import torch
import triton
import triton.language as tl

# The Triton kernel matmul_kernel_fp8dot_final remains the same.
# It is assumed to be defined above this function in your environment.
@triton.jit
def matmul_kernel_fp8dot_final(
    A_ptr, B_ptr, A_SCALE_ptr, B_SCALE_ptr, C_ptr,
    M, N, K,
    stride_a_m_dim, stride_a_k_dim,
    stride_b_n_dim, stride_b_k_dim,
    stride_a_scale_m_dim, stride_a_scale_k_scaled_dim,
    stride_b_scale_n_scaled_dim, stride_b_scale_k_scaled_dim,
    stride_c_m_dim, stride_c_n_dim,
    BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr,
    BLOCK_SIZE_K: tl.constexpr
):
    """
    Triton Kernel for FP8 Matrix Multiplication with Blockwise Scaling.

    This kernel computes C = A @ B.T where A and B are FP8 matrices and
    are scaled using FP32 scale factors.
    A: M x K (column-major, e4m3fnuz)
    B: N x K (column-major, e4m3fnuz) -> effectively K x N for dot product after loading
    A_SCALE: M x (K // 128) (column-major, fp32)
    B_SCALE: (N // 128) x (K // 128) (column-major, fp32)
    C: M x N (row-major, bf16)

    BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K: Tile dimensions for computation.
    It's assumed that BLOCK_SIZE_K is 128 due to scale factor granularity.
    It's assumed that BLOCK_SIZE_N is 128 due to b_scale indexing.
    """
    # Program IDs identify the current tile being processed
    pid_m_abs = tl.program_id(0)  # Absolute program ID for M dimension
    pid_n_abs = tl.program_id(1)  # Absolute program ID for N dimension

    # Offsets for the C tile this program instance will compute
    # arange creates a sequence like [0, 1, ..., BLOCK_SIZE - 1]
    offs_m_for_c_tile = pid_m_abs * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) # Rows in C tile
    offs_n_for_c_tile = pid_n_abs * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) # Cols in C tile

    # Boundary masks for M and N dimensions to handle non-perfectly tiled matrices
    mask_m_dim = offs_m_for_c_tile < M    # Mask for valid rows
    mask_n_dim = offs_n_for_c_tile < N    # Mask for valid columns

    # Accumulator for the C tile, initialized to zeros. Computation is in FP32.
    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)

    # Base pointers for loading A scales. Each thread in M-dim of block loads its scale.
    # offs_m_for_c_tile has shape (BLOCK_SIZE_M,), so a_scale_load_ptrs_base also has this shape.
    a_scale_load_ptrs_base = A_SCALE_ptr + offs_m_for_c_tile * stride_a_scale_m_dim

    # Base pointers for loading A tiles.
    # offs_m_for_c_tile[:, None] makes it a column vector for broadcasting with k_indices.
    a_tile_ptrs_base = A_ptr + offs_m_for_c_tile[:, None] * stride_a_m_dim

    # Range of K indices for a K-block
    k_indices_range = tl.arange(0, BLOCK_SIZE_K)

    # Loop over K dimension in blocks of BLOCK_SIZE_K
    for k_block_start in range(0, K, BLOCK_SIZE_K):

        # Index for the current K-block in the scale tensors
        # Assumes scale block size in K is BLOCK_SIZE_K (typically 128)
        current_k_scale_idx = k_block_start // BLOCK_SIZE_K # Integer division

        # --- Load A scales for the current M-tile and K-block ---
        # Pointers to load a vector of scales for the M-dimension of the C tile
        a_scale_load_ptrs = a_scale_load_ptrs_base + current_k_scale_idx * stride_a_scale_k_scaled_dim
        # Load a_scales for the current M-tile rows, for the current K-block. Shape: (BLOCK_SIZE_M,)
        a_scales_m_vector = tl.load(a_scale_load_ptrs, mask=mask_m_dim, other=1.0, eviction_policy="evict_first")

        # --- Load B scale for the current N-tile and K-block ---
        # `pid_n_abs` directly indexes the N-block dimension of b_scale.
        b_scale_n_idx_for_tile = pid_n_abs
        
        b_scale_scalar_ptr = B_SCALE_ptr + (
            b_scale_n_idx_for_tile * stride_b_scale_n_scaled_dim +
            current_k_scale_idx * stride_b_scale_k_scaled_dim
        )
        b_scale_val = tl.load(b_scale_scalar_ptr, eviction_policy="evict_first") 

