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


@triton.heuristics(
    {
        "EVEN_K": lambda args: args["K"] % args["BLOCK_SIZE_K"] == 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_a8w8_kernel(
    # Pointers to matrices
    a_ptr,
    b_ptr,
    c_ptr,
    a_scale_ptr,
    b_scale_ptr,
    bias_ptr,
    # Matrix dimensions
    M,
    N,
    K,
    # The stride variables represent how much to increase the ptr by when
    # moving by 1 element in a particular dimension. E.g. `stride_am` is
    # how much to increase `a_ptr` by to get the element one row down
    # (A has M rows).
    stride_ab,
    stride_am,
    stride_ak,
    stride_bb,
    stride_bk,
    stride_bn,
    stride_cb,
    stride_cm,
    stride_cn,
    stride_ascaleb,
    stride_bscaleb,
    stride_biasb,
    # Meta-parameters
    HAS_BIAS: tl.constexpr,
    BLOCK_SIZE_M: tl.constexpr,
    BLOCK_SIZE_N: tl.constexpr,
    BLOCK_SIZE_K: tl.constexpr,
    GROUP_SIZE_M: tl.constexpr,
    EVEN_K: tl.constexpr,
    GRID_MN: tl.constexpr,
):
    """
    Note: this is Triton jited function and not meant to be called directly. Call batched_gemm_a8w8 function
    below

    Computes the matmul C[i] = A[i] x B[i] and applies a conversion scale for every i in a given batch.
    Optionally, adds a bias to each result.

    The conversion scale for each matmul is received in the form of two 1D tensors that are multiplied to form a
    2D one before being applied.

    Key parameters:
    - A: Batch tensor A with shape (B, M, K).
    - B: Batch tensor B with shape (B, K, N).
    - C: Batch tensor C with shape (B, M, N).
    - A_scale: First scale batch tensor with shape (B, M, 1).
    - B_scale: Second scale batch tensor with shape (B, 1, N).
    - Bias: Bias batch tensor with shape (B, 1, N).
    """

    tl.assume(stride_ab > 0)
    tl.assume(stride_am > 0)
    tl.assume(stride_ak > 0)
    tl.assume(stride_bb > 0)
    tl.assume(stride_bk > 0)
    tl.assume(stride_bn > 0)
    tl.assume(stride_cb > 0)
    tl.assume(stride_cm > 0)
    tl.assume(stride_cn > 0)
    tl.assume(stride_ascaleb > 0)
    tl.assume(stride_bscaleb > 0)
    tl.assume(stride_biasb > 0)

    # -----------------------------------------------------------
    # Get batch program id
    batch_id = tl.program_id(axis=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 = tl.program_id(axis=1)
    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)

    if GROUP_SIZE_M == 1:
        pid_m = pid // num_pid_n
        pid_n = pid % num_pid_n
    else:
        num_pid_in_group = GROUP_SIZE_M * num_pid_n
        group_id = pid // num_pid_in_group
        first_pid_m = group_id * GROUP_SIZE_M
        group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
        pid_m = first_pid_m + (pid % group_size_m)
        pid_n = (pid % num_pid_in_group) // group_size_m

    tl.assume(pid_m >= 0)
    tl.assume(pid_n >= 0)

    # 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
    batch_id = tl.cast(batch_id, tl.int64)
    stride_ab = tl.cast(stride_ab, tl.int64)
    stride_bb = tl.cast(stride_bb, tl.int64)
    stride_cb = tl.cast(stride_cb, tl.int64)

    # Create pointers for first block of A and B input matrices
    offs_k = tl.arange(0, BLOCK_SIZE_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 + (
        batch_id * stride_ab
        + offs_am[:, None] * stride_am
        + offs_k[None, :] * stride_ak
    )
    b_ptrs = b_ptr + (
        batch_id * stride_bb
        + offs_k[:, None] * stride_bk
        + offs_bn[None, :] * stride_bn
    )

    # Create pointers for the scale tensors and load them
    offs_a_scale = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) % M
    offs_b_scale = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) % N
    a_scale = tl.load(a_scale_ptr + batch_id * stride_ascaleb + offs_a_scale)
    b_scale = tl.load(b_scale_ptr + batch_id * stride_bscaleb + offs_b_scale)

    acc_dtype = tl.float32 if c_ptr.type.element_ty != tl.int8 else tl.int32
    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=acc_dtype)

    for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
        # 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)
        else:
            a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
            b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)

        accumulator += tl.dot(a, b, input_precision="ieee")

