submission 66209

import torch
import triton
import triton.language as tl
 
 
@triton.autotune(
    configs=[
        triton.Config({'BLOCK_SIZE': 256}, num_warps=2, num_stages=2),
        triton.Config({'BLOCK_SIZE': 512}, num_warps=2, num_stages=2),
        triton.Config({'BLOCK_SIZE': 1024}, num_warps=4, num_stages=2),
        triton.Config({'BLOCK_SIZE': 2048}, num_warps=4, num_stages=2),
        triton.Config({'BLOCK_SIZE': 2048}, num_warps=8, num_stages=2),
        triton.Config({'BLOCK_SIZE': 4096}, num_warps=8, num_stages=2),
        triton.Config({'BLOCK_SIZE': 4096}, num_warps=8, num_stages=3),
    ],
    key=['N'],
)
@triton.jit
def vectoradd_kernel(
    a_ptr,
    b_ptr,
    c_ptr,
    N,
    BLOCK_SIZE: tl.constexpr,
):
    """
    Ultra-optimized float16 vector addition for B200.
    Uses autotuning for optimal configuration.
    """
    pid = tl.program_id(0)
    block_start = pid * BLOCK_SIZE
    offsets = block_start + tl.arange(0, BLOCK_SIZE)
    mask = offsets < N
    # Load with cache eviction hints optimized for B200
    a = tl.load(a_ptr + offsets, mask=mask, other=0.0, eviction_policy='evict_last')
    b = tl.load(b_ptr + offsets, mask=mask, other=0.0, eviction_policy='evict_last')
    # Perform addition
    c = a + b
    # Store with write-back policy
    tl.store(c_ptr + offsets, c, mask=mask, eviction_policy='evict_last')
 
 
def custom_kernel(data):
    """
    Main kernel with autotuning for peak B200 performance.
    """
    A, B, C = data
    # Ensure contiguous layout
    A = A.contiguous()
    B = B.contiguous()
    C = C.contiguous()
    N = A.numel()
    # Grid configuration - will be optimized by autotune
    grid = lambda meta: (triton.cdiv(N, meta['BLOCK_SIZE']),)
    # Launch autotuned kernel
    vectoradd_kernel[grid](A, B, C, N)
    return C