benchmarks/vortex_unroll.py

import torch
import time
import triton
import triton.language as tl

@triton.jit
def vortex_unroll_kernel(X, Out, N, BLOCK_SIZE: tl.constexpr):
    pid = tl.program_id(0)
    offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    mask = offsets < N
    x = tl.load(X + offsets, mask=mask)
    
    # UNROLLED ARTISAN OPS (Sm_90 Register Persistent)
    y = x * 1.1 + 0.1
    y = y * 1.1 + 0.1
    y = y * 1.1 + 0.1
    y = y * 1.1 + 0.1
    y = y * 1.1 + 0.1
    y = y * 1.1 + 0.1
    y = y * 1.1 + 0.1
    y = y * 1.1 + 0.1
    y = y * 1.1 + 0.1
    y = y * 1.1 + 0.1
    
    tl.store(Out + offsets, y, mask=mask)

def run_unroll():
    N = 1024 * 1024 * 128
    print("--- BLITZ VORTEX: THE 10X UNROLL (H200) ---")
    X = torch.randn(N, device="cuda", dtype=torch.bfloat16)
    Out = torch.empty_like(X)
    
    # 1. Eager Baseline
    torch.cuda.synchronize()
    start = time.time()
    for _ in range(100):
        y = X * 1.1 + 0.1; y = y * 1.1 + 0.1; y = y * 1.1 + 0.1; y = y * 1.1 + 0.1; y = y * 1.1 + 0.1
        y = y * 1.1 + 0.1; y = y * 1.1 + 0.1; y = y * 1.1 + 0.1; y = y * 1.1 + 0.1; y = y * 1.1 + 0.1
    torch.cuda.synchronize()
    eager_ms = (time.time() - start) / 100 * 1000
    
    # 2. Artisan Unroll
    grid = (triton.cdiv(N, 16384),)
    torch.cuda.synchronize()
    start = time.time()
    for _ in range(100): vortex_unroll_kernel[grid](X, Out, N, BLOCK_SIZE=16384)
    torch.cuda.synchronize()
    vortex_ms = (time.time() - start) / 100 * 1000
    
    print(f"Eager Latency: {eager_ms:.4f}ms")
    print(f"Vortex Latency: {vortex_ms:.4f}ms")
    print(f"ARTISAN SPEEDUP: {eager_ms/vortex_ms:.2f}x")

if __name__ == "__main__":
    run_unroll()