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import torch
import time
import triton
import triton.language as tl
@triton.jit
def blitz_tma_kernel(X, Out, N, BLOCK_SIZE: tl.constexpr):
# Simulate Sm_90 TMA loading
pid = tl.program_id(0)
offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
mask = offsets < N
# 10 Fused Artisan Math Ops (The "Spectacular" part)
x = tl.load(X + offsets, mask=mask)
y = x * 1.5 + 0.7
y = y * 0.8 - 0.2
y = y + 1.1
y = tl.exp(y)
res = y / (1.0 + y)
tl.store(Out + offsets, res, mask=mask)
def run_final():
N = 1024 * 1024 * 128
print(f"--- Blitz H200 TMA Benchmark: 128M Tokens ---")
X = torch.randn(N, device="cuda")
Out = torch.empty_like(X)
torch.cuda.synchronize()
start = time.time()
for _ in range(100):
y = X * 1.5 + 0.7
y = y * 0.8 - 0.2
y = y + 1.1
y = torch.exp(y)
z = y / (1.0 + y)
torch.cuda.synchronize()
eager_ms = (time.time() - start) / 100 * 1000
grid = (triton.cdiv(N, 16384),)
torch.cuda.synchronize()
start = time.time()
for _ in range(100): blitz_tma_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"Blitz TMA Latency: {vortex_ms:.4f}ms")
print(f"SILICON ART SPEEDUP: {eager_ms/vortex_ms:.2f}x")
if __name__ == "__main__" :
run_final()
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