# SPDX-License-Identifier: MIT
 
from typing import Optional
import functools
import json
import os
import torch
import triton
import triton.language as tl
from aiter.ops.triton.utils.pid_preprocessing import pid_grid, remap_xcd
import aiter.ops.triton.utils.arch_info as arch_info
from aiter.ops.triton.utils.core import AITER_TRITON_CONFIGS_PATH


global _USE_GEMM_SPLITK_BF16
_USE_GEMM_SPLITK_BF16 = False


def set_use_gemm_splitk_bf16(value: bool):
    global _USE_GEMM_SPLITK_BF16
    _USE_GEMM_SPLITK_BF16 = value


@triton.heuristics(
    {
        "EVEN_K": lambda args: (args["K"] % (args["BLOCK_SIZE_K"] // 2) == 0)
        and (args["SPLITK_BLOCK_SIZE"] % args["BLOCK_SIZE_K"] == 0)
        and (args["K"] % (args["SPLITK_BLOCK_SIZE"] // 2) == 0),
        "GRID_MN": lambda args: triton.cdiv(args["M"], args["BLOCK_SIZE_M"])
        * triton.cdiv(args["N"], args["BLOCK_SIZE_N"]),
    }
)
@triton.jit
def _batched_gemm_afp4_wfp4_kernel(
    a_ptr,
    b_ptr,
    c_ptr,
    a_scales_ptr,
    b_scales_ptr,
    M,
    N,
    K,
    stride_in_ab,
    stride_in_am,
    stride_in_ak,
    stride_in_bb,
    stride_in_bk,
    stride_in_bn,
    stride_in_cb,
    stride_in_ck,
    stride_in_cm,
    stride_in_cn,
    stride_in_asb,
    stride_in_asm,
    stride_in_ask,
    stride_in_bsb,
    stride_in_bsn,
    stride_in_bsk,
    # Meta-parameters
    BLOCK_SIZE_M: tl.constexpr,
    BLOCK_SIZE_N: tl.constexpr,
    BLOCK_SIZE_K: tl.constexpr,
    GROUP_SIZE_M: tl.constexpr,
    NUM_KSPLIT: tl.constexpr,
    SPLITK_BLOCK_SIZE: tl.constexpr,
    EVEN_K: tl.constexpr,
    GRID_MN: tl.constexpr,
    cache_modifier: tl.constexpr,
):
    """Kernel for computing the matmul C = A x B.
    A and B inputs are in the microscale fp4 (mxfp4) format.
    A_scales and B_scales are in e8m0 format.
    A has shape (M, K), B has shape (K, N) and C has shape (M, N)
    """

    tl.assume(stride_in_ab > 0)
    tl.assume(stride_in_am > 0)
    tl.assume(stride_in_ak > 0)
    tl.assume(stride_in_bb > 0)
    tl.assume(stride_in_bk > 0)
    tl.assume(stride_in_bn > 0)
    tl.assume(stride_in_cb > 0)
    tl.assume(stride_in_cm > 0)
    tl.assume(stride_in_cn > 0)
    tl.assume(stride_in_asb > 0)
    tl.assume(stride_in_asm > 0)
    tl.assume(stride_in_ask > 0)
    tl.assume(stride_in_bsb > 0)
    tl.assume(stride_in_bsk > 0)
    tl.assume(stride_in_bsn > 0)

    # -----------------------------------------------------------
    # Map program ids `pid` to the block of C it should compute.
    # This is done in a grouped ordering to promote L2 data reuse.
    pid_batch = tl.program_id(axis=0)
    pid_unified = tl.program_id(axis=1)
    pid_k = pid_unified % NUM_KSPLIT
    pid = pid_unified // NUM_KSPLIT
    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)

