# https://github.com/thu-ml/DiT-Extrapolation/blob/ultra-wan/sageattn/quant_per_block.py

import torch
import triton
import triton.language as tl

@triton.jit
def quant_per_block_int8_kernel(Input, Output, Scale, L,
                                stride_iz, stride_ih, stride_in,
                                stride_oz, stride_oh, stride_on,
                                stride_sz, stride_sh,
                                sm_scale,
                                C: tl.constexpr, BLK: tl.constexpr):
    off_blk = tl.program_id(0)
    off_h = tl.program_id(1)
    off_b = tl.program_id(2)

    offs_n = off_blk * BLK + tl.arange(0, BLK)
    offs_k = tl.arange(0, C)

    input_ptrs = Input + off_b * stride_iz + off_h * stride_ih + offs_n[:, None] * stride_in + offs_k[None, :]
    output_ptrs = Output + off_b * stride_oz + off_h * stride_oh + offs_n[:, None] * stride_on + offs_k[None, :]
    scale_ptrs = Scale + off_b * stride_sz + off_h * stride_sh + off_blk

    x = tl.load(input_ptrs, mask=offs_n[:, None] < L)
    x = x.to(tl.float32)
    x *= sm_scale
    scale = tl.max(tl.abs(x)) / 127.
    x_int8 = x / scale
    x_int8 += 0.5 * tl.where(x_int8 >= 0, 1, -1)
    x_int8 = x_int8.to(tl.int8)
    tl.store(output_ptrs, x_int8, mask=offs_n[:, None] < L)
    tl.store(scale_ptrs, scale)

def per_block_int8(q, k, BLKQ=128, BLKK=64, sm_scale=None, tensor_layout="HND"):
    q_int8 = torch.empty(q.shape, dtype=torch.int8, device=q.device)
    k_int8 = torch.empty(k.shape, dtype=torch.int8, device=k.device)

    if tensor_layout == "HND":
        b, h_qo, qo_len, head_dim = q.shape
        _, h_kv, kv_len, _ = k.shape

        stride_bz_q, stride_h_q, stride_seq_q = q.stride(0), q.stride(1), q.stride(2)
        stride_bz_qo, stride_h_qo, stride_seq_qo = q_int8.stride(0), q_int8.stride(1), q_int8.stride(2)
        stride_bz_k, stride_h_k, stride_seq_k = k.stride(0), k.stride(1), k.stride(2)
        stride_bz_ko, stride_h_ko, stride_seq_ko = k_int8.stride(0), k_int8.stride(1), k_int8.stride(2)
    # elif tensor_layout == "NHD":
    #     b, qo_len, h_qo, head_dim = q.shape
    #     _, kv_len, h_kv, _ = k.shape

    #     stride_bz_q, stride_h_q, stride_seq_q = q.stride(0), q.stride(2), q.stride(1)
    #     stride_bz_qo, stride_h_qo, stride_seq_qo = q_int8.stride(0), q_int8.stride(2), q_int8.stride(1)
    #     stride_bz_k, stride_h_k, stride_seq_k = k.stride(0), k.stride(2), k.stride(1)
    #     stride_bz_ko, stride_h_ko, stride_seq_ko = k_int8.stride(0), k_int8.stride(2), k_int8.stride(1)
    else:
        raise ValueError(f"Unknown tensor layout: {tensor_layout}")

    q_scale = torch.empty((b, h_qo, (qo_len + BLKQ - 1) // BLKQ, 1), device=q.device, dtype=torch.float32)
    k_scale = torch.empty((b, h_kv, (kv_len + BLKK - 1) // BLKK, 1), device=q.device, dtype=torch.float32)

    if sm_scale is None:
        sm_scale = head_dim**-0.5

    grid = ((qo_len + BLKQ - 1) // BLKQ, h_qo, b)
    quant_per_block_int8_kernel[grid](
        q, q_int8, q_scale, qo_len,
        stride_bz_q, stride_h_q, stride_seq_q,
        stride_bz_qo, stride_h_qo, stride_seq_qo,
        q_scale.stride(0), q_scale.stride(1),
        sm_scale=(sm_scale * 1.44269504),
        C=head_dim, BLK=BLKQ
    )

    grid = ((kv_len + BLKK - 1) // BLKK, h_kv, b)
    quant_per_block_int8_kernel[grid](
        k, k_int8, k_scale, kv_len,
        stride_bz_k, stride_h_k, stride_seq_k,
        stride_bz_ko, stride_h_ko, stride_seq_ko,
        k_scale.stride(0), k_scale.stride(1),
        sm_scale=1.0,
        C=head_dim, BLK=BLKK
    )

    return q_int8, q_scale, k_int8, k_scale