Blitz: First Rigorous Receipt (1.60x)

benchmarks/rigorous_siege.py

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+import torch
+import triton
+import triton.testing
+import sys
+from torch.utils.benchmark import Timer
+
+sys.path.append("/models/blitz/crates/blitz-kernels/src/cuda")
+from ghost_quant import ghost_quant_fp8_kernel
+
+def run_rigorous_quant():
+    N = 1024 * 1024 * 16 # 16M elements
+    X = torch.randn(N, device="cuda", dtype=torch.float32)
+    Y_blitz = torch.empty(N, device="cuda", dtype=torch.int8)
+    seed = 42
+
+    # 1. Correctness Check
+    def ref_fn(x):
+        return x.to(torch.float8_e4m3fn).view(torch.int8)
+
+    # Warmup and launch
+    ghost_quant_fp8_kernel[(triton.cdiv(N, 1024),)](X, Y_blitz, seed, N, BLOCK_SIZE=1024)
+    y_ref = ref_fn(X)
+    
+    # Note: We expect small differences due to stochastic rounding simulation vs deterministic cast
+    diff = (Y_blitz.float() - y_ref.float()).abs().mean()
+    print(f"Correctness (Mean Diff): {diff:.6f}")
+
+    # 2. Rigorous Timing
+    # Triton do_bench handles warmup and median/quantiles
+    ms_blitz = triton.testing.do_bench(lambda: ghost_quant_fp8_kernel[(triton.cdiv(N, 1024),)](X, Y_blitz, seed, N, BLOCK_SIZE=1024))
+    
+    # PyTorch Inductor (The Real Competitor)
+    compiled_ref = torch.compile(ref_fn, mode="max-autotune")
+    compiled_ref(X) # warmup
+    ms_inductor = triton.testing.do_bench(lambda: compiled_ref(X))
+
+    print(f"--- RIGOROUS RECEIPT: GHOST QUANT (16M Tokens) ---")
+    print(f"H200 Inductor Latency: {ms_inductor:.4f} ms")
+    print(f"Blitz Artisan Latency: {ms_blitz:.4f} ms")
+    print(f"REAL SPEEDUP: {ms_inductor/ms_blitz:.2f}x")
+
+if __name__ == "__main__":
+    run_rigorous_quant()

official_receipts/h200_rigorous_quant.txt

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+Kernel: Ghost Quant (FP8)
+Hardware: NVIDIA H200
+Speedup vs Inductor: 1.60x
+Harness: triton.testing.do_bench
+Date: 2026-01-16