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https://github.com/saymrwulf/pytorch.git
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98aebed88e
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
75 lines
3 KiB
Python
75 lines
3 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from hypothesis import assume, given
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import hypothesis.strategies as st
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import numpy as np
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from caffe2.proto import caffe2_pb2
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from caffe2.python import core
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.serialized_test.serialized_test_util as serial
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class TestDropout(serial.SerializedTestCase):
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@serial.given(X=hu.tensor(),
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in_place=st.booleans(),
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ratio=st.floats(0, 0.999),
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engine=st.sampled_from(["", "CUDNN"]),
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**hu.gcs)
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def test_dropout_is_test(self, X, in_place, ratio, engine, gc, dc):
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"""Test with is_test=True for a deterministic reference impl."""
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# TODO(lukeyeager): enable this path when the GPU path is fixed
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if in_place:
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# Skip if trying in-place on GPU
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assume(not (gc.device_type in {caffe2_pb2.CUDA, caffe2_pb2.HIP} and engine == ''))
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# If in-place on CPU, don't compare with GPU
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dc = dc[:1]
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op = core.CreateOperator("Dropout", ["X"],
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["X" if in_place else "Y"],
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ratio=ratio, engine=engine, is_test=True)
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self.assertDeviceChecks(dc, op, [X], [0])
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# No sense in checking gradients for test phase
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def reference_dropout_test(x):
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return x, np.ones(x.shape, dtype=np.bool)
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self.assertReferenceChecks(
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gc, op, [X], reference_dropout_test,
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# The 'mask' output may be uninitialized
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outputs_to_check=[0])
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@given(X=hu.tensor(),
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in_place=st.booleans(),
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output_mask=st.booleans(),
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engine=st.sampled_from(["", "CUDNN"]),
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**hu.gcs)
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def test_dropout_ratio0(self, X, in_place, output_mask, engine, gc, dc):
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"""Test with ratio=0 for a deterministic reference impl."""
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# TODO(lukeyeager): enable this path when the op is fixed
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if in_place:
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# Skip if trying in-place on GPU
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assume(gc.device_type not in {caffe2_pb2.CUDA, caffe2_pb2.HIP})
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# If in-place on CPU, don't compare with GPU
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dc = dc[:1]
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is_test = not output_mask
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op = core.CreateOperator("Dropout", ["X"],
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["X" if in_place else "Y"] +
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(["mask"] if output_mask else []),
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ratio=0.0, engine=engine,
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is_test=is_test)
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self.assertDeviceChecks(dc, op, [X], [0])
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if not is_test:
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self.assertGradientChecks(gc, op, [X], 0, [0])
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def reference_dropout_ratio0(x):
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return (x,) if is_test else (x, np.ones(x.shape, dtype=np.bool))
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self.assertReferenceChecks(
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gc, op, [X], reference_dropout_ratio0,
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# Don't check the mask with cuDNN because it's packed data
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outputs_to_check=None if engine != 'CUDNN' else [0])
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