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df86672bd4
In addition to div, any ops that will generate non-zero outputs from zero inputs need to be guarded.
48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
#!/usr/bin/env python
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import numpy as np
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import unittest
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from tinygrad.lazy import LazyBuffer
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from tinygrad.tensor import Tensor
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class TestLazyBuffer(unittest.TestCase):
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def test_fromcpu_buffer_sharing(self):
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a = np.arange(8)
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assert LazyBuffer.fromCPU(a).realized._buf is a
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def test_fromcpu_shape_tracker(self):
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def helper(a: np.ndarray):
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print(a.shape, a.strides, a.flags.c_contiguous)
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b = LazyBuffer.fromCPU(a).realize()
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assert b.st.contiguous == a.flags.c_contiguous
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assert b.st.shape == a.shape
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np.testing.assert_equal(a, b.toCPU())
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for ndims in range(1, 4):
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a = np.random.randn(*(4,)*ndims).astype(np.float32)
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for stride in [-2, 1, 2]:
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for start in [0, 1]:
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helper(a[(slice(start, None, stride),)*ndims])
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def test_shuffle_pad_ops_cmpeq(self):
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y = Tensor([1]).cat(Tensor([1]).eq(0)).numpy()
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z = Tensor([1, 0]).numpy()
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np.testing.assert_allclose(y, z)
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def test_shuffle_pad_ops_div(self):
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y = Tensor([1]).cat(Tensor([1]).div(Tensor([2.0]))).numpy()
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z = Tensor([1, 0.5]).numpy()
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np.testing.assert_allclose(y, z)
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def test_shuffle_pad_ops_log(self):
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y = Tensor([1]).cat(Tensor([1]).log()).numpy()
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z = Tensor([1, 0]).numpy()
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np.testing.assert_allclose(y, z)
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def test_shuffle_pad_ops_exp(self):
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y = Tensor([1]).cat(Tensor([1]).exp()).numpy()
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z = Tensor([1, np.e]).numpy()
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np.testing.assert_allclose(y, z)
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if __name__ == "__main__":
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unittest.main()
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