kvcacheio.py

from typing import List

import torch


def is_hip() -> bool:
    return torch.version.hip is not None


_is_hip = is_hip()


def dcu_alloc_extend_kernel(
    pre_lens_ptr: torch.Tensor,
    seq_lens_ptr: torch.Tensor,
    last_loc_ptr: torch.Tensor,
    free_page_ptr: torch.Tensor,
    out_indices: torch.Tensor,
    bs: int,
    bs_upper: int,
    page_size: int,
    max_num_extend_tokens: int,           
):
    torch.ops.sgl_kernel.dcu_alloc_extend_kernel(
        pre_lens_ptr,
        seq_lens_ptr,
        last_loc_ptr,
        free_page_ptr,
        out_indices,
        bs,
        bs_upper,
        page_size,
        max_num_extend_tokens,
    )

def dcu_alloc_decode_kernel(
    seq_lens_ptr: torch.Tensor,   
    last_loc_ptr: torch.Tensor,    
    free_page_ptr: torch.Tensor ,   
    out_indices: torch.Tensor , 
    bs: int,          
    bs_upper: int,              
    page_size: int,              
):
    torch.ops.sgl_kernel.dcu_alloc_decode_kernel(
        seq_lens_ptr,
        last_loc_ptr,
        free_page_ptr,
        out_indices,
        bs,
        bs_upper,
        page_size,
    )

def transfer_kv_per_layer(
    src_k: torch.Tensor,
    dst_k: torch.Tensor,
    src_v: torch.Tensor,
    dst_v: torch.Tensor,
    src_indices: torch.Tensor,
    dst_indices: torch.Tensor,
    item_size: int,
    block_quota: int = 2,
    num_warps_per_block: int = 16 if _is_hip else 32,
):
    torch.ops.sgl_kernel.transfer_kv_per_layer(
        src_k,
        dst_k,
        src_v,
        dst_v,
        src_indices,
        dst_indices,
        item_size,
        block_quota,
        num_warps_per_block,
    )


def transfer_kv_per_layer_pf_lf(
    src_k: torch.Tensor,
    dst_k: torch.Tensor,
    src_v: torch.Tensor,
    dst_v: torch.Tensor,
    src_indices: torch.Tensor,
    dst_indices: torch.Tensor,
    layer_id: int,
    item_size: int,
    src_layout_dim: int,
    block_quota: int = 2,
    num_warps_per_block: int = 16 if _is_hip else 32,
):
    torch.ops.sgl_kernel.transfer_kv_per_layer_pf_lf(
        src_k,
        dst_k,
        src_v,
        dst_v,
        src_indices,
        dst_indices,
        layer_id,
        item_size,
        src_layout_dim,
        block_quota,
        num_warps_per_block,
    )


def transfer_kv_all_layer(
    src_k_layers: torch.Tensor,
    dst_k_layers: torch.Tensor,
    src_v_layers: torch.Tensor,
    dst_v_layers: torch.Tensor,
    src_indices: torch.Tensor,
    dst_indices: torch.Tensor,
    item_size: int,
    num_layers: int,
    block_quota: int = 2,
    num_warps_per_block: int = 16 if _is_hip else 32,
):
    torch.ops.sgl_kernel.transfer_kv_all_layer(
        src_k_layers,
        dst_k_layers,
        src_v_layers,
        dst_v_layers,
        src_indices,
        dst_indices,
        item_size,
        num_layers,
        block_quota,
        num_warps_per_block,
    )


def transfer_kv_all_layer_lf_pf(
    src_k_layers: torch.Tensor,
    dst_k: torch.Tensor,
    src_v_layers: torch.Tensor,
    dst_v: torch.Tensor,
    src_indices: torch.Tensor,
    dst_indices: torch.Tensor,
    item_size: int,
    dst_layout_dim: int,
    num_layers: int,
    block_quota: int = 2,
    num_warps_per_block: int = 16 if _is_hip else 32,
):
    torch.ops.sgl_kernel.transfer_kv_all_layer_lf_pf(
        src_k_layers,
        dst_k,
        src_v_layers,
        dst_v,
        src_indices,
        dst_indices,
        item_size,
        dst_layout_dim,
        num_layers,
        block_quota,
        num_warps_per_block,
    )


