tensor b_g_idx,
bool use_exllama, int64_t bit);
void gptq_shuffle(torch::Tensor q_weight, torch::Tensor q_perm, int64_t bit);
void static_scaled_fp8_quant(torch::Tensor& out, torch::Tensor const& input,
torch::Tensor const& scale);
void dynamic_scaled_fp8_quant(torch::Tensor& out, torch::Tensor const& input,
torch::Tensor& scale);
void dynamic_per_token_scaled_fp8_quant(
torch::Tensor& out, torch::Tensor const& input, torch::Tensor& scale,
c10::optional<torch::Tensor> const& scale_ub);
void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
int64_t block_size, torch::Tensor sorted_token_ids,
torch::Tensor experts_ids,
torch::Tensor num_tokens_post_pad);
#ifndef USE_ROCM
using fptr_t = int64_t;
fptr_t init_custom_ar(torch::Tensor& meta, torch::Tensor& rank_data,
const std::vector<std::string>& handles,
const std::vector<int64_t>& offsets, int64_t rank,
bool full_nvlink);
bool should_custom_ar(torch::Tensor& inp, int64_t max_size, int64_t world_size,
bool full_nvlink);
void all_reduce_reg(fptr_t _fa, torch::Tensor& inp, torch::Tensor& out);
void all_reduce_unreg(fptr_t _fa, torch::Tensor& inp, torch::Tensor& reg_buffer,
torch::Tensor& out);
void dispose(fptr_t _fa);
int64_t meta_size();
void register_buffer(fptr_t _fa, torch::Tensor& t,
const std::vector<std::string>& handles,
const std::vector<int64_t>& offsets);
std::tuple<torch::Tensor, std::vector<int64_t>> get_graph_buffer_ipc_meta(
fptr_t _fa);
void register_graph_buffers(fptr_t _fa, const std::vector<std::string>& handles,
const std::vector<std::vector<int64_t>>& offsets);
#endif