"""
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, math
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 _attn_fwd_inner(acc, l_i, m_i, q, q_scale, qo_len, kv_len,
                    K_ptrs, K_scale_ptr, V_ptrs, stride_kn, stride_vn, 
                    start_m, mask_ptrs, stride_maskn,
                    BLOCK_M: tl.constexpr, HEAD_DIM: tl.constexpr, BLOCK_N: tl.constexpr,  
                    STAGE: tl.constexpr, offs_m: tl.constexpr, offs_n: tl.constexpr,  
                    ):
    lo, hi = 0, kv_len
    for start_n in range(lo, hi, BLOCK_N):
        start_n = tl.multiple_of(start_n, BLOCK_N)
        mask_block = None
        skip = False
        if mask_ptrs is not None:
            if mask_ptrs.dtype.element_ty == tl.int1:
                mask_block = tl.load(mask_ptrs + start_n * stride_maskn, mask=(offs_m[:, None] < qo_len) & (offs_n[None, :] < kv_len - start_n), other=False)
                if tl.max(mask_block) == 0:
                    skip = True
            else:
                mask_block = tl.load(mask_ptrs + start_n * stride_maskn, mask=(offs_m[:, None] < qo_len) & (offs_n[None, :] < kv_len - start_n), other=-1.0e6)
        if not skip:
            k_mask = offs_n[None, :] < (kv_len - start_n)
            k = tl.load(K_ptrs, mask=k_mask)
            k_scale = tl.load(K_scale_ptr)

            qk = tl.dot(q, k).to(tl.float32) * (q_scale * k_scale)
            
            if mask_block is not None:
                if mask_block.dtype == tl.int1:
                    qk = qk + tl.where(mask_block, 0, -1.0e6)
                else:
                    qk = qk + mask_block
            else:
                qk += tl.where(k_mask, 0, -1.0e6)

            m_ij = tl.maximum(m_i, tl.max(qk, 1))
            qk = qk - m_ij[:, None]
            p = tl.math.exp2(qk)
            l_ij = tl.sum(p, 1)
            
            alpha = tl.math.exp2(m_i - m_ij)
            l_i = l_i * alpha + l_ij
            
            acc = acc * alpha[:, None]
            
            v = tl.load(V_ptrs, mask = offs_n[:, None] < (kv_len - start_n))
            p = p.to(tl.float16)
            
            acc += tl.dot(p, v, out_dtype=tl.float16)   
            m_i = m_ij
        K_ptrs += BLOCK_N * stride_kn
        K_scale_ptr += 1
        V_ptrs += BLOCK_N * stride_vn
    return acc, l_i, m_i

@triton.jit
def _attn_fwd(Q, K, V, Q_scale, K_scale, Out, mask, Lse, 
              stride_qz, stride_qh, stride_qn,
              stride_kz, stride_kh, stride_kn,  
              stride_vz, stride_vh, stride_vn,  
              stride_oz, stride_oh, stride_on, 
              stride_maskz, stride_maskh, stride_maskm, stride_maskn,
              qo_len, kv_len, H: tl.constexpr, num_kv_groups: tl.constexpr,
              HEAD_DIM: tl.constexpr,  
              BLOCK_M: tl.constexpr,  
              BLOCK_N: tl.constexpr,  
              STAGE: tl.constexpr,
              RETURN_LSE: tl.constexpr,
              ):
    tl.assume(stride_qz > 0)
    tl.assume(stride_qh > 0)
    tl.assume(stride_qn > 0)
    tl.assume(stride_kz > 0)
    tl.assume(stride_kh > 0)
    tl.assume(stride_kn > 0)
    tl.assume(stride_vz > 0)
    tl.assume(stride_vh > 0)
    tl.assume(stride_vn > 0)
    tl.assume(stride_oz > 0)
    tl.assume(stride_oh > 0)
    tl.assume(stride_on > 0)
    tl.assume(stride_maskz > 0)
    tl.assume(stride_maskh > 0)
    tl.assume(stride_maskm > 0)
    tl.assume(stride_maskn > 0)
    tl.assume(qo_len > 0)
    tl.assume(kv_len > 0)
    tl.assume(num_kv_groups > 0)

    start_m = tl.program_id(0)

    off_z = tl.program_id(2).to(tl.int64)
    off_h = tl.program_id(1).to(tl.int64)

    q_scale_offset = (off_z * H + off_h) * tl.cdiv(qo_len, BLOCK_M)
    k_scale_offset = (off_z * (H // num_kv_groups) + off_h // num_kv_groups) * tl.cdiv(kv_len, BLOCK_N)  
    
