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https://github.com/zebrajr/pytorch.git
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9e1560821e
This PR adds refs for the following ops: - `torch.triu` - `torch.tril` - `torch.triu_indices` - `torch.tril_indices` - `torch.nn.functional.pairwise_distance` - `torch.nn.functional.pdist` It adds OpInfos for - `torch.triu_indices` - `torch.tril_indices` Note that these were already tested in `test/test_tensor_creation_ops.py` but for the ref tests we need the OpInfos. Finally, it improves documentation for PairwiseDistance and adds a missing import to `torch/testing/_internal/opinfo/core.py`. This started with an attempt to just add the `nn.functional` refs above, but it turned out that `pdist` was easiest to implement using `triu_indices` so I added that one and the related functions as well. ~~In the end, I changed the `pdist` implementation to not use `triu_indices` but kept the other refs.~~ Pull Request resolved: https://github.com/pytorch/pytorch/pull/82819 Approved by: https://github.com/ngimel
76 lines
2.9 KiB
Python
76 lines
2.9 KiB
Python
# Owner(s): ["oncall: jit"]
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import os
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import sys
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import torch
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# Make the helper files in test/ importable
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pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
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sys.path.append(pytorch_test_dir)
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from torch.testing._internal.jit_utils import JitTestCase
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if __name__ == '__main__':
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raise RuntimeError("This test file is not meant to be run directly, use:\n\n"
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"\tpython test/test_jit.py TESTNAME\n\n"
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"instead.")
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class TestTensorCreationOps(JitTestCase):
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"""
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A suite of tests for ops that create tensors.
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"""
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def test_randperm_default_dtype(self):
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def randperm(x: int):
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perm = torch.randperm(x)
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# Have to perform assertion here because TorchScript returns dtypes
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# as integers, which are not comparable against eager torch.dtype.
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assert perm.dtype == torch.int64
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self.checkScript(randperm, (3, ))
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def test_randperm_specifed_dtype(self):
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def randperm(x: int):
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perm = torch.randperm(x, dtype=torch.float)
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# Have to perform assertion here because TorchScript returns dtypes
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# as integers, which are not comparable against eager torch.dtype.
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assert perm.dtype == torch.float
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self.checkScript(randperm, (3, ))
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def test_triu_indices_default_dtype(self):
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def triu_indices(rows: int, cols: int):
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indices = torch.triu_indices(rows, cols)
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# Have to perform assertion here because TorchScript returns dtypes
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# as integers, which are not comparable against eager torch.dtype.
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assert indices.dtype == torch.int64
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self.checkScript(triu_indices, (3, 3))
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def test_triu_indices_specified_dtype(self):
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def triu_indices(rows: int, cols: int):
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indices = torch.triu_indices(rows, cols, dtype=torch.int32)
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# Have to perform assertion here because TorchScript returns dtypes
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# as integers, which are not comparable against eager torch.dtype.
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assert indices.dtype == torch.int32
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self.checkScript(triu_indices, (3, 3))
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def test_tril_indices_default_dtype(self):
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def tril_indices(rows: int, cols: int):
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indices = torch.tril_indices(rows, cols)
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# Have to perform assertion here because TorchScript returns dtypes
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# as integers, which are not comparable against eager torch.dtype.
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assert indices.dtype == torch.int64
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self.checkScript(tril_indices, (3, 3))
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def test_tril_indices_specified_dtype(self):
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def tril_indices(rows: int, cols: int):
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indices = torch.tril_indices(rows, cols, dtype=torch.int32)
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# Have to perform assertion here because TorchScript returns dtypes
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# as integers, which are not comparable against eager torch.dtype.
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assert indices.dtype == torch.int32
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self.checkScript(tril_indices, (3, 3))
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