Utilities for deterministic unit tests #497
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Summary: A longstanding pain point of ours has been writing unit tests that (a) are nontrivial enough to catch any regressions, and (b) don't break every time we add a new test case, change model initialization order, or perform other superficial refactors. Random initialization usually passes (a) but fails (b), while deterministic initialization ([e.g.](https://github.com/facebookresearch/multimodal/blob/2ddb8cdb205f2035e88e4fafb7e88cccb7b99705/tests/test_utils.py#L192)) does the opposite. This diff introduces a utility for constructing deterministic but nontrivial tensors of any shape, with flexibility so the user can determine the tensor's range. As an easy extension, we also add a utility to initialize nn.Module parameters in the same way. Differential Revision: D50251029
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Summary: A longstanding pain point of ours has been writing unit tests that (a) are nontrivial enough to catch any regressions, and (b) don't break every time we add a new test case, change model initialization order, or perform other superficial refactors. Random initialization usually passes (a) but fails (b), while deterministic initialization ([e.g.](https://github.com/facebookresearch/multimodal/blob/2ddb8cdb205f2035e88e4fafb7e88cccb7b99705/tests/test_utils.py#L192)) does the opposite. This diff introduces a utility for constructing deterministic but nontrivial tensors of any shape, with flexibility so the user can determine the tensor's range. As an easy extension, we also add a utility to initialize nn.Module parameters in the same way. Differential Revision: D50251029
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Summary:
A longstanding pain point of ours has been writing unit tests that
(a) are nontrivial enough to catch any regressions, and
(b) don't break every time we add a new test case, change model initialization order, or perform other superficial refactors.
Random initialization usually passes (a) but fails (b), while deterministic initialization (e.g.) does the opposite.
This diff introduces a utility for constructing deterministic but nontrivial tensors of any shape, with flexibility so the user can determine the tensor's range. As an easy extension, we also add a utility to initialize nn.Module parameters in the same way.
Differential Revision: D50251029