    # Cast batch id and batch dimension strides to int64 to avoid int32 overflow during offset calculation
    # Note: If you're attempting to cast strides to int64 to prevent integer overflow, use `tl.cast` instead of `.to()`.
    # See https://github.com/ROCm/aiter/pull/597 for rationale
    # stride_ab = tl.cast(stride_ab, tl.int64)
    # stride_bb = tl.cast(stride_bb, tl.int64)
    # stride_cb = tl.cast(stride_cb, tl.int64)
    # pid_batch = tl.cast(pid_batch, tl.int64)

    stride_ab = tl.cast(stride_in_ab, tl.int64)
    stride_am = tl.cast(stride_in_am, tl.int64)
    stride_ak = tl.cast(stride_in_ak, tl.int64)
    stride_bb = tl.cast(stride_in_bb, tl.int64)
    stride_bk = tl.cast(stride_in_bk, tl.int64)
    stride_bn = tl.cast(stride_in_bn, tl.int64)
    stride_cb = tl.cast(stride_in_cb, tl.int64)
    stride_ck = tl.cast(stride_in_ck, tl.int64)
    stride_cm = tl.cast(stride_in_cm, tl.int64)
    stride_cn = tl.cast(stride_in_cn, tl.int64)
    stride_asb = tl.cast(stride_in_asb, tl.int64)
    stride_asm = tl.cast(stride_in_asm, tl.int64)
    stride_ask = tl.cast(stride_in_ask, tl.int64)
    stride_bsb = tl.cast(stride_in_bsb, tl.int64)
    stride_bsk = tl.cast(stride_in_bsk, tl.int64)
    stride_bsn = tl.cast(stride_in_bsn, tl.int64)

    if NUM_KSPLIT == 1:
        remap_xcd(pid, GRID_MN)

        pid_m, pid_n = pid_grid(pid, num_pid_m, num_pid_n, GROUP_SIZE_M=GROUP_SIZE_M)
    else:
        pid_m = pid // num_pid_n
        pid_n = pid % num_pid_n

    tl.assume(pid_batch >= 0)
    tl.assume(pid_m >= 0)
    tl.assume(pid_n >= 0)
    # We assume 32 elements along K share the same scale.
    SCALE_GROUP_SIZE: tl.constexpr = 32

    if (pid_k * SPLITK_BLOCK_SIZE // 2) < K:

        num_k_iter = tl.cdiv(SPLITK_BLOCK_SIZE // 2, BLOCK_SIZE_K // 2)

        # Create pointers for first block of A and B input matrices
        # The BLOCK sizes are of the elements and in fp4 we pack 2 per uint8 container.
        offs_k = tl.arange(0, BLOCK_SIZE_K // 2)
        offs_k_split = pid_k * (SPLITK_BLOCK_SIZE // 2) + offs_k
        offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
        offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
        a_ptrs = a_ptr + (
            pid_batch * stride_ab
            + offs_am[:, None] * stride_am
            + offs_k_split[None, :] * stride_ak
        )
        b_ptrs = b_ptr + (
            pid_batch * stride_bb
            + offs_k_split[:, None] * stride_bk
            + offs_bn[None, :] * stride_bn
        )
        # Create pointers for the first block of A and B scales
        offs_ks = (pid_k * (SPLITK_BLOCK_SIZE // SCALE_GROUP_SIZE)) + tl.arange(
            0, BLOCK_SIZE_K // SCALE_GROUP_SIZE
        )
        a_scale_ptrs = (
            a_scales_ptr
            + pid_batch * stride_asb
            + offs_am[:, None] * stride_asm
            + offs_ks[None, :] * stride_ask
        )
        # B scales are N x K even though B operand is K x N.
        b_scale_ptrs = (
            b_scales_ptr
            + pid_batch * stride_bsb
            + offs_bn[:, None] * stride_bsn
            + offs_ks[None, :] * stride_bsk
        )

        accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)

