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* different way to write torch backend * both backends * more work * simpler code * more work * test both * imply unwrap/wrap * FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add works * ready to start making test_ops work in torch backend * backward pass, TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add works * FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_simple_conv2d works * matmul backward is broken with as_strided
51 lines
1.2 KiB
Python
51 lines
1.2 KiB
Python
# simple tests
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import unittest
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import torch
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import numpy as np
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from tinygrad.helpers import getenv
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if getenv("TINY_BACKEND2"):
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import extra.torch_backend.backend2
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device = "cpu"
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else:
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import extra.torch_backend.backend
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device = "tiny"
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class TestTorchBackend(unittest.TestCase):
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def test_numpy_ones(self):
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a = torch.ones(4, device=device)
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np.testing.assert_equal(a.cpu().numpy(), [1,1,1,1])
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def test_numpy_ones(self):
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a = torch.ones(4, dtype=torch.int32, device=device)
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assert a.dtype == torch.int32
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np.testing.assert_equal(a.cpu().numpy(), [1,1,1,1])
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def test_plus(self):
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a = torch.ones(4, device=device)
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b = torch.ones(4, device=device)
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c = a+b
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np.testing.assert_equal(c.cpu().numpy(), [2,2,2,2])
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def test_exp2(qself):
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a = torch.ones(4, device=device)
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b = a.exp2()
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print(b)
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def test_eq(self):
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a = torch.ones(4, device=device)
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b = torch.ones(4, device=device)
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c = a == b
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print(c.cpu().numpy())
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def test_isfinite(self):
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a = torch.ones(4, device=device)
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np.testing.assert_equal(torch.isfinite(a).cpu().numpy(), [True, True, True, True])
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# TODO: why
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def test_str(self):
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a = torch.ones(4, device=device)
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print(str(a))
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if __name__ == "__main__":
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unittest.main()
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