# import triton
# import triton.language as tl
# import torch

# @triton.heuristics(
#     {
#         "EVEN_K": lambda args: args["K"] % args["BLOCK_K"] == 0,
#     }
# )
# @triton.jit
# def fp8_gemm_kernel(
#     a_ptr,          # Pointer to A [M, K], column-major, float8_e4m3fnuz
#     b_ptr,          # Pointer to B [N, K], column-major, float8_e4m3fnuz
#     c_ptr,          # Pointer to C [M, N], row-major, bfloat16
#     a_scale_ptr,    # Pointer to a_scale [M, K // 128], column-major, float32
#     b_scale_ptr,    # Pointer to b_scale [N // 128, K // 128], column-major, float32
#     M, N, K,        # Matrix dimensions
#     stride_a_m, stride_a_k,      # Strides for A
#     stride_b_n, stride_b_k,      # Strides for B
#     stride_c_m, stride_c_n,      # Strides for C
#     stride_as_m, stride_as_k,    # Strides for a_scale
#     stride_bs_n, stride_bs_k,    # Strides for b_scale
#     BLOCK_M: tl.constexpr,       # Tile size for M
#     BLOCK_N: tl.constexpr,       # Tile size for N
#     BLOCK_K: tl.constexpr,       # Tile size for K
#     GROUP_SIZE: tl.constexpr,
#     EVEN_K: tl.constexpr,
# ):
#     # tl.assume(stride_a_m > 0)
#     # tl.assume(stride_a_k > 0)
#     # tl.assume(stride_b_k > 0)
#     # tl.assume(stride_b_n > 0)
#     # tl.assume(stride_c_m > 0)
#     # tl.assume(stride_c_n > 0)
#     # tl.assume(stride_as_m > 0)
#     # tl.assume(stride_as_k > 0)
#     # tl.assume(stride_bs_k > 0)
#     # tl.assume(stride_bs_n > 0)

#     # GROUP_SIZE = 8
#     PID = tl.program_id(axis=0) 
#     # defining the size of groups
#     num_PID_along_M = tl.cdiv(M, BLOCK_M) # the number of blocks along M dimension
#     num_PID_along_N = tl.cdiv(N, BLOCK_N) # the number of blocks along N dimension
#     num_PID_in_group = GROUP_SIZE * num_PID_along_N
#     # figurinig out which group this PID is in
#     group_id = PID // num_PID_in_group
#     # tells us which row to start at for this group
#     first_PID_in_group_along_M = group_id * GROUP_SIZE 
#     # this is usually equal to GROUP_SIZE; the alternative case happens when we're at edge of the tensor and 
#     #  its dimensions don't cleanly divde into GROUP_SIZE # TODO is this true?
#     group_size_adj = min(num_PID_along_M - first_PID_in_group_along_M, GROUP_SIZE) 
#     # this is the bulk of the actual mapping of PIDs to group-major ordering
#     pid_m = first_PID_in_group_along_M + ((PID % num_PID_in_group) % group_size_adj)
#         # (PID % num_PID_in_group) puts the current program id into the context of a group
#         # (first_PID_in_group_along_m + ...) shifts the PID into the correct group
#         # (... % group_size_adj) removes the column component to get us onto the correct row
#     pid_n = (PID % num_PID_in_group) // group_size_adj

#     # tl.assume(pid_m > 0)
#     # tl.assume(pid_n > 0)
    
#     # # Get block indices in the 2D grid
#     # pid_m = tl.program_id(axis=0)  # Block index for M dimension
#     # pid_n = tl.program_id(axis=1)  # Block index for N dimension

#     # Compute offsets for the C tile
#     offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) % M # Row indices for C
#     offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) % N  # Column indices for C

#     # Initialize accumulator for the C tile in FP32
#     acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
#     it_k = tl.arange(0, BLOCK_K)
#     scale_n_idx = (offs_n // 128)

#     broadcasted_offs_m = offs_m[:, None]
#     broadcasted_offs_n_r = offs_n[:, None]
#     broadcasted_offs_n_c = offs_n[None, :]

#     # K_scale = tl.cdiv(K, 128)
#     # N_scale = tl.cdiv(N, 128)

#     # Loop over K dimension in chunks of BLOCK_K
#     # for k in tl.range(0, K, BLOCK_K, num_stages=3): # 1(none) , 2 or 3
#     for k in range(0, K, BLOCK_K): # 2 or 3

#         # Compute K offsets
#         offs_k = k + it_k

#         broadcasted_offs_k = offs_k[None, :]
        


#         # Load tiles from A and B (column-major)
#         # a_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
#         # b_mask = (offs_n[:, None] < N) & (offs_k[None, :] < K)