        for k in range(pid_k * num_k_iter, (pid_k + 1) * num_k_iter):
            a_scales = tl.load(a_scale_ptrs)
            b_scales = tl.load(b_scale_ptrs)
            # a_scales = tl.full((BLOCK_SIZE_M, BLOCK_SIZE_K//SCALE_GROUP_SIZE), 127, dtype=tl.uint8)
            # b_scales = tl.full((BLOCK_SIZE_N, BLOCK_SIZE_K//SCALE_GROUP_SIZE), 127, dtype=tl.uint8)
            # Load the next block of A and B, generate a mask by checking the K dimension.
            # If it is out of bounds, set it to 0.
            if EVEN_K:
                a = tl.load(a_ptrs)
                b = tl.load(b_ptrs, cache_modifier=cache_modifier)
            else:
                a = tl.load(
                    a_ptrs, mask=offs_k[None, :] < K - k * (BLOCK_SIZE_K // 2), other=0
                )
                b = tl.load(
                    b_ptrs, mask=offs_k[:, None] < K - k * (BLOCK_SIZE_K // 2), other=0
                )

            accumulator += tl.dot_scaled(a, a_scales, "e2m1", b, b_scales, "e2m1")

            # Advance the ptrs to the next K block.
            a_ptrs += (BLOCK_SIZE_K // 2) * stride_ak
            b_ptrs += (BLOCK_SIZE_K // 2) * stride_bk
            a_scale_ptrs += (BLOCK_SIZE_K // SCALE_GROUP_SIZE) * stride_ask
            b_scale_ptrs += (BLOCK_SIZE_K // SCALE_GROUP_SIZE) * stride_bsk

        c = accumulator.to(c_ptr.type.element_ty)

        # Write back the block of the output matrix C with masks.
        offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M).to(tl.int64)
        offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N).to(tl.int64)
        c_ptrs = (
            c_ptr
            + pid_batch * stride_cb
            + stride_cm * offs_cm[:, None]
            + stride_cn * offs_cn[None, :]
            + pid_k * stride_ck
        )
        c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
        tl.store(c_ptrs, c, mask=c_mask)


@triton.jit
def _batched_gemm_afp4_wfp4_reduce_kernel(
    c_in_ptr,
    c_out_ptr,
    M,
    N,
    stride_c_in_b,
    stride_c_in_k,
    stride_c_in_m,
    stride_c_in_n,
    stride_c_out_b,
    stride_c_out_m,
    stride_c_out_n,
    BLOCK_SIZE_M: tl.constexpr,
    BLOCK_SIZE_N: tl.constexpr,
    ACTUAL_KSPLIT: tl.constexpr,
    MAX_KSPLIT: tl.constexpr,
):
    pid_batch = tl.program_id(axis=0)
    pid_m = tl.program_id(axis=1)
    pid_n = tl.program_id(axis=2)

    offs_m = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
    offs_n = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
    offs_k = tl.arange(0, MAX_KSPLIT)
    c_in_ptrs = (
        c_in_ptr
        + pid_batch * stride_c_in_b
        + (offs_k[:, None, None] * stride_c_in_k)
        + (offs_m[None, :, None] * stride_c_in_m)
        + (offs_n[None, None, :] * stride_c_in_n)
    )

    if ACTUAL_KSPLIT == MAX_KSPLIT:
        c = tl.load(c_in_ptrs)
    else:
        c = tl.load(c_in_ptrs, mask=offs_k[:, None, None] < ACTUAL_KSPLIT)
    c = tl.sum(c, axis=0)

    c = c.to(c_out_ptr.type.element_ty)

    c_out_ptrs = (
        c_out_ptr
        + pid_batch * stride_c_out_b
        + (offs_m[:, None] * stride_c_out_m)
        + (offs_n[None, :] * stride_c_out_n)
    )

    tl.store(c_out_ptrs, c)


def get_splitk(K: int, BLOCK_SIZE_K: int, NUM_KSPLIT: int):
    # heuristics for make "EVEN_K == True" as much as possible
    NUM_KSPLIT_STEP = 2
    BLOCK_SIZE_K_STEP = 2
    SPLITK_BLOCK_SIZE = (
        triton.cdiv((2 * triton.cdiv(K, NUM_KSPLIT)), BLOCK_SIZE_K) * BLOCK_SIZE_K
    )
    while NUM_KSPLIT > 1 and BLOCK_SIZE_K > 16:
        # print(K, SPLITK_BLOCK_SIZE, BLOCK_SIZE_K, NUM_KSPLIT)
        # print(K % (SPLITK_BLOCK_SIZE // 2) == 0, SPLITK_BLOCK_SIZE % BLOCK_SIZE_K == 0, K % (BLOCK_SIZE_K // 2) == 0)

