"""
Copyright (c) 2024 by SageAttention team.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

import json
import torch
import triton
import triton.language as tl
from triton.utils.hcutuner import get_gpu_label
from aiter.ops.triton.utils.core import AITER_TRITON_CONFIGS_PATH
import functools


@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):
    tl.assume(L > 0)
    tl.assume(stride_iz > 0)
    tl.assume(stride_ih > 0)
    tl.assume(stride_in > 0)
    tl.assume(stride_oz > 0)
    tl.assume(stride_oh > 0)
    tl.assume(stride_on > 0)
    tl.assume(stride_sz > 0)
    tl.assume(stride_sh > 0)

    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)


@functools.lru_cache(maxsize=1024)
def _get_config(key, blk):
    if not hasattr(_get_config, "_config_dict"):
        try:
            config_path = f"{AITER_TRITON_CONFIGS_PATH}/sage_attention/quant_per_block_int8_kernel-device={get_gpu_label()}-dtype=f16_i8_f32.json"
            print(f"config_path={config_path}")
            with open(config_path, "r") as file:
                config = json.load(file)
        except Exception as e:
            print(e)
            config = {'config': {}}
        _get_config._config_dict = config

    config = _get_config._config_dict["config"]
    if key not in config:
        default_config = {
            "num_warps": 4,
            "num_stages": 2
        }
        print(f"WARNING: optimal config {key} not found for quant_per_block_int8_kernel, use default config: {default_config}")
        return default_config
    else:
        return config[key]


def per_block_int8(q, k, km=None, 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 km is not None:
        k = k - km

    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}")

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

    grid = ((qo_len + BLKQ - 1) // BLKQ, h_qo, b)

    keys = str((qo_len, head_dim, BLKQ))
    config = _get_config(keys, BLKQ)
    assert config is not None, "ERROR: optimal config not found"

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

    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,
        **config
    )

    grid = ((kv_len + BLKK - 1) // BLKK, h_kv, b)

    keys = str((kv_len, head_dim, BLKK))
    config = _get_config(keys, BLKK)
    assert config is not None, "ERROR: optimal config not found"

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

    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,
        **config
    )

    return q_int8, q_scale, k_int8, k_scale