        # Advance the ptrs to the next K block.
        a_ptrs += BLOCK_SIZE_K * stride_ak
        b_ptrs += BLOCK_SIZE_K * stride_bk

    # Apply scale
    accumulator *= a_scale[:, None] * b_scale[None, :]

    # Add bias
    if HAS_BIAS:
        offs_bias = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
        bias = tl.load(bias_ptr + batch_id * stride_biasb + offs_bias)
        accumulator = accumulator.to(bias_ptr.type.element_ty) + bias[None, :]

    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)
    offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
    c_ptrs = (
        c_ptr
        + stride_cb * batch_id
        + stride_cm * offs_cm[:, None]
        + stride_cn * offs_cn[None, :]
    )
    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)

    tl.store(c_ptrs, c, mask=c_mask)


@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()
        fpath = f"{AITER_TRITON_CONFIGS_PATH}/gemm/{dev}-BATCHED_GEMM-A8W8.json"
        print(f"fpath={fpath}")
        with open(fpath, "r") as file:
            config = json.load(file)
        _get_config._config_dict = config

    if M + N >= 4096:
        return _get_config._config_dict["large"]
    else:
        return _get_config._config_dict["small"]


def batched_gemm_a8w8(
    XQ: torch.Tensor,
    WQ: torch.Tensor,
    x_scale: torch.Tensor,
    w_scale: torch.Tensor,
    bias: Optional[torch.Tensor] = None,
    dtype: Optional[torch.dtype] = torch.bfloat16,
    splitK: Optional[int] = None,
    YQ: Optional[torch.Tensor] = None,
    config: Optional[dict] = None,
):
    """
    Computes the matmul YQ[i] = XQ[i] x WQ[i]T and applies a conversion scale for every i in a given batch.
    Optionally, adds a bias to each result.

    The conversion scale for each matmul is received in the form of two 1D tensors that are multiplied to form a
    2D one before being applied.

    Key parameters:
    - XQ: Batch tensor XQ with shape (B, M, K).
    - WQ: Batch tensor WQ with shape (B, N, K).
    - X_scale: First scale batch tensor with shape (B, M, 1).
    - W_scale: Second scale batch tensor with shape (B, 1, N).
    - Bias: Bias batch tensor with shape (B, 1, N).
    - YQ: Output Matrix Y with shape (B, M, N). If this is none, then it's created by this API and returned as output

    Returns:
    - YQ: The output batch tensor with shape (B, M, N).
    """

    # Make sure XQ and WQ are contiguous in memory
    XQ = XQ.contiguous()
    WQ = WQ.contiguous()

    # Check constraints.
    assert XQ.shape[0] == WQ.shape[0], "Incompatible Batch dimensions!!!"
    assert XQ.shape[2] == WQ.shape[2], "Incompatible K dimensions!!!"
    assert dtype in [
        torch.bfloat16,
        torch.float16,
    ], f"Output {dtype=} is currently not supported in batched_gemm_a8w8"
    assert splitK == None, "Currently, there isn't any support for splitK on Triton"

    # Transpose N and K dimensions of WQ: (B, N, K) -> (B, K, N)
    WQ = WQ.transpose(1, 2)

    B = XQ.shape[0]
    M = XQ.shape[1]
    K = XQ.shape[2]
    N = WQ.shape[2]

    has_bias = bias is not None
    if YQ is None:
        YQ = torch.empty((B, M, N), dtype=dtype, device=XQ.device)

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

    grid = lambda META: (  # noqa: E731
        B,
        triton.cdiv(M, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"]),
    )

    _batched_gemm_a8w8_kernel[grid](
        XQ,
        WQ,
        YQ,
        x_scale,
        w_scale,
        bias,
        M,
        N,
        K,
        XQ.stride(0),
        XQ.stride(1),
        XQ.stride(2),
        WQ.stride(0),
        WQ.stride(1),
        WQ.stride(2),
        YQ.stride(0),
        YQ.stride(1),
        YQ.stride(2),
        x_scale.stride(0),
        w_scale.stride(0),
        bias.stride(0) if has_bias else 0,
        has_bias,
        **config,
    )

    return YQ
