# SPDX-License-Identifier: MIT
 
import torch
import triton
import triton.language as tl


def ceil_div(a, b):
    return (a + b - 1) // b


@triton.jit
def _moe_align_block_size_stage1_kernel(
    topk_ids_ptr,
    tokens_cnts_ptr,
    num_experts: tl.constexpr,
    numel: tl.constexpr,
    tokens_per_thread: tl.constexpr,
):
    pid = tl.program_id(0)

    start_idx = pid * tokens_per_thread

    off_c = (pid + 1) * num_experts

    for i in range(tokens_per_thread):
        if start_idx + i < numel:
            idx = tl.load(topk_ids_ptr + start_idx + i)
            token_cnt = tl.load(tokens_cnts_ptr + off_c + idx)
            tl.store(tokens_cnts_ptr + off_c + idx, token_cnt + 1)


@triton.jit
def _moe_align_block_size_stage2_kernel(
    tokens_cnts_ptr,
    num_experts: tl.constexpr,
):
    pid = tl.program_id(0)

    last_cnt = 0
    for i in range(1, num_experts + 1):
        token_cnt = tl.load(tokens_cnts_ptr + i * num_experts + pid)
        last_cnt = last_cnt + token_cnt
        tl.store(tokens_cnts_ptr + i * num_experts + pid, last_cnt)


@triton.jit
def _moe_align_block_size_stage3_kernel(
    total_tokens_post_pad_ptr,
    tokens_cnts_ptr,
    cumsum_ptr,
    num_experts: tl.constexpr,
    block_size: tl.constexpr,
):
    last_cumsum = 0
    off_cnt = num_experts * num_experts
    for i in range(1, num_experts + 1):
        token_cnt = tl.load(tokens_cnts_ptr + off_cnt + i - 1)
        last_cumsum = last_cumsum + tl.cdiv(token_cnt, block_size) * block_size
        tl.store(cumsum_ptr + i, last_cumsum)
    tl.store(total_tokens_post_pad_ptr, last_cumsum)


@triton.jit
def _moe_align_block_size_stage4_kernel(
    topk_ids_ptr,
    sorted_token_ids_ptr,
    expert_ids_ptr,
    tokens_cnts_ptr,
    cumsum_ptr,
    num_experts: tl.constexpr,
    block_size: tl.constexpr,
    numel: tl.constexpr,
    tokens_per_thread: tl.constexpr,
):
    pid = tl.program_id(0)
    start_idx = tl.load(cumsum_ptr + pid)
    end_idx = tl.load(cumsum_ptr + pid + 1)

    for i in range(start_idx, end_idx, block_size):
        tl.store(expert_ids_ptr + i // block_size, pid)

    start_idx = pid * tokens_per_thread
    off_t = pid * num_experts

    for i in range(start_idx, tl.minimum(start_idx + tokens_per_thread, numel)):
        expert_id = tl.load(topk_ids_ptr + i)
        token_cnt = tl.load(tokens_cnts_ptr + off_t + expert_id)
        rank_post_pad = token_cnt + tl.load(cumsum_ptr + expert_id)
        tl.store(sorted_token_ids_ptr + rank_post_pad, i)
        tl.store(tokens_cnts_ptr + off_t + expert_id, token_cnt + 1)


def moe_align_block_size_triton(
    topk_ids: torch.Tensor,  # [num_tkns, num_experts]
    num_experts: int,
    block_size: int,
    sorted_token_ids: torch.Tensor,
    expert_ids: torch.Tensor,
    num_tokens_post_pad: torch.Tensor,
) -> None:
    numel = topk_ids.numel()
    grid = (num_experts,)
    tokens_cnts = torch.zeros(
        (num_experts + 1, num_experts), dtype=torch.int32, device=topk_ids.device
    )
    cumsum = torch.zeros((num_experts + 1,), dtype=torch.int32, device=topk_ids.device)
    tokens_per_thread = ceil_div(numel, num_experts)

    _moe_align_block_size_stage1_kernel[grid](
        topk_ids,
        tokens_cnts,
        num_experts,
        numel,
        tokens_per_thread,
    )

    _moe_align_block_size_stage2_kernel[grid](
        tokens_cnts,
        num_experts,
    )

    _moe_align_block_size_stage3_kernel[(1,)](
        num_tokens_post_pad,
        tokens_cnts,
        cumsum,
        num_experts,
        block_size,
    )

    _moe_align_block_size_stage4_kernel[grid](
        topk_ids,
        sorted_token_ids,
        expert_ids,
        tokens_cnts,
        cumsum,
        num_experts,
        block_size,
        numel,
        tokens_per_thread,
    )
