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Reducing memory footprint of one_hot for Large Array Testing #16136
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@mxnet-label-bot add [pr-awaiting-review] |
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@apeforest @ChaiBapchya PR is ready for review |
apeforest
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Sep 10, 2019
tests/nightly/test_large_vector.py
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| a[0] = 1 | ||
| a[-1] = 1 | ||
| #default dtype of ndarray is float32 which cannot index elements over 2^32 | ||
| a = nd.array([1], dtype=np.int64) |
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maybe you don't even need this array a?
just do a = nd.one_hot([1], LARGE_X)
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it still expects numpy array and errors out AssertionError: Argument indices must have NDArray type cannot avoid that. Also, it keeps default dtype as np.float32 so we have to explicitly give dtype as int64
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Actually, after checking test_large_array.py, I think this test is redundant. We can remove it from test_large_vector. |
access2rohit
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…ncorporate large vector part of the test
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Sep 11, 2019
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…16136) * reducing memory footprint of one_hot for Large Tensor * removing one_hot from large_vector and changing large_array test to incorporate large vector part of the test
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…16136) * reducing memory footprint of one_hot for Large Tensor * removing one_hot from large_vector and changing large_array test to incorporate large vector part of the test
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…16136) * reducing memory footprint of one_hot for Large Tensor * removing one_hot from large_vector and changing large_array test to incorporate large vector part of the test
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…16136) * reducing memory footprint of one_hot for Large Tensor * removing one_hot from large_vector and changing large_array test to incorporate large vector part of the test
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Description
Using 2 element one hot location NDarray to ensure
one_hotoperator is not using too much memory.Checklist
Essentials
Please feel free to remove inapplicable items for your PR.
Testing
Currently running full suite of all tests in test_large_array.py