| import torch |
| import time |
| import triton |
| import triton.language as tl |
|
|
| @triton.jit |
| def vortex_monolith_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) |
| |
| |
| state = x |
| for _ in range(10): |
| state = tl.exp(state * 0.5) - 1.0 |
| state = tl.log(tl.abs(state) + 1.0) |
| state = state * 1.1 |
| |
| tl.store(Out + offsets, state, mask=mask) |
|
|
| def run_monolith(): |
| N = 1024 * 1024 * 64 |
| print("--- BLITZ VORTEX: THE 10X MONOLITH (H200) ---") |
| X = torch.randn(N, device="cuda") |
| Out = torch.empty_like(X) |
| |
| |
| torch.cuda.synchronize() |
| start = time.time() |
| for _ in range(10): |
| s = X |
| for j in range(10): |
| s = torch.exp(s * 0.5) - 1.0 |
| s = torch.log(torch.abs(s) + 1.0) |
| s = s * 1.1 |
| torch.cuda.synchronize() |
| eager_ms = (time.time() - start) / 10 * 1000 |
| |
| |
| grid = (triton.cdiv(N, 16384),) |
| torch.cuda.synchronize() |
| start = time.time() |
| for _ in range(10): vortex_monolith_kernel[grid](X, Out, N, BLOCK_SIZE=16384) |
| torch.cuda.synchronize() |
| vortex_ms = (time.time() - start) / 10 * 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_monolith() |
|
|