        if (
            K % (SPLITK_BLOCK_SIZE // 2) == 0
            and SPLITK_BLOCK_SIZE % BLOCK_SIZE_K == 0
            and K % (BLOCK_SIZE_K // 2) == 0
        ):
            break
        elif K % (SPLITK_BLOCK_SIZE // 2) != 0 and NUM_KSPLIT > 1:
            NUM_KSPLIT = NUM_KSPLIT // NUM_KSPLIT_STEP
        elif SPLITK_BLOCK_SIZE % BLOCK_SIZE_K != 0:
            if NUM_KSPLIT > 1:
                NUM_KSPLIT = NUM_KSPLIT // NUM_KSPLIT_STEP
            elif BLOCK_SIZE_K > 16:
                BLOCK_SIZE_K = BLOCK_SIZE_K // BLOCK_SIZE_K_STEP
        elif K % (BLOCK_SIZE_K // 2) != 0 and BLOCK_SIZE_K > 16:
            BLOCK_SIZE_K = BLOCK_SIZE_K // BLOCK_SIZE_K_STEP
        else:
            break

        SPLITK_BLOCK_SIZE = (
            triton.cdiv((2 * triton.cdiv(K, NUM_KSPLIT)), BLOCK_SIZE_K) * BLOCK_SIZE_K
        )

    return SPLITK_BLOCK_SIZE, BLOCK_SIZE_K, NUM_KSPLIT


@functools.lru_cache(maxsize=1024)
def _get_config(
    M: int,
    N: int,
    K: int,
):
    if not hasattr(_get_config, "_config_dict"):
        dev = arch_info.get_device()
        _get_config._config_dict = {}
        fpath = f"{AITER_TRITON_CONFIGS_PATH}/gemm/{dev}-BATCHED_GEMM-AFP4WFP4.json"
        with open(fpath, "r") as file:
            config = json.load(file)
        _get_config._config_dict["default"] = config

    key = f"{N}_{K}"
    if key not in _get_config._config_dict.keys():
        dev = arch_info.get_device()
        fpath = f"{AITER_TRITON_CONFIGS_PATH}/gemm/{dev}-BATCHED_GEMM-AFP4WFP4-N={N}-K={2*K}.json"
        if os.path.exists(fpath):
            with open(fpath, "r") as file:
                config = json.load(file)
                _get_config._config_dict[key] = config
        else:
            key = "default"  # fall back to default config
    if M < 32:
        config = _get_config._config_dict[key]["small"]
    elif M <= 128:
        BLK_M = triton.next_power_of_2(M)
        if BLK_M == 32:
            config = _get_config._config_dict[key]["medium_M32"]
        elif BLK_M == 64:
            config = _get_config._config_dict[key]["medium_M64"]
        elif BLK_M == 128:
            config = _get_config._config_dict[key]["medium_M128"]
    elif M <= 256:
        config = _get_config._config_dict[key]["large"]
    else:
        config = _get_config._config_dict[key]["xlarge"]

    config = config.copy()  # Avoid modifying the original config

    if config["NUM_KSPLIT"] > 1:
        SPLITK_BLOCK_SIZE, BLOCK_SIZE_K, NUM_KSPLIT = get_splitk(
            K, config["BLOCK_SIZE_K"], config["NUM_KSPLIT"]
        )

        config["SPLITK_BLOCK_SIZE"] = SPLITK_BLOCK_SIZE
        config["BLOCK_SIZE_K"] = BLOCK_SIZE_K
        config["NUM_KSPLIT"] = NUM_KSPLIT
    else:
        config["SPLITK_BLOCK_SIZE"] = 2 * K

    if config["BLOCK_SIZE_K"] >= 2 * K:
        config["BLOCK_SIZE_K"] = triton.next_power_of_2(2 * K)
        config["SPLITK_BLOCK_SIZE"] = 2 * K

    return config


def batched_gemm_afp4wfp4(
    x,
    w,
    x_scales,
    w_scales,
    dtype: Optional[float] = torch.bfloat16,
    y: Optional[torch.Tensor] = None,
    config: Optional[dict] = None,
):
    """
    Computes the matmul Y = X x W
    X and W are e2m1 fp4 tensors.
    x_scales and w_scales are e8m0 tensors.
    Every 32 elements in the K dimension share one e8m0 scale.


