"""
Based on:
Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023).
Punica: Multi-Tenant LoRA Serving.
https://arxiv.org/abs/2310.18547
"""

import torch
import triton
import triton.language as tl

from aphrodite.common.utils import direct_register_custom_op
from aphrodite.triton_utils import libentry


@libentry()
@triton.jit
def _sgmv_expand_slice_kernel(
    input_ptr,
    lora_ptr,
    out_ptr,
    N,
    K,
    b_seq_start_loc,
    seq_lens,
    lora_indices,
    xm_stride,
    xk_stride,  # 1
    l0_stride,  # hidden_size*max_rank
    lora_k_stride,
    lora_n_stride,
    cm_stride,
    cn_stride,
    slice_offset,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    EVEN_K: tl.constexpr,
    ADD_INPUTS: tl.constexpr,
    CAST_TYPE: tl.constexpr,
):
    """

    Similar to the 'sgmv_expand' operator, but with an added parameter
    'slice_offset'. The reason for not reusing the 'sgmv_expand' operator
    might be that in the future, we could implement a fusion operator to
    achieve the current functionality instead of having to call it multiple
    times.
    """
    pid = tl.program_id(axis=0)
    cur_batch = tl.program_id(axis=1)
    cta_n_num = tl.cdiv(N, BLOCK_N)
    pid_m = pid // cta_n_num
    pid_n = pid % cta_n_num
    M = tl.load(seq_lens + cur_batch)
    if pid_m * BLOCK_M > M:
        return
    lora_index = tl.load(lora_indices + cur_batch)
    if lora_index == -1:
        return
    cur_seq_start = tl.load(b_seq_start_loc + cur_batch)
    offset_m = tl.arange(0, BLOCK_M) + pid_m * BLOCK_M
    offset_n = tl.arange(0, BLOCK_N) + pid_n * BLOCK_N
    offset_k = tl.arange(0, BLOCK_K)
    ram = tl.max_contiguous(tl.multiple_of(offset_m % M, BLOCK_M), BLOCK_M)
    rbn = tl.max_contiguous(tl.multiple_of(offset_n % N, BLOCK_N), BLOCK_N)

    a_ptr = (input_ptr + cur_seq_start * xm_stride + ram[:, None] * xm_stride +
             offset_k[None, :] * xk_stride, )
    b_ptr = (lora_ptr + l0_stride * lora_index +
             offset_k[:, None] * lora_n_stride + rbn[None, :] * lora_k_stride)
    accumulator = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(tl.cdiv(K, BLOCK_K)):
        if EVEN_K:
            tiled_a = tl.load(a_ptr)
            tiled_b = tl.load(b_ptr)
        else:
            tiled_a = tl.load(a_ptr,
                              mask=offset_k[None, :] < K - k * BLOCK_K,
                              other=0)
            tiled_b = tl.load(b_ptr,
                              mask=offset_k[:, None] < K - k * BLOCK_K,
                              other=0)
        if CAST_TYPE:
            tiled_a = tiled_a.to(lora_ptr.dtype.element_ty)
        accumulator += tl.dot(
            tiled_a,
            tiled_b,
        )
        a_ptr += BLOCK_K * xk_stride
        b_ptr += BLOCK_K * lora_n_stride
    tiled_c = accumulator.to(lora_ptr.dtype.element_ty)
    offset_cm = cur_seq_start + tl.arange(0, BLOCK_M) + pid_m * BLOCK_M
    offset_cn = tl.arange(0, BLOCK_N) + pid_n * BLOCK_N + slice_offset
    c_ptr = (out_ptr + offset_cm[:, None] * cm_stride +
             offset_cn[None, :] * cn_stride)
    M = tl.load(seq_lens + cur_batch)
    c_mask = (offset_cm[:, None] < (cur_seq_start + M)) & (offset_cn[None, :] <
                                                           (slice_offset + N))
    if ADD_INPUTS:
        # explicitly pass in other=None to tell triton that masked values
        # can be uninitialized. This is OK because the later tl.store operation
        # uses the same mask, eliminating the risk of garbage values propagating
        tiled_out = tl.load(c_ptr, mask=c_mask, other=None)
        tiled_c += tiled_out
    tl.store(c_ptr, tiled_c, mask=c_mask)


