caffe2/python/serialized_test/README.md - platform/external/pytorch - Git at Google

 # Serialized operator test framework

 Major functionality lives in `serialized_test_util.py`

 ## How to use
 1. Extend the test case class from `SerializedTestCase`
 2. Change the `@given` decorator to `@serialized_test_util.given`. This runs a seeded hypothesis test instance which will generate outputs if desired in addition to the unseeded hypothesis tests normally run.
 3. [Optional] Add (or change a call of `unittest.main()` to) `testWithArgs` in `__main__`. This allows you to generate outputs using `python caffe2/python/operator_test/my_test.py -G`.
 4.  Run your test `python -m pytest caffe2/python/operator_test/my_test.py -G` to generate serialized outputs. They will live in `caffe2/python/serialized_test/data/operator_test`, one zip file per test function. The zip file contains an `inout.npz` file of the inputs, outputs, and meta data (like device type), a `op.pb` file of the operator, and `grad_#.pb` files of the gradients if there are any. Use `-O` to change the output directory.
 5. Thereafter, runs of the test without the flag will load serialized outputs and gradient operators for comparison against the seeded run. The comparison is done as long as you have a call to assertReferenceChecks. If for any reason the seeded run's inputs are different (this can happen with different hypothesis versions or different setups), then we'll run the serialized inputs through the serialized operator to get a runtime output for comparison.

 ##Additional Notes

 If we'd like to extend the test framework beyond that for operator tests, we can create a new subfolder for them inside `caffe2/python/serialized_test/data`.

 Note, we currently don't support using other hypothesis decorators on top of `given_and_seeded`. Hypothis has some handling to explicitly check that `@given` is on the bottom of the decorator stack.

 If there are multiple calls to assertReferenceChecks in a test function, we'll serialize and write the last one. The actual input checked may then differ if we refactor a test function that calls this multiple times, though the serialized test should still pass since we then use the serialized input to generate a dynamic output.
	# Serialized operator test framework

	Major functionality lives in `serialized_test_util.py`

	## How to use
	1. Extend the test case class from `SerializedTestCase`
	2. Change the `@given` decorator to `@serialized_test_util.given`. This runs a seeded hypothesis test instance which will generate outputs if desired in addition to the unseeded hypothesis tests normally run.
	3. [Optional] Add (or change a call of `unittest.main()` to) `testWithArgs` in `__main__`. This allows you to generate outputs using `python caffe2/python/operator_test/my_test.py -G`.
	4. Run your test `python -m pytest caffe2/python/operator_test/my_test.py -G` to generate serialized outputs. They will live in `caffe2/python/serialized_test/data/operator_test`, one zip file per test function. The zip file contains an `inout.npz` file of the inputs, outputs, and meta data (like device type), a `op.pb` file of the operator, and `grad_#.pb` files of the gradients if there are any. Use `-O` to change the output directory.
	5. Thereafter, runs of the test without the flag will load serialized outputs and gradient operators for comparison against the seeded run. The comparison is done as long as you have a call to assertReferenceChecks. If for any reason the seeded run's inputs are different (this can happen with different hypothesis versions or different setups), then we'll run the serialized inputs through the serialized operator to get a runtime output for comparison.

	##Additional Notes

	If we'd like to extend the test framework beyond that for operator tests, we can create a new subfolder for them inside `caffe2/python/serialized_test/data`.

	Note, we currently don't support using other hypothesis decorators on top of `given_and_seeded`. Hypothis has some handling to explicitly check that `@given` is on the bottom of the decorator stack.

	If there are multiple calls to assertReferenceChecks in a test function, we'll serialize and write the last one. The actual input checked may then differ if we refactor a test function that calls this multiple times, though the serialized test should still pass since we then use the serialized input to generate a dynamic output.