import torch
import triton
import triton.language as tl

# The Triton kernel matmul_kernel_fp8dot_final is defined here.
# Changes from v15:
# 1. Data load (A and B tiles) eviction_policy changed from Triton's default to "evict_last".
# (num_stages remains 2, scale load eviction_policy remains "evict_first")
@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.
    Version 16:
    - BLOCK_SIZE_M/N/K = 128
    - num_warps = 8, num_stages = 2
    - Scale loads eviction_policy: "evict_first".
    - Data (A, B tiles) loads eviction_policy: "evict_last".
    This configuration uses 64KB of shared memory, which is at the hardware limit.

    A: M x K (column-major, e4m3fnuz)
    B: N x K (column-major, e4m3fnuz)
    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)
    """
    pid_m_abs = tl.program_id(0)
    pid_n_abs = tl.program_id(1)

    # Offsets for the C tile this program instance will compute
    offs_m_for_c_tile = pid_m_abs * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
    offs_n_for_c_tile = pid_n_abs * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)

    # Boundary masks for M and N dimensions
    mask_m_dim = offs_m_for_c_tile < M
    mask_n_dim = offs_n_for_c_tile < N

    # Accumulator for the C tile
    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)

    # Base pointers for A scale and A data
    a_scale_load_ptrs_base = A_SCALE_ptr + offs_m_for_c_tile * stride_a_scale_m_dim
    a_tile_ptrs_base = A_ptr + offs_m_for_c_tile[:, None] * stride_a_m_dim
    
    # K indices for a block
    k_indices_range = tl.arange(0, BLOCK_SIZE_K)

    # Loop over K dimension
    for k_block_start in range(0, K, BLOCK_SIZE_K):
        current_k_scale_idx = k_block_start // BLOCK_SIZE_K

        # Load A scales with "evict_first" policy
        a_scale_load_ptrs = a_scale_load_ptrs_base + current_k_scale_idx * stride_a_scale_k_scaled_dim
        a_scales_m_vector = tl.load(a_scale_load_ptrs, mask=mask_m_dim, other=1.0, eviction_policy="evict_first")

        # Load B scale with "evict_first" policy
        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

        # Load A tile with "evict_last" eviction policy
        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, eviction_policy="evict_last") # MODIFIED

        # Load B tile (transposed) with "evict_last" eviction policy
        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, eviction_policy="evict_last") # MODIFIED
        
        # Perform dot product and apply scaling
        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
    BLOCK_SIZE_N_val = 128 
    BLOCK_SIZE_K_val = 128 

    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
    # Shared memory for num_stages=2: 2 * (128*128*1_A + 128*128*1_B) = 65536 bytes. (Limit 65536)
    num_warps_val = 8
    num_stages_val = 2

    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/tmp2pnpb2xd