    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, HEAD_DIM)
    Q_ptrs = Q + (off_z * stride_qz + off_h * stride_qh) + offs_m[:, None] * stride_qn + offs_k[None, :]
    Q_scale_ptr = Q_scale + q_scale_offset + start_m
    K_ptrs = K + (off_z * stride_kz + (off_h // num_kv_groups) * stride_kh) + offs_n[None, :] * stride_kn + offs_k[:, None] 
    K_scale_ptr = K_scale + k_scale_offset
    V_ptrs = V + (off_z * stride_vz + (off_h // num_kv_groups) * stride_vh) + offs_n[:, None] * stride_vn + offs_k[None, :]
    O_block_ptr = Out + (off_z * stride_oz + off_h * stride_oh) + offs_m[:, None] * stride_on + offs_k[None, :]
    if mask is None:
        mask_ptrs = None
    else:
        mask_ptrs = mask + (off_z * stride_maskz + off_h * stride_maskh) + offs_m[:, None] * stride_maskm + offs_n[None, :] * stride_maskn

    m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
    l_i = tl.zeros([BLOCK_M], dtype=tl.float32) + 1.0
    acc = tl.zeros([BLOCK_M, HEAD_DIM], dtype=tl.float32)
    
    q = tl.load(Q_ptrs, mask = offs_m[:, None] < qo_len)
    q_scale = tl.load(Q_scale_ptr)
    acc, l_i, m_i = _attn_fwd_inner(acc, l_i, m_i, q, q_scale, qo_len, kv_len, K_ptrs, K_scale_ptr, V_ptrs, stride_kn, stride_vn,
                                    start_m, mask_ptrs, stride_maskn,
                                    BLOCK_M, HEAD_DIM, BLOCK_N,  
                                    4 - STAGE, offs_m, offs_n 
                                    )
    acc = acc / l_i[:, None]
    tl.store(O_block_ptr, acc.to(Out.type.element_ty), mask = (offs_m[:, None] < qo_len))

    if RETURN_LSE:
        lse_ptrs = Lse + (off_z * qo_len * H + off_h * qo_len) + offs_m
        l_i = tl.log2(l_i) + m_i
        tl.store(lse_ptrs, l_i, mask = (offs_m < qo_len))


@functools.lru_cache(maxsize=1024)
def _get_config(key, head_dim):
    if not hasattr(_get_config, "_config_dict"):
        try:
            config_path = f"{AITER_TRITON_CONFIGS_PATH}/sage_attention/_attn_fwd-device={get_gpu_label()}-dtype=f16_f16_f16_f32_f32_f16_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 = {
            "BLOCK_M": 128,
            "BLOCK_N": 64,
            "STAGE": 1,
            "waves_per_eu": 1,
            "matrix_instr_nonkdim": 16,
            "kpack": 2,
            "num_warps": 4 if head_dim == 64 else 8,
            "num_stages": 2
        }
        print(f"WARNING: optimal config {key} not found for _attn_fwd, use default config: {default_config}")
        return default_config
    else:
        return config[key]


def forward(q, k, v, q_scale, k_scale, tensor_layout="HND", attn_mask=None, output_dtype=torch.float16, return_lse=False, config=None):
    o = torch.empty(q.shape, dtype=output_dtype, device=q.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_k, stride_h_k, stride_seq_k = k.stride(0), k.stride(1), k.stride(2)
        stride_bz_v, stride_h_v, stride_seq_v = v.stride(0), v.stride(1), v.stride(2)
        stride_bz_o, stride_h_o, stride_seq_o = o.stride(0), o.stride(1), o.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_k, stride_h_k, stride_seq_k = k.stride(0), k.stride(2), k.stride(1)
        stride_bz_v, stride_h_v, stride_seq_v = v.stride(0), v.stride(2), v.stride(1)
        stride_bz_o, stride_h_o, stride_seq_o = o.stride(0), o.stride(2), o.stride(1)
    else:
        raise ValueError(f"tensor_layout {tensor_layout} not supported")

    if attn_mask is not None:
        stride_bz_mask, stride_h_mask, stride_m_mask, stride_n_mask = attn_mask.stride(0), attn_mask.stride(1), attn_mask.stride(2), attn_mask.stride(3)
    else:
        stride_bz_mask, stride_h_mask, stride_m_mask, stride_n_mask = 0, 0, 0, 0

    HEAD_DIM_K = head_dim
    num_kv_groups = h_qo // h_kv

    if return_lse:
        lse = torch.empty([b, h_qo, qo_len], dtype=torch.float32, device=q.device)
    else:
        lse = torch.empty([0], dtype=torch.float32, device='cpu')

    grid = lambda META: (triton.cdiv(qo_len, META['BLOCK_M']), h_qo, b)

    if not config:
        key = str((qo_len, kv_len, h_qo, num_kv_groups))
        config = _get_config(key, head_dim)
        assert config is not None, "ERROR: optimal config not found"

    _attn_fwd[grid](
        q, k, v, q_scale, k_scale, o, attn_mask, lse,
        stride_bz_q, stride_h_q, stride_seq_q, 
        stride_bz_k, stride_h_k, stride_seq_k,  
        stride_bz_v, stride_h_v, stride_seq_v,  
        stride_bz_o, stride_h_o, stride_seq_o,
        stride_bz_mask, stride_h_mask, stride_m_mask, stride_n_mask,
        qo_len, kv_len,
        h_qo, num_kv_groups,
        HEAD_DIM=HEAD_DIM_K,
        RETURN_LSE=return_lse,
        **config
    )

    return o, lse
