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test_tiny cleanup (#13276)

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chenyu
2025-11-14 08:11:32 -08:00
committed by GitHub
parent 3e63831b98
commit 888aaab151
1 changed files with 4 additions and 6 deletions
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10
test/test_tiny.py
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@@ -32,8 +32,8 @@ class TestTiny(unittest.TestCase):
self.assertListEqual(out.tolist(), [2]*16)
def test_cat(self):
out = Tensor.cat(Tensor.ones(8).contiguous(), Tensor.ones(8).contiguous())
self.assertListEqual(out.tolist(), [1]*16)
out = Tensor.cat(Tensor.ones(8).contiguous(), Tensor.zeros(8).contiguous())
self.assertListEqual(out.tolist(), [1]*8+[0]*8)
def test_sum(self):
out = Tensor.ones(256).contiguous().sum()
@@ -62,7 +62,7 @@ class TestTiny(unittest.TestCase):
out = Tensor.rand(10)
for x in out.tolist():
self.assertGreaterEqual(x, 0.0)
self.assertLessEqual(x, 1.0)
self.assertLess(x, 1.0)
# *** JIT (for Python speed) ***
@@ -138,9 +138,7 @@ class TestTiny(unittest.TestCase):
nn.Conv2d(8, 8, 5), Tensor.relu]
# replace random weights with ones
# TODO: there's a bug here where it's tying two of the biases together. we need UNIQUE const
#Tensor.realize(*[p.replace(Tensor.ones_like(p).contiguous()) for p in nn.state.get_parameters(layers)])
for p in nn.state.get_parameters(layers): p.replace(Tensor.empty(p.shape))
Tensor.realize(*[p.replace(Tensor.ones_like(p).contiguous()) for p in nn.state.get_parameters(layers)])
# realize gradients
for x in nn.state.get_parameters(layers): x.requires_grad_()