#         if EVEN_K:
#             a = tl.load(a_ptr + broadcasted_offs_m * stride_a_m + broadcasted_offs_k * stride_a_k)
#             b = tl.load(b_ptr + broadcasted_offs_n_r * stride_b_n + broadcasted_offs_k * stride_b_k)
#         else:
#             # a_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
#             # b_mask = (offs_n[:, None] < N) & (offs_k[None, :] < K)
#             a = tl.load(a_ptr + broadcasted_offs_m * stride_a_m + broadcasted_offs_k * stride_a_k, mask=(offs_m[:, None] < M) & (offs_k[None, :] < K), other=0.0)
#             b = tl.load(b_ptr + broadcasted_offs_n_r * stride_b_n + broadcasted_offs_k * stride_b_k, mask=(offs_n[:, None] < N) & (offs_k[None, :] < K), other=0.0)
        
#         # a = tl.load(
#         #     a_ptr + offs_m[:, None] * stride_a_m + offs_k[None, :] * stride_a_k,
#         #     mask=a_mask,
#         #     other=0.0
#         # )
#         # b = tl.load(
#         #     b_ptr + offs_n[:, None] * stride_b_n + offs_k[None, :] * stride_b_k,
#         #     mask=b_mask,
#         #     other=0.0
#         # )

#         # Compute scale indices for a_scale and b_scale
#         scale_k_idx = k // 128  # Index into K // 128 dimension
#         # a_scale_mask = (offs_m < M) & (scale_k_idx < K_scale)
#         # b_scale_mask = ((offs_n // 128) < N_scale) & (scale_k_idx < K_scale)

#         # Load a_scale [M, K // 128] for 1x128 chunks
#         a_scale = tl.load(
#             a_scale_ptr + offs_m * stride_as_m + scale_k_idx * stride_as_k,
#             # mask=a_scale_mask,
#             # other=1.0
#         )
#         # Load b_scale [N // 128, K // 128] for 128x128 chunks
#         b_scale = tl.load(
#             b_scale_ptr + scale_n_idx * stride_bs_n + scale_k_idx * stride_bs_k,
#             # mask=b_scale_mask,
#             # other=1.0
#         )

#         # Apply scaling block-wise
#         # a_scaled = a * a_scale[:, None]
#         # b_scaled = b * b_scale[:, None]
#         # b_scale = b_scale[scale_k_idx]

#         # Compute dot product
#         # acc = tl.dot(a * a_scale[:, None], tl.trans(b * b_scale[:, None]), acc=acc)

#         # Compute block result
#         acc += tl.dot(a, tl.trans(b), out_dtype=tl.float32, input_precision="ieee") * a_scale[:, None] * b_scale[None, :]
    
#     # Write results to C (row-major)
#     # c_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
#     tl.store(
#         c_ptr + broadcasted_offs_m * stride_c_m + broadcasted_offs_n_c * stride_c_n,
#         acc.to(tl.bfloat16),
#         mask=(broadcasted_offs_m < M) & (broadcasted_offs_n_c < N)
#     )

# def fp8_blockwise_gemm(
#     a: torch.Tensor,          # [M, K], float8_e4m3fnuz, column-major
#     b: torch.Tensor,          # [N, K], float8_e4m3fnuz, column-major
#     a_scale: torch.Tensor,    # [M, K // 128], float32, column-major
#     b_scale: torch.Tensor,    # [N // 128, K // 128], float32, column-major
#     c: torch.Tensor           # [M, N], bfloat16, row-major
# ):
#     assert a.shape[1] == b.shape[1], "Incompatible K dimension"
#     assert a.is_cuda and b.is_cuda and a_scale.is_cuda and b_scale.is_cuda and c.is_cuda, "Tensors must be on GPU"
#     assert a.dtype == torch.float8_e4m3fnuz, "A must be FP8"
#     assert b.dtype == torch.float8_e4m3fnuz, "B must be FP8"
#     assert a_scale.dtype == torch.float32, "a_scale must be FP32"
#     assert b_scale.dtype == torch.float32, "b_scale must be FP32"
#     assert c.dtype == torch.bfloat16, "C must be BF16"

#     M, N = c.shape
#     _, K = a.shape

#     BLOCK_M = 128
#     BLOCK_N = 128
#     BLOCK_K = 128
#     GROUP_SIZE = 4
#     num_warps = 4
#     num_stages = 2
#     kpack = 2 # only for gemm kernel
#     # waves_per_eu = 2
#     # matrix_instr_nonkdim = 16

#     b = b.contiguous()
#     b_scale = b_scale.contiguous()

#     print(a.is_contiguous(), a_scale.is_contiguous())
#     print(b.is_contiguous(), b_scale.is_contiguous())

#     # 2D grid
#     # grid = (triton.cdiv(M, BLOCK_M), triton.cdiv(N, BLOCK_N))
#     # 1D grid
#     grid = (triton.cdiv(M, BLOCK_M) * triton.cdiv(N, BLOCK_N), 1)
   