        # Absolute K indices for the current block
        k_indices_absolute = k_block_start + k_indices_range # Shape: (BLOCK_SIZE_K,)

        # --- Load A tile (BLOCK_SIZE_M x BLOCK_SIZE_K) ---
        a_tile_ptrs = a_tile_ptrs_base + k_indices_absolute[None, :] * stride_a_k_dim
        mask_a_data_load = mask_m_dim[:, None] 
        a_block_fp8 = tl.load(a_tile_ptrs, mask=mask_a_data_load, other=0.0)

        # --- Load B tile (transposed, effectively BLOCK_SIZE_K x BLOCK_SIZE_N) ---
        b_tile_ptrs = B_ptr + (offs_n_for_c_tile[None, :] * stride_b_n_dim +
                               k_indices_absolute[:, None] * stride_b_k_dim)
        mask_b_data_load = mask_n_dim[None, :]
        b_block_fp8_transposed = tl.load(b_tile_ptrs, mask=mask_b_data_load, other=0.0)

        # --- Perform dot product and scale ---
        dot_output_f32 = tl.dot(a_block_fp8, b_block_fp8_transposed, out_dtype=tl.float32, allow_tf32=False)
        
        scaled_dot_output = dot_output_f32 * a_scales_m_vector[:, None] * b_scale_val
        accumulator += scaled_dot_output

    # --- Store the accumulated C tile ---
    c_tile_ptrs = C_ptr + (offs_m_for_c_tile[:, None] * stride_c_m_dim +
                           offs_n_for_c_tile[None, :] * stride_c_n_dim)
    
    mask_c_store = mask_m_dim[:, None] & mask_n_dim[None, :]
    tl.store(c_tile_ptrs, accumulator.to(tl.bfloat16), mask=mask_c_store)


def custom_kernel(kernel_input_data: tuple) -> torch.Tensor:
    a, b, a_scale, b_scale, c = kernel_input_data

    M, K_orig = a.shape
    N, K_check = b.shape 
    K = K_orig 

    stride_a_m_dim = 1
    stride_a_k_dim = M

    stride_b_n_dim = 1
    stride_b_k_dim = N
      
    stride_a_scale_m_dim = 1
    stride_a_scale_k_scaled_dim = M
      
    stride_b_scale_n_scaled_dim = 1
    stride_b_scale_k_scaled_dim = b_scale.shape[0] if b_scale.nelement() > 0 and b_scale.dim() > 1 else 1
    if b_scale.nelement() > 0 and b_scale.dim() == 1: 
        stride_b_scale_k_scaled_dim = b_scale.shape[0]
    if b_scale.nelement() == 0 :
         stride_b_scale_k_scaled_dim = 1

    stride_c_m_dim = N
    stride_c_n_dim = 1

    # Tile sizes for the kernel
    BLOCK_SIZE_M_val = 128  # Reverted to 128
    BLOCK_SIZE_N_val = 128  # Must be 128 for current b_scale logic in kernel
    BLOCK_SIZE_K_val = 128  # Must be 128 for scale granularity

    grid_m = triton.cdiv(M, BLOCK_SIZE_M_val)
    grid_n = triton.cdiv(N, BLOCK_SIZE_N_val)
    grid = (grid_m, grid_n)

    # Kernel launch parameters: num_warps and num_stages
    # Shared memory for num_stages=1:
    # 1 * (128*128*1_byte_A + 128*128*1_byte_B) = 32768 bytes. (Limit 65536)
    num_warps_val = 4    # Testing with 4 warps
    num_stages_val = 1   # Testing with 1 stage

    matmul_kernel_fp8dot_final[grid](
        a, b, a_scale, b_scale, c, 
        M, N, K,  
        stride_a_m_dim, stride_a_k_dim, 
        stride_b_n_dim, stride_b_k_dim, 
        stride_a_scale_m_dim, stride_a_scale_k_scaled_dim, 
        stride_b_scale_n_scaled_dim, stride_b_scale_k_scaled_dim,  
        stride_c_m_dim, stride_c_n_dim, 
        BLOCK_SIZE_M=BLOCK_SIZE_M_val, 
        BLOCK_SIZE_N=BLOCK_SIZE_N_val, 
        BLOCK_SIZE_K=BLOCK_SIZE_K_val, 
        num_warps=num_warps_val, 
        num_stages=num_stages_val 
    )
    return c

python eval.py benchmark /tmp/tmp86tmcna5