import torch
import triton
import triton.language as tl

@triton.autotune(
    configs=[
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 128, 'num_stages': 1}, num_warps=8), # Total shmem: 24576
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 128, 'num_stages': 2}, num_warps=8), # Total shmem: 49152
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 128, 'num_stages': 1}, num_warps=4), # Total shmem: 24576
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 128, 'num_stages': 2}, num_warps=4), # Total shmem: 49152

        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K_DATA_TILE': 128, 'num_stages': 1}, num_warps=8), # Total shmem: 32768
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K_DATA_TILE': 128, 'num_stages': 2}, num_warps=8), # Total shmem: 65536 
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K_DATA_TILE': 128, 'num_stages': 1}, num_warps=4), # Total shmem: 32768
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K_DATA_TILE': 128, 'num_stages': 2}, num_warps=4), # Total shmem: 65536 

        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 64,  'num_stages': 1}, num_warps=8), # Total shmem: 20480
        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 64,  'num_stages': 2}, num_warps=8), # Total shmem: 40960
        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 64,  'num_stages': 3}, num_warps=8), # Total shmem: 61440
        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 64,  'num_stages': 1}, num_warps=4), # Total shmem: 20480
        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 64,  'num_stages': 2}, num_warps=4), # Total shmem: 40960
        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 64,  'num_stages': 3}, num_warps=4), # Total shmem: 61440


        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 64,  'num_stages': 1}, num_warps=8), # Total shmem: 12288
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 64,  'num_stages': 2}, num_warps=8), # Total shmem: 24576
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 64,  'num_stages': 3}, num_warps=8), # Total shmem: 36864
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 64,  'num_stages': 4}, num_warps=8), # Total shmem: 49152
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 64,  'num_stages': 5}, num_warps=8), # Total shmem: 61440
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 64,  'num_stages': 2}, num_warps=4), # Total shmem: 24576
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 64,  'num_stages': 4}, num_warps=4), # Total shmem: 49152

        triton.Config({'BLOCK_SIZE_M': 64,  'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 128, 'num_stages': 1}, num_warps=8), # Total shmem: 16384
        triton.Config({'BLOCK_SIZE_M': 64,  'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 128, 'num_stages': 2}, num_warps=8), # Total shmem: 32768
        triton.Config({'BLOCK_SIZE_M': 64,  'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 128, 'num_stages': 3}, num_warps=8), # Total shmem: 49152
        triton.Config({'BLOCK_SIZE_M': 64,  'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 128, 'num_stages': 4}, num_warps=8), # Total shmem: 65536 
        triton.Config({'BLOCK_SIZE_M': 64,  'BLOCK_SIZE_N': 64,  'BLOCK_SIZE_K_DATA_TILE': 128, 'num_stages': 2}, num_warps=4), # Total shmem: 32768
    ],
    key=['M', 'N', 'K'], 
)
@triton.jit
def _matmul_kernel_fp8dot_autotuned(
    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,
    B_SCALE_N_GRANULARITY: tl.constexpr, 
    SCALE_K_GRANULARITY: tl.constexpr,
    BLOCK_SIZE_M: tl.constexpr, 
    BLOCK_SIZE_N: tl.constexpr, 
    BLOCK_SIZE_K_DATA_TILE: tl.constexpr 

):
   
    pid_m_abs = tl.program_id(0) 
    pid_n_abs = tl.program_id(1) 

    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)

    mask_m_dim = offs_m_for_c_tile < M
    mask_n_dim = offs_n_for_c_tile < N

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

    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_range_data_tile = tl.arange(0, BLOCK_SIZE_K_DATA_TILE)

    for k_block_start in range(0, K, BLOCK_SIZE_K_DATA_TILE):
        current_k_scale_idx = k_block_start // SCALE_K_GRANULARITY 

        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")

        b_scale_n_idx_for_tile = (pid_n_abs * BLOCK_SIZE_N) // B_SCALE_N_GRANULARITY
        
        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")

        k_indices_absolute = k_block_start + k_indices_range_data_tile

        a_tile_ptrs = a_tile_ptrs_base + k_indices_absolute[None, :] * stride_a_k_dim
        mask_a_k_tile = k_indices_absolute[None, :] < K 
        a_block_fp8 = tl.load(a_tile_ptrs, mask=mask_m_dim[:, None] & mask_a_k_tile, other=0.0) 

        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_k_tile = k_indices_absolute[:, None] < K
        b_block_fp8_transposed = tl.load(b_tile_ptrs, mask=mask_n_dim[None, :] & mask_b_k_tile, other=0.0) 
        
        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

    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
    
    B_SCALE_N_GRANULARITY_val = 128 
    SCALE_K_GRANULARITY_val = 128   

    grid = lambda META: (
        triton.cdiv(M, META['BLOCK_SIZE_M']),
        triton.cdiv(N, META['BLOCK_SIZE_N']),
    )

    _matmul_kernel_fp8dot_autotuned[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, 
        B_SCALE_N_GRANULARITY_val, 
        SCALE_K_GRANULARITY_val,   
    )
    return c

python eval.py test /tmp/tmpdivw1oyh