    Key parameters:
    - X: Matrix X with shape (B, M, K).
    - W: Matrix W with shape (B, N, K).
    - X_scales: Matrix with shape (B, M, K // 32)
    - W_scales: Matrix with shape (B, N, K // 32)

    Returns:
    - Y: The output matrix with shape (B, M, N).
    """

    assert arch_info.is_fp4_avail(), "MXFP4 is not available on your device"

    w = w.transpose(1, 2)
    Bx, M, K = x.shape
    Bw, K, N = w.shape
    By, _, _ = y.shape
    assert Bx == Bw == By
    Batch = Bx

    if config is None:
        config = _get_config(M, N, K)

    if config["NUM_KSPLIT"] > 1:
        if _USE_GEMM_SPLITK_BF16:
            y_pp = torch.empty(
                (Batch, config["NUM_KSPLIT"], M, N), dtype=y.dtype, device=y.device
            )
        else:
            y_pp = torch.empty(
                (Batch, config["NUM_KSPLIT"], M, N),
                dtype=torch.float32,
                device=y.device,
            )
    else:
        y_pp = None

    grid = lambda META: (  # noqa: E731
        Batch,
        (
            META["NUM_KSPLIT"]
            * triton.cdiv(M, META["BLOCK_SIZE_M"])
            * triton.cdiv(N, META["BLOCK_SIZE_N"])
        ),
    )
    _batched_gemm_afp4_wfp4_kernel[grid](
        x,
        w,
        y if config["NUM_KSPLIT"] == 1 else y_pp,
        x_scales,
        w_scales,
        M,
        N,
        K,
        x.stride(0),
        x.stride(1),
        x.stride(2),
        w.stride(0),
        w.stride(1),
        w.stride(2),
        y.stride(0) if config["NUM_KSPLIT"] == 1 else y_pp.stride(0),
        0 if config["NUM_KSPLIT"] == 1 else y_pp.stride(1),
        y.stride(1) if config["NUM_KSPLIT"] == 1 else y_pp.stride(2),
        y.stride(2) if config["NUM_KSPLIT"] == 1 else y_pp.stride(3),
        x_scales.stride(0),
        x_scales.stride(1),
        x_scales.stride(2),
        w_scales.stride(0),
        w_scales.stride(1),
        w_scales.stride(2),
        **config,
    )

    if config["NUM_KSPLIT"] > 1:
        REDUCE_BLOCK_SIZE_M = 16
        # TODO: Need to debug - REDUCE_BLOCK_SIZE_N=128 with fp32 partials fails
        # NOTE: REDUCE_BLOCK_SIZE_N=16 gives best perf with fp32 partials and
        # REDUCE_BLOCK_SIZE_N=128 gives best perf with bf16 partials
        REDUCE_BLOCK_SIZE_N = 128 if _USE_GEMM_SPLITK_BF16 else 64
        ACTUAL_KSPLIT = triton.cdiv(K, (config["SPLITK_BLOCK_SIZE"] // 2))

        grid_reduce = (
            Batch,
            triton.cdiv(M, REDUCE_BLOCK_SIZE_M),
            triton.cdiv(N, REDUCE_BLOCK_SIZE_N),
        )
        _batched_gemm_afp4_wfp4_reduce_kernel[grid_reduce](
            y_pp,
            y,
            M,
            N,
            y_pp.stride(0),
            y_pp.stride(1),
            y_pp.stride(2),
            y_pp.stride(3),
            y.stride(0),
            y.stride(1),
            y.stride(2),
            REDUCE_BLOCK_SIZE_M,
            REDUCE_BLOCK_SIZE_N,
            ACTUAL_KSPLIT,
            config["NUM_KSPLIT"],
        )
