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use default_float.np to construct test data in test_ops (#3701)

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first step of #2797
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chenyu
2024-03-12 11:58:20 -04:00
committed by GitHub
parent 6755a9254f
commit 02ca067bdf
1 changed files with 6 additions and 4 deletions
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10
test/test_ops.py
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@@ -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