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#pragma once
#include <torch/torch.h>
std::vector<torch::Tensor>
mha_fwd(const torch::Tensor &q, // batch_size x seqlen_q x num_heads x round_multiple(head_size, 8)
const torch::Tensor &k, // batch_size x seqlen_k x num_heads_k x round_multiple(head_size, 8)
const torch::Tensor &v, // batch_size x seqlen_k x num_heads_k x round_multiple(head_size, 8)
const c10::optional<torch::Tensor> &out_, // batch_size x seqlen_q x num_heads x round_multiple(head_size, 8)
const c10::optional<torch::Tensor> &alibi_slopes_, // num_heads or batch_size x num_heads
const double p_dropout,
const double softmax_scale,
bool is_causal,
const int64_t window_size_left,
const int64_t window_size_right,
const double softcap,
const bool return_softmax,
const c10::optional<at::Generator> gen_);
std::vector<torch::Tensor>
mha_varlen_fwd(
const torch::Tensor &q, // total_q x num_heads x head_size, total_q := \sum_{i=0}^{b} s_i
const torch::Tensor &k, // total_k x num_heads_k x head_size, total_k := \sum_{i=0}^{b} s_i or num_blocks x page_block_size x num_heads_k x head_>
const torch::Tensor &v, // total_k x num_heads_k x head_size, total_k := \sum_{i=0}^{b} s_i or num_blocks x page_block_size x num_heads_k x head_>
const c10::optional<torch::Tensor> &out_, // total_q x num_heads x head_size, total_k := \sum_{i=0}^{b} s_i
const torch::Tensor &cu_seqlens_q, // b+1
const torch::Tensor &cu_seqlens_k, // b+1
const c10::optional<torch::Tensor> &seqused_k_, // b. If given, only this many elements of each batch element's keys are used.
const c10::optional<torch::Tensor> &leftpad_k_, // batch_size
const c10::optional<torch::Tensor> &block_table_, // batch_size x max_num_blocks_per_seq
const c10::optional<torch::Tensor> &alibi_slopes_, // num_heads or b x num_heads
const int64_t max_seqlen_q,
const int64_t max_seqlen_k,
const double p_dropout,
const double softmax_scale,
const bool zero_tensors,
const bool is_causal,
const int64_t window_size_left,
const int64_t window_size_right,
const double softcap,
const bool return_softmax,
const c10::optional<at::Generator> gen_);
std::vector<torch::Tensor>
mha_bwd(const torch::Tensor &dout, // batch_size x seqlen_q x num_heads, x multiple_of(head_size_og, 8)
const torch::Tensor &q, // batch_size x seqlen_q x num_heads x head_size
const torch::Tensor &k, // batch_size x seqlen_k x num_heads_k x head_size
const torch::Tensor &v, // batch_size x seqlen_k x num_heads_k x head_size
const torch::Tensor &out, // batch_size x seqlen_q x num_heads x head_size
const torch::Tensor &softmax_lse, // b x h x seqlen_q
const c10::optional<torch::Tensor> &dq_, // batch_size x seqlen_q x num_heads x head_size
const c10::optional<torch::Tensor> &dk_, // batch_size x seqlen_k x num_heads_k x head_size
const c10::optional<torch::Tensor> &dv_, // batch_size x seqlen_k x num_heads_k x head_size
const c10::optional<torch::Tensor> &alibi_slopes_, // num_heads or batch_size x num_heads
const double p_dropout, // probability to drop
const double softmax_scale,
const bool is_causal,
const int64_t window_size_left,
const int64_t window_size_right,
const double softcap,
const bool deterministic,
c10::optional<at::Generator> gen_,
const c10::optional<torch::Tensor> &rng_state);
std::vector<torch::Tensor>
mha_varlen_bwd(
const torch::Tensor &dout, // batch_size x seqlen_q x num_heads, x multiple_of(head_size_og, 8)
const torch::Tensor &q, // batch_size x seqlen_q x num_heads x head_size
const torch::Tensor &k, // batch_size x seqlen_k x num_heads_k x head_size
const torch::Tensor &v, // batch_size x seqlen_k x num_heads_k x head_size
const torch::Tensor &out, // batch_size x seqlen_q x num_heads x head_size
const torch::Tensor &softmax_lse, // b x h x seqlen_q
const c10::optional<torch::Tensor> &dq_, // batch_size x seqlen_q x num_heads x head_size
const c10::optional<torch::Tensor> &dk_, // batch_size x seqlen_k x num_heads_k x head_size
const c10::optional<torch::Tensor> &dv_, // batch_size x seqlen_k x num_heads_k x head_size
const torch::Tensor &cu_seqlens_q, // batch_size + 1
const torch::Tensor &cu_seqlens_k, // batch_size + 1
const c10::optional<torch::Tensor> &alibi_slopes_, // num_heads or b x num_heads
const int64_t max_seqlen_q,
const int64_t max_seqlen_k,
const double p_dropout,
const double softmax_scale,
const bool zero_tensors,
const bool is_causal,
const int64_t window_size_left,
const int64_t window_size_right,
const double softcap,
const bool deterministic,
c10::optional<at::Generator> gen_,
const c10::optional<torch::Tensor> &rng_state);
std::vector<torch::Tensor>
mha_fwd_kvcache(
const torch::Tensor &q, // batch_size x seqlen_q x num_heads x head_size
const torch::Tensor &kcache, // batch_size_c x seqlen_k x num_heads_k x head_size or num_blocks x page_block_size x num_heads_k x head_size if there's a block_table.
const torch::Tensor &vcache, // batch_size_c x seqlen_k x num_heads_k x head_size or num_blocks x page_block_size x num_heads_k x head_size if there's a block_table.
const c10::optional<torch::Tensor> &k_, // batch_size x seqlen_knew x num_heads_k x head_size
const c10::optional<torch::Tensor> &v_, // batch_size x seqlen_knew x num_heads_k x head_size
const c10::optional<torch::Tensor> &seqlens_k_, // batch_size
const c10::optional<torch::Tensor> &rotary_cos_, // seqlen_ro x (rotary_dim / 2)
const c10::optional<torch::Tensor> &rotary_sin_, // seqlen_ro x (rotary_dim / 2)
const c10::optional<torch::Tensor> &cache_batch_idx_, // indices to index into the KV cache
const c10::optional<torch::Tensor> &leftpad_k_, // batch_size
const c10::optional<torch::Tensor> &block_table_, // batch_size x max_num_blocks_per_seq
const c10::optional<torch::Tensor> &alibi_slopes_, // num_heads or batch_size x num_heads
const c10::optional<torch::Tensor> &out_, // batch_size x seqlen_q x num_heads x head_size
const double softmax_scale,
bool is_causal,
const int64_t window_size_left,
const int64_t window_size_right,
const double softcap,
bool is_rotary_interleaved,
const int64_t num_splits);
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