@torch.inference_mode()
def _sgmv_expand_slice(
    inputs: torch.Tensor,
    lora_b_weights: torch.Tensor,
    output_tensor: torch.Tensor,
    b_seq_start_loc: torch.Tensor,
    seq_len_tensor: torch.Tensor,
    lora_indices_tensor: torch.Tensor,
    batches: int,
    max_seq_length: int,
    token_nums: int,
    slice_offset: int,
    slice_size: int,
    add_inputs: bool = False,
) -> None:
    """_summary_

    Args:
        inputs (torch.Tensor): input tensor
        lora_b_weights (torch.Tensor): lora'a weight
        output_tensor (torch.Tensor): output tensor
        b_seq_start_loc (torch.Tensor): (batch_size,). The cumulative
            sequence lengths of the sequences in the batch, used to index
            into sequence. E.g., if the sequence length is [4, 6], it is
            [0, 4, 10].
        seq_len_tensor (torch.Tensor): (batch_size,). Record the sequence
            length of the sequences in the batch
        lora_indices_tensor (torch.Tensor): (batch_size,). The LoRA index
            corresponding to each batch. An index of -1 means no lora should be
            applied.
        batches (int): batch size
        max_seq_length (int): The max sequence lengths of the sequences
            in the batch
        token_nums (int): The token numbers in the batch. Used to verify if the
            token numbers in the inputs matches the one in the metadata.
        slice_offset (int): output_tensor's offset
        slice_size (int): current output_tensor's size
        add_inputs (bool, optional): Defaults to False, adds the final lora
            results to the output.
    """

    assert inputs.dtype in [torch.float16, torch.bfloat16, torch.float32]
    assert lora_b_weights.dtype in [
        torch.float16,
        torch.bfloat16,
    ]
    assert inputs.size(0) == token_nums
    assert inputs.size(1) == lora_b_weights.size(-1)
    assert b_seq_start_loc.size(0) == batches
    assert lora_indices_tensor.size(0) == batches
    assert slice_size == lora_b_weights.size(-2)
    assert inputs.is_contiguous()
    assert output_tensor.is_contiguous()

    if lora_b_weights.ndim == 4:  # shape:(lora_num,1,size,rank)
        assert lora_b_weights.size(1) == 1
        lora_b_weights = lora_b_weights.squeeze(dim=1)
    else:
        assert lora_b_weights.ndim == 3  # shape:(lora_num,size,rank)

    assert lora_b_weights.is_contiguous()

    # TODO tuning this config
    N, K = lora_b_weights.shape[-2:]  # K= rank,N=hidden_size

    BLOCK_M = 32
    BLOCK_N = 32
    BLOCK_K = 16
    EVEN_K = K % BLOCK_K == 0
    ADD_INPUTS = add_inputs
    CAST_TYPE = False
    if inputs.dtype == torch.float32 and lora_b_weights.dtype in [
            torch.float16,
            torch.bfloat16,
    ]:
        CAST_TYPE = True
    grid = (
        triton.cdiv(max_seq_length, BLOCK_M) * triton.cdiv(N, BLOCK_N),
        batches,
    )
    _sgmv_expand_slice_kernel[grid](
        inputs,
        lora_b_weights,
        output_tensor,
        N,
        K,
        b_seq_start_loc,
        seq_len_tensor,
        lora_indices_tensor,
        inputs.stride(0),
        inputs.stride(1),
        lora_b_weights.stride(0),
        lora_b_weights.stride(1),
        lora_b_weights.stride(2),
        output_tensor.stride(0),
        output_tensor.stride(1),
        slice_offset,
        BLOCK_M,
        BLOCK_N,
        BLOCK_K,
        EVEN_K,
        ADD_INPUTS,
        CAST_TYPE,
    )
    return


def sgmv_expand_slice_fake(
    inputs: torch.Tensor,
    lora_b_weights: torch.Tensor,
    output_tensor: torch.Tensor,
    b_seq_start_loc: torch.Tensor,
    seq_len_tensor: torch.Tensor,
    lora_indices_tensor: torch.Tensor,
    batches: int,
    max_seq_length: int,
    token_nums: int,
    slice_offset: int,
    slice_size: int,
    add_inputs: bool = False,
) -> None:
    return


try:
    direct_register_custom_op(
        op_name="sgmv_expand_slice",
        op_func=_sgmv_expand_slice,
        mutates_args=["output_tensor"],
        fake_impl=sgmv_expand_slice_fake,
    )
    sgmv_expand_slice = torch.ops.aphrodite.sgmv_expand_slice

except AttributeError:
    sgmv_expand_slice = _sgmv_expand_slice
