| """signed-error / rounding-bias check of the custom GELU vs ttnn.gelu on fc1-like data.""" |
| import sys |
| from pathlib import Path |
| sys.path.insert(0, str(Path(__file__).resolve().parents[2])) |
| from common import * |
| from hamer import tt_kernels |
| dev = open_dev() |
| try: |
| L1 = ttnn.L1_MEMORY_CONFIG |
| torch.manual_seed(0) |
| xt = torch.randn(1, 1, 192, 5120) * 2.0 - 0.5 |
| x = ttnn.from_torch(xt, dtype=ttnn.bfloat16, layout=ttnn.TILE_LAYOUT, device=dev, memory_config=L1) |
| xb = xt.bfloat16().double() |
| ref = torch.nn.functional.gelu(xb) |
| ulp = lambda r: torch.where(r == 0, torch.full_like(r, 1e-30), 2.0 ** (torch.floor(torch.log2(r.abs().clamp_min(1e-30))) - 7)) |
| u = ulp(ref) |
| for name, t in [("custom", ttnn.to_torch(tt_kernels.gelu(x, memory_config=L1)).double()), |
| ("custom_bf16dst", ttnn.to_torch(tt_kernels.gelu(x, memory_config=L1, fp32_dest=False)).double()), |
| ("ttnn", ttnn.to_torch(ttnn.gelu(x, memory_config=L1)).double()), |
| ("rne(ref)", ref.bfloat16().double())]: |
| e = (t - ref) / u |
| m = ref.abs() > 1e-3 |
| print(f"{name:15s} signed err mean {float(e[m].mean()):+.4f} ulp |err| mean {float(e[m].abs().mean()):.4f} ulp max {float(e[m].abs().max()):.3f} sum(t-ref)/sum|ref| {float((t-ref).sum()/ref.abs().sum()):+.3e}", flush=True) |
| finally: |
| ttnn.close_device(dev) |
|
|
