vLLM/vllm/lora/ops/utils.py

import functools
from typing import Dict, List, Tuple

import torch


@functools.lru_cache
def _get_op_configs(op_type: str, batch: int, hidden_size: int):
    # TODO: add optimal configurations
    return None


def _check_divisibility(hidden_size: int):
    # The bgmv_expand kernel requires that the hidden_size be divisible by
    # the number below.
    divisibility = [2, 4, 8, 16, 32, 64]
    divisibility.sort(reverse=True)
    for div in divisibility:
        if hidden_size % div == 0:
            return div
    # hidden_size is an odd number
    return 1


def _get_default_config(op_type: str, batch: int, hidden_size: int):
    if op_type == "expand":
        return {
            "BLOCK_N": 256,
            "SPLIT_N": _check_divisibility(hidden_size),
            "num_warps": 8
        }
    else:
        return {"BLOCK_K": 256, "SPLIT_K": 64, "num_warps": 8}


def get_lora_op_configs(op_type: str, batch: int,
                        hidden_size: int) -> Dict[str, int]:
    """Inspired by `fused_moe_kernel`
    The return value will be a dictionary mapping an irregular grid of batch 
    sizes and hidden_size to configurations of the bgmv-related kernel. 
    NOTE: It currently only supports the default configuration. We plan to 
    generate optimal configurations for different hardware in the future using 
    scripts similar to `benchmark_moe.py`.
    """
    config = _get_op_configs(op_type, batch, hidden_size)
    if not config:
        config = _get_default_config(op_type, batch, hidden_size)
    return config


_LORA_A_PTR_DICT: Dict[Tuple[int, ...], Tuple[torch.tensor, ...]] = {}
_LORA_B_PTR_DICT: Dict[Tuple[int, ...], Tuple[torch.tensor, ...]] = {}


def _get_lora_a_ptr(lora_a_weights: List[torch.Tensor], device: str):
    """
    `_LORA_A_PTR_DICT` collects the required information during `profile_run`, 
    After this, it remains constant and subsequent usage is through LUT.
    Refer to: 
    https://github.com/triton-lang/triton/blob/release/3.1.x/python/tutorials/08-grouped-gemm.py
    """
    key = tuple(lora_weight.data_ptr() for lora_weight in lora_a_weights)

    if values := _LORA_A_PTR_DICT.get(key):
        return values

    lora_strides_d0 = []
    lora_strides_d1 = []
    lora_strides_d2 = []
    tensor_ptrs = []
    for lora_a_weight in lora_a_weights:
        if lora_a_weight.ndim == 4:  # shape:(lora_num,1,size,rank)
            assert lora_a_weight.size(1) == 1
            lora_a_weight = lora_a_weight.squeeze(dim=1)
        else:
            assert lora_a_weight.ndim == 3  # shape:(lora_num,size,rank)
        assert lora_a_weight.is_contiguous()
        tensor_ptrs.append(lora_a_weight.data_ptr())
        lora_strides_d0.append(lora_a_weight.stride(0))
        lora_strides_d1.append(lora_a_weight.stride(1))
        lora_strides_d2.append(lora_a_weight.stride(2))
    if len(lora_a_weights) > 1:
        lora_ptr_tensor = torch.tensor(tensor_ptrs, device=device)
    else:
        lora_ptr_tensor = lora_a_weights[0]

    if (len(set(lora_strides_d0)) > 1 or len(set(lora_strides_d1)) > 1
            or len(set(lora_strides_d2)) > 1):
        raise ValueError("All LoRA weights must have the same stride.")

    _LORA_A_PTR_DICT[key] = (
        lora_ptr_tensor,
        lora_strides_d0[0],
        lora_strides_d1[0],
        lora_strides_d2[0],
    )
    return _LORA_A_PTR_DICT.get(key)


def _get_lora_b_ptr(lora_weights: List[torch.Tensor], offset_start: int,
                    device: str):
    """ 
     `_LORA_B_PTR_DICT` collects the required information during `profile_run`, 
    After this, it remains constant and subsequent usage is through LUT.
    Refer to: 
    https://github.com/triton-lang/triton/blob/release/3.1.x/python/tutorials/08-grouped-gemm.py

    """

    key = tuple(lora_weight.data_ptr() for lora_weight in lora_weights)
    if values := _LORA_B_PTR_DICT.get(key):
        return values
    slice_offset_lst = []
    tensor_ptrs = []
    lora_strides_d0 = []
    lora_strides_d1 = []
    lora_strides_d2 = []
    hidden_sizes = []
    slice_offset = offset_start
    for lora_b_weight in lora_weights:
        if lora_b_weight.ndim == 4:  # shape:(lora_num,1,size,rank)
            assert lora_b_weight.size(1) == 1
            lora_b_weight = lora_b_weight.squeeze(dim=1)
        else:
            assert lora_b_weight.ndim == 3  # shape:(lora_num,size,rank)
        assert lora_b_weight.is_contiguous()
        tensor_ptrs.append(lora_b_weight.data_ptr())
        lora_strides_d0.append(lora_b_weight.stride(0))
        lora_strides_d1.append(lora_b_weight.stride(1))
        lora_strides_d2.append(lora_b_weight.stride(2))
        slice_offset_lst.append(slice_offset)
        slice_offset += lora_b_weight.size(1)
        hidden_sizes.append(lora_b_weight.size(1))

    if len(lora_weights) > 1:
        # note these are device tensors
        lora_ptr_tensor = torch.tensor(tensor_ptrs, device=device)
        slice_start_tensor = torch.tensor(slice_offset_lst, device=device)
    else:
        slice_start_tensor = slice_offset_lst[0]
        lora_ptr_tensor = lora_b_weight[0]

    # If each lora has the same stride, there's no need to use a
    # tensor for storage.
    if (len(set(lora_strides_d0)) == 1 and len(set(lora_strides_d1)) == 1 and
            len(set(lora_strides_d2)) == 1) and len(set(hidden_sizes)) == 1:
        lora_strides_d0_tensor = lora_strides_d0[0]
        lora_strides_d1_tensor = lora_strides_d1[0]
        lora_strides_d2_tensor = lora_strides_d2[0]
        hidden_sizes_tensor = hidden_sizes[0]
        same_stride = True

    else:
        lora_strides_d0_tensor = torch.tensor(lora_strides_d0, device=device)
        lora_strides_d1_tensor = torch.tensor(lora_strides_d1, device=device)
        lora_strides_d2_tensor = torch.tensor(lora_strides_d2, device=device)
        hidden_sizes_tensor = torch.tensor(hidden_sizes, device=device)
        same_stride = False
    # MAX_N is the maximum hidden size among all the lora_b weights
    MAX_N = max(hidden_sizes)
    _LORA_B_PTR_DICT[key] = (slice_start_tensor, lora_ptr_tensor,
                             lora_strides_d0_tensor, lora_strides_d1_tensor,
                             lora_strides_d2_tensor, hidden_sizes_tensor,
                             same_stride, MAX_N)
    return _LORA_B_PTR_DICT.get(key)