#     # Launch kernel
#     fp8_gemm_kernel[grid](
#         a_ptr=a,
#         b_ptr=b,
#         c_ptr=c,
#         a_scale_ptr=a_scale,
#         b_scale_ptr=b_scale,
#         M=M, N=N, K=K,
#         stride_a_m=a.stride(0), stride_a_k=a.stride(1),
#         stride_b_n=b.stride(0), stride_b_k=b.stride(1),
#         stride_c_m=c.stride(0), stride_c_n=c.stride(1),
#         stride_as_m=a_scale.stride(0), stride_as_k=a_scale.stride(1),
#         stride_bs_n=b_scale.stride(0), stride_bs_k=b_scale.stride(1),
#         BLOCK_M=BLOCK_M,
#         BLOCK_N=BLOCK_N,
#         BLOCK_K=BLOCK_K,
#         GROUP_SIZE=GROUP_SIZE,
#         # waves_per_eu=waves_per_eu,
#         kpack=kpack,
#         # matrix_instr_nonkdim=matrix_instr_nonkdim,
#         num_warps=num_warps,
#         num_stages=num_stages,

#     )
#     return c


# SPDX-License-Identifier: MIT
# Copyright (C) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.

import torch
import triton
import triton.language as tl
from typing import Optional


# TODO Move this to a common folder. Will need to add future arch list
def get_arch():
    return triton.runtime.driver.active.get_current_target().arch


@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 _gemm_a8w8_blockscale_kernel(
    # Pointers to matrices
    a_ptr,
    b_ptr,
    c_ptr,
    a_scale_ptr,
    b_scale_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_am,
    stride_ak,
    stride_bk,
    stride_bn,
    stride_cm,
    stride_cn,
    stride_ascale_m,
    stride_ascale_k,
    stride_bscale_k,
    stride_bscale_n,
    # Meta-parameters
    GROUP_K: tl.constexpr,
    GROUP_N: 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 gemm_a8w8_blockscale function
    below

    Computes the 8 bit matmul C = A x B using the block-scale quantization approach.

    Key parameters:
    - A: Matrix A with shape (M, K).
    - B: Matrix B with shape (K, N).
    - C: Matrix C with shape (M, N).
    - A_scale: Scale tensor for A with shape (M, *scale_k).
    - B_scale: Scale tensor for B with shape (*scale_k, **scale_n).

    *scale_k = (K + GROUP_K - 1) // GROUP_K
    **scale_n = (N + GROUP_N - 1) // GROUP_N
    """

    NUM_XCDS: tl.constexpr = 8

    # tl.assume(stride_am > 0)
    # tl.assume(stride_ak > 0)
    # tl.assume(stride_bk > 0)
    # tl.assume(stride_bn > 0)
    # tl.assume(stride_cm > 0)
    # tl.assume(stride_cn > 0)
    # tl.assume(stride_ascale_m > 0)
    # tl.assume(stride_ascale_k > 0)
    # tl.assume(stride_bscale_k > 0)
    # tl.assume(stride_bscale_n > 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=0)
    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)

    ## pid remapping on xcds
    # Number of pids per XCD in the new arrangement
    pids_per_xcd = (GRID_MN + NUM_XCDS - 1) // NUM_XCDS
    # When GRID_MN cannot divide NUM_XCDS, some xcds will have
    # pids_per_xcd pids, the other will have pids_per_xcd - 1 pids.
    # We calculate the number of xcds that have pids_per_xcd pids as
    # tall_xcds
    tall_xcds = GRID_MN % NUM_XCDS
    tall_xcds = NUM_XCDS if tall_xcds == 0 else tall_xcds
    # Compute current XCD and local pid within the XCD
    xcd = pid % NUM_XCDS
    local_pid = pid // NUM_XCDS
    # Calculate new pid based on the new grouping
    # Note that we need to consider the following two cases:
    # 1. the current pid is on a tall xcd
    # 2. the current pid is on a short xcd
    if xcd < tall_xcds:
        pid = xcd * pids_per_xcd + local_pid
    else:
        pid = (
            tall_xcds * pids_per_xcd
            + (xcd - tall_xcds) * (pids_per_xcd - 1)
            + local_pid
        )

    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)

    # 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 + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
    b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)

    # Create pointers for the scales
    k_start = 0
    offs_ks = k_start // GROUP_K
    a_scale_ptrs = a_scale_ptr + offs_am * stride_ascale_m + offs_ks * stride_ascale_k
    offs_bsn = offs_bn // GROUP_N
    b_scale_ptrs = b_scale_ptr + offs_ks * stride_bscale_k + offs_bsn * stride_bscale_n

    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)

        a_scale = tl.load(a_scale_ptrs)
        b_scale = tl.load(b_scale_ptrs)

