From 02ca067bdf4f5b9660df1df233d7962983b46268 Mon Sep 17 00:00:00 2001 From: chenyu Date: Tue, 12 Mar 2024 11:58:20 -0400 Subject: [PATCH] use default_float.np to construct test data in test_ops (#3701) first step of #2797 --- test/test_ops.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/test/test_ops.py b/test/test_ops.py index 0af62f06fe..4913048509 100644 --- a/test/test_ops.py +++ b/test/test_ops.py @@ -61,10 +61,12 @@ def helper_test_op(shps, torch_fxn, tinygrad_fxn=None, atol=1e-6, rtol=1e-3, gra (shps, torch_fp*1000, tinygrad_fp*1000, torch_fbp*1000, tinygrad_fbp*1000), end="") def prepare_test_op(low, high, shps, vals, forward_only=False): - torch.manual_seed(0) - np.random.seed(0) - if shps is None: ts = [torch.tensor(x, requires_grad=(not forward_only)) for x in vals] - else: ts = [torch.tensor(np.random.uniform(low=low, high=high, size=x), requires_grad=(not forward_only), dtype=torch.float32) for x in shps] + if shps is None: + ts = [torch.tensor(x, requires_grad=(not forward_only)) for x in vals] + else: + np.random.seed(0) + np_data = [np.random.uniform(low=low, high=high, size=size).astype(dtypes.default_float.np) for size in shps] + ts = [torch.tensor(data, requires_grad=(not forward_only)) for data in np_data] tst = [Tensor(x.detach().numpy(), requires_grad=(not forward_only and not FORWARD_ONLY)) for x in ts] return ts, tst