def transfer_kv_direct(
    src_layers: List[torch.Tensor],
    dst_layers: List[torch.Tensor],
    src_indices: torch.Tensor,
    dst_indices: torch.Tensor,
    page_size: int,
):
    torch.ops.sgl_kernel.transfer_kv_direct(
        src_layers, dst_layers, src_indices, dst_indices, page_size
    )


def transfer_kv_per_layer_direct_pf_lf(
    src_ptrs: List[torch.Tensor],
    dst_ptrs: List[torch.Tensor],
    src_indices: torch.Tensor,
    dst_indices: torch.Tensor,
    layer_id: int,
    page_size: int,
):
    torch.ops.sgl_kernel.transfer_kv_per_layer_direct_pf_lf(
        src_ptrs, dst_ptrs, src_indices, dst_indices, layer_id, page_size
    )


def transfer_kv_all_layer_direct_lf_pf(
    src_ptrs: List[torch.Tensor],
    dst_ptrs: List[torch.Tensor],
    src_indices: torch.Tensor,
    dst_indices: torch.Tensor,
    page_size: int,
):
    torch.ops.sgl_kernel.transfer_kv_all_layer_direct_lf_pf(
        src_ptrs, dst_ptrs, src_indices, dst_indices, page_size
    )


def transfer_kv_per_layer_mla(
    src: torch.Tensor,
    dst: torch.Tensor,
    src_indices: torch.Tensor,
    dst_indices: torch.Tensor,
    item_size: int,
    block_quota: int = 2,
    num_warps_per_block: int = 16 if _is_hip else 32,
):
    torch.ops.sgl_kernel.transfer_kv_per_layer_mla(
        src,
        dst,
        src_indices,
        dst_indices,
        item_size,
        block_quota,
        num_warps_per_block,
    )


def transfer_kv_per_layer_mla_pf_lf(
    src: torch.Tensor,
    dst: torch.Tensor,
    src_indices: torch.Tensor,
    dst_indices: torch.Tensor,
    layer_id: int,
    item_size: int,
    src_layout_dim: int,
    block_quota: int = 2,
    num_warps_per_block: int = 16 if _is_hip else 32,
):
    torch.ops.sgl_kernel.transfer_kv_per_layer_mla_pf_lf(
        src,
        dst,
        src_indices,
        dst_indices,
        layer_id,
        item_size,
        src_layout_dim,
        block_quota,
        num_warps_per_block,
    )


def transfer_kv_all_layer_mla(
    src_layers: torch.Tensor,
    dst_layers: torch.Tensor,
    src_indices: torch.Tensor,
    dst_indices: torch.Tensor,
    item_size: int,
    num_layers: int,
    block_quota: int = 2,
    num_warps_per_block: int = 16 if _is_hip else 32,
):
    torch.ops.sgl_kernel.transfer_kv_all_layer_mla(
        src_layers,
        dst_layers,
        src_indices,
        dst_indices,
        item_size,
        num_layers,
        block_quota,
        num_warps_per_block,
    )


def transfer_kv_all_layer_mla_lf_pf(
    src_layers: torch.Tensor,
    dst: torch.Tensor,
    src_indices: torch.Tensor,
    dst_indices: torch.Tensor,
    item_size: int,
    dst_layout_dim: int,
    num_layers: int,
    block_quota: int = 2,
    num_warps_per_block: int = 16 if _is_hip else 32,
):
    torch.ops.sgl_kernel.transfer_kv_all_layer_mla_lf_pf(
        src_layers,
        dst,
        src_indices,
        dst_indices,
        item_size,
        dst_layout_dim,
        num_layers,
        block_quota,
        num_warps_per_block,
    )