        # Perform dot operation and apply scale
        accumulator += (
            tl.dot(a, b, input_precision="ieee") * a_scale[:, None] * b_scale[None, :]
        )

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

        k_cur = k * BLOCK_SIZE_K // GROUP_K
        k_nxt = (k + 1) * BLOCK_SIZE_K // GROUP_K
        offs_ks = k_nxt - k_cur
        a_scale_ptrs += offs_ks * stride_ascale_k
        b_scale_ptrs += offs_ks * stride_bscale_k

    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_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)


def gemm_a8w8_blockscale(
    x: torch.Tensor,
    w: torch.Tensor,
    x_scale: torch.Tensor,
    w_scale: torch.Tensor,
    dtype: Optional[float] = torch.bfloat16,
    y: Optional[torch.Tensor] = None,
):
    """
    Computes the 8 bit matmul Y = X x WT using the block-scale quantization approach.

    Key parameters:
    - X: Matrix X with shape (M, K).
    - W: Matrix W with shape (N, K).
    - X_scale: Scale tensor for X with shape (M, *scale_k).
    - W_scale: Scale tensor for W with shape (**scale_n, *scale_k).
    - Y: Output Matrix Y with shape (M, K). If this is none, then it's created by this API and returned as output

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

    *scale_k = (K + scale_block_size_k - 1) // scale_block_size_k
    **scale_n = (N + scale_block_size_n - 1) // scale_block_size_n
    """

    # Transpose w and w_scale
    w = w.T
    w_scale = w_scale.T

    M, K = x.shape
    K, N = w.shape

    # Scale block sizes
    # TODO: need a better way to pass scale block sizes around
    GROUP_K = triton.next_power_of_2(triton.cdiv(K, w_scale.shape[0]))
    GROUP_N = triton.next_power_of_2(triton.cdiv(N, w_scale.shape[1]))

    # Check constraints.
    assert x.shape[1] == w.shape[0], "Incompatible dimensions!!!"

    # if y is None:
    #     y = torch.empty((M, N), dtype=dtype, device=x.device)

    BLOCK_SIZE_M = 128
    BLOCK_SIZE_N = 128
    BLOCK_SIZE_K = 128
    GROUP_SIZE_M = 4
    waves_per_eu = 5
    kpack = 1 if get_arch() in ("gfx950") else 2
    matrix_instr_nonkdim = 16
    num_warps = 4
    num_stages = 2

    grid = (triton.cdiv(M, BLOCK_SIZE_M) * triton.cdiv(N, BLOCK_SIZE_N),)
    _gemm_a8w8_blockscale_kernel[grid](
        x,
        w,
        y,
        x_scale,
        w_scale,
        M,
        N,
        K,
        x.stride(0),
        x.stride(1),
        w.stride(0),
        w.stride(1),
        y.stride(0),
        y.stride(1),
        x_scale.stride(0),
        x_scale.stride(1),
        w_scale.stride(0),
        w_scale.stride(1),
        GROUP_K,
        GROUP_N,
        BLOCK_SIZE_M,
        BLOCK_SIZE_N,
        BLOCK_SIZE_K,
        GROUP_SIZE_M,
        waves_per_eu=waves_per_eu,
        kpack=kpack,
        # matrix_instr_nonkdim=matrix_instr_nonkdim,
        num_warps=num_warps,
        num_stages=num_stages,
    )

    return y


from task import input_t, output_t

def custom_kernel(data: input_t) -> output_t:
    """
    Efficient Triton implementation of FP8 GEMM for AMD MI300.
    
    Args:
        data: Tuple of (a, b, a_scale, b_scale, c)
    
    Returns:
        c: torch.Tensor[bfloat16] of shape [m, n]
    """
    a, b, a_scale, b_scale, c = data
    return gemm_a8w8_blockscale(a, b, a_scale, b_scale, torch.bfloat16, c)


# # autotune_configs = [
# #     triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 32, 'GROUP_SIZE': 8}, num_stages=4, num_warps=4),
# #     triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'GROUP_SIZE': 8}, num_stages=4, num_warps=4),
# #     triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 32, 'GROUP_SIZE': 8}, num_stages=4, num_warps=4),
# #     triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 32, 'GROUP_SIZE': 8}, num_stages=4, num_warps=4),
# #     triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 32, 'GROUP_SIZE': 8}, num_stages=4, num_warps=4),
# #     triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32, 'GROUP_SIZE': 8}, num_stages=5, num_warps=2),
# #     triton.Config({'BLOCK_M': 32, 'BLOCK_N': 64, 'BLOCK_K': 32, 'GROUP_SIZE': 8}, num_stages=5, num_warps=2)
# # ]

# # @triton.autotune(configs = autotune_configs, key=['M', 'N', 'K'])
python eval.py test /tmp/tmpt9_ojb9j