import torch
import triton
import triton.language as tl

@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_COMMON: tl.constexpr, 
    A_B_SCALE_K_GRANULARITY: tl.constexpr,
    B_SCALE_N_GRANULARITY: 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)

    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_common_tile = tl.arange(0, BLOCK_SIZE_K_COMMON) 
    
    for k_block_start in range(0, K, BLOCK_SIZE_K_COMMON): 
        current_k_scale_idx = k_block_start // A_B_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, eviction_policy="evict_first") # No M-mask

        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_common_tile

        a_tile_ptrs = a_tile_ptrs_base + k_indices_absolute[None, :] * stride_a_k_dim
        a_block_fp8 = tl.load(a_tile_ptrs) # No M or K masks for A data load

        b_tile_ptrs = B_ptr + (
            k_indices_absolute[:, None] * stride_b_k_dim + 
            offs_n_for_c_tile[None, :] * stride_b_n_dim    
        )
        b_block_fp8_for_dot = tl.load(b_tile_ptrs) # No N or K masks for B data load
        
        dot_output_f32 = tl.dot(a_block_fp8, b_block_fp8_for_dot, 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)
    tl.store(c_tile_ptrs, accumulator.to(tl.bfloat16)) # No M or N masks for 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 else 1
    
    stride_c_m_dim = N 
    stride_c_n_dim = 1

    # --- Kernel Launch Parameters ---
    BLOCK_SIZE_M_val = 64
    BLOCK_SIZE_N_val = 64
    BLOCK_SIZE_K_COMMON_val = 128
    
    A_B_SCALE_K_GRANULARITY_val = 128
    B_SCALE_N_GRANULARITY_val = 128

    grid_m = M // BLOCK_SIZE_M_val # Exact division
    grid_n = N // BLOCK_SIZE_N_val # Exact division
    grid = (grid_m, grid_n)
    
    # --- Tuning Parameters ---
    # Shared mem per stage: A(64x128)=8KB, B_T(128x64)=8KB. Total = 16KB.
    # Max stages = floor(65536 / 16384) = 4.
    num_warps_val = 4
    num_stages_val = 4 # Using max possible stages

    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_COMMON=BLOCK_SIZE_K_COMMON_val,
        A_B_SCALE_K_GRANULARITY=A_B_SCALE_K_GRANULARITY_val,
        B_SCALE_N_GRANULARITY=B_SCALE_N_GRANULARITY_val,
        num_warps=num_warps_val,
        num_stages=num_stages_val
    )
    return c
python eval.py benchmark /tmp/tmpnrrwjo9t