Summary:
There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports:
```2to3 -f future -w caffe2```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033
Reviewed By: seemethere
Differential Revision: D23808648
Pulled By: bugra
fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40379
The current sum operator doesn't support Long .. hence modify the code
Test Plan: Write a test case
Reviewed By: jspark1105, yinghai
Differential Revision: D21917365
fbshipit-source-id: b37d2c100c70d17d2f89c309e40360ddfab584ee
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33431
Some elementwise operators don't have shape and type inference specified for the output tensor: `BitwiseOr`, `BitwiseAnd`, `BitwiseXor`, `Not`, `Sign`.
This change fixes this issue:
- For `Not` and `Sign` operators, the output has the same type and shape as the input, so `IdenticalTypeAndShapeOfInput` function is used to specify that.
- For bitwise operators created by `CAFFE2_SCHEMA_FOR_BINARY_BITWISE_OP` macro, the type and shape inference rules should be the same as for other binary element-wise operators, so `TensorInferenceFunction(ElementwiseOpShapeInference)` is used to specify that.
Also some tests were modified to ensure that the shape and type are inferred (`ensure_outputs_are_inferred` parameter)
Test Plan:
```
CAFFE2_ASSERT_SHAPEINFERENCE=1 buck test caffe2/caffe2/python/operator_test:elementwise_ops_test
CAFFE2_ASSERT_SHAPEINFERENCE=1 buck test caffe2/caffe2/python/operator_test:math_ops_test
```
Note that the tests have to be executed with `CAFFE2_ASSERT_SHAPEINFERENCE=1` in order to fail upon shape inference failure.
Reviewed By: idning
Differential Revision: D19880164
fbshipit-source-id: 5d7902e045d79e5669e5e98dfb13a39711294939
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21927
Add `OUTPUT_PROB` output to CTCBeamSearchDecoderOp to return a probability for each sequence.
Add argument to output top-k instead of top-1 decoded sequences.
Reviewed By: SuperIRabbit
Differential Revision: D15797371
fbshipit-source-id: 737ca5cc4f90a0bcc3660ac9f58519a175977b69
Summary:
arguments order is okay to be different
ajyu
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18466
Differential Revision: D14627258
Pulled By: bddppq
fbshipit-source-id: 430e1fb1bea2c5639a547ae7c1652368788c86b9
Summary:
Add a markdown document summarizing the coverage of serialized operator tests. This currently only takes into account what has been covered by the tests with respect to the entire registry of c2 operators.
Next, we will break down the coverage by which operators have unit tests associated with them, which have hypothesis tests, and which have tests more specifically calling assertReferenceChecks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13703
Reviewed By: dzhulgakov
Differential Revision: D12970810
Pulled By: ajyu
fbshipit-source-id: 4f0cd057b1cf734371333e24d26cbab630a170e1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12021
TestPilot runs stress tests in parallel. These fail for serialized tests because extracting (and subsequent deletion) of binary data during the process isn't threadsafe. Extract zips into tempfile to avoid this problem.
Also remove some accidentally checked in zips of a test that we didn't end up including for now.
Reviewed By: houseroad
Differential Revision: D10013682
fbshipit-source-id: 6e13b850b38dee4106d3c10a9372747d17b67c5a
Summary:
Followup to [the serialized test framework](https://github.com/pytorch/pytorch/pull/10594)
Round 1 for refactoring tests, starting alphabetically. I added some functionality, so I wanted to send out some of these initial changes sooner.
I'm skipping all tests that don't explicitly call assertReferenceChecks. Some tests directly call np.allclose, and others are simply TestCase (rather than HypothesisTestCase).
1. Start alphabetically producing serialized outputs for test functions, annotating those we want to include with `serialized_test_util.given`. So far I've only added one test per operator, but this already does seem to add quite a few tests.
2. Add functionality to allow us to generate outputs using pytest by adding pytest argument options. This allows us to skip adding a `__main__` function to quite a few tests.
3. Catch any exceptions generating the gradient operator and skip serializing/reading it, since certain operators don't have gradients.
4. Add functionality to better handle jagged array inputs, which numpy doesn't handle very well. We simply explicitly do the conversion to dtype=object.
5. Make only one file per test function, rather than 4, to reduce the number of files in the github repo.
I also noticed that there is some hypothesis handling that makes `serialized_test_util.given` not compatible with adding more hypothesis decorators on top. For example, there are tests that do
```
settings(...)
given(...)
def test_my_stuff(...)
```
But there is a hypothesis handler that explicitly checks that `given` is called below `settings`, so we cannot refactor this to `serialized_test_util.given`. I've just avoided decorating these kinds of tests for now, I hope that's alright.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11350
Reviewed By: houseroad
Differential Revision: D9693857
Pulled By: ajyu
fbshipit-source-id: a9b4279afbe51c90cf2025c5ac6b2db2111f4af7
Summary:
Generate serialized test inputs/outputs/backward graphs of tests inside `caffe2/python/operator_test` that call assertSerializedOperatorCheck(). Tests should be decorated with serialized_test.collect_tests.given_and_seeded to run hypothesis tests that are actually random and a single fixed seeded hypothesis tests.
To use:
1. Refactor your test to be a SerializedTestCase
1a. Decorate it with given_and_seeded
1b. Call testWithArgs in main
2. Run your test with -g to generate the output. Check it in.
3. Subsequent runs of the test without generating the output will check against the checked in test case.
Details:
Run your test with `python caffe2/python/operator_test/[your_test].py -g`
Outputs are in `caffe2/python/serialized_test/data`. The operator tests outputs are in a further subdirectory `operator_test`, to allow for other tests in the future (model zoo tests?)
Currently, we've only refactored weighted_sum_test to use this, but in the next diff, we'll refactor as many as possible. The directory structure may also change as usually there are multiple tests in a single file, so we may create more structure to account for that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10594
Reviewed By: ezyang
Differential Revision: D9370359
Pulled By: ajyu
fbshipit-source-id: 2ce77389cd8bcc0255d3bccd61569833e545ede8
