mirror of
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
315 lines
10 KiB
Python
315 lines
10 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 caffe2.python import core, workspace
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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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from hypothesis import given
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from hypothesis import strategies as st
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import numpy as np
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import time
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class TestTensorPackOps(serial.SerializedTestCase):
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def pack_segments_ref(self, return_presence_mask=False, max_length=None):
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def pack_segments_ref(lengths, data, max_length=max_length):
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arr = []
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constant_values = 0
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if data.dtype.char == 'S':
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constant_values = ''
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if max_length is not None:
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assert(max_length > np.max(lengths))
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else:
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max_length = np.max(lengths)
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for idx in range(np.size(lengths)):
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chunk = data[np.sum(lengths[:idx]):np.sum(lengths[:idx + 1])]
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pad_length = max_length - lengths[idx]
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# ((0, pad_length), (0, 0)) says add pad_length rows of padding
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# below chunk and 0 rows of padding elsewhere
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arr.append(
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np.pad(
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chunk, ((0, pad_length), (0, 0)),
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mode=str("constant"),
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constant_values=constant_values
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)
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)
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result = [arr]
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if return_presence_mask:
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presence_arr = []
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for length in lengths:
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pad_length = max_length - length
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presence_arr.append(
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np.pad(
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np.ones((length), dtype=np.bool), ((0, pad_length)),
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mode=str("constant")
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)
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)
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result.append(presence_arr)
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return result
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return pack_segments_ref
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@serial.given(
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num_seq=st.integers(10, 100),
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cell_size=st.integers(1, 10),
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**hu.gcs
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)
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def test_pack_with_max_length_ops(self, num_seq, cell_size, gc, dc):
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# create data
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lengths = np.arange(num_seq, dtype=np.int32) + 1
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num_cell = np.sum(lengths)
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data = np.zeros(num_cell * cell_size, dtype=np.float32)
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left = np.cumsum(np.arange(num_seq) * cell_size)
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right = np.cumsum(lengths * cell_size)
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for i in range(num_seq):
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data[left[i]:right[i]] = i + 1.0
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data.resize(num_cell, cell_size)
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print("\nnum seq:{}, num cell: {}, cell size:{}\n".format(
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num_seq, num_cell, cell_size)
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+ "=" * 60
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)
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# run test
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max_length = num_seq + 1
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op = core.CreateOperator(
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'PackSegments', ['l', 'd'], ['t'], max_length=max_length)
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workspace.FeedBlob('l', lengths)
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workspace.FeedBlob('d', data)
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start = time.time()
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[lengths, data, max_length],
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reference=self.pack_segments_ref(max_length=max_length),
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)
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end = time.time()
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print("{} used time: {}".format(gc, end - start).replace('\n', ' '))
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with core.DeviceScope(gc):
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workspace.FeedBlob('l', lengths)
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workspace.FeedBlob('d', data)
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workspace.RunOperatorOnce(core.CreateOperator(
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'PackSegments',
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['l', 'd'],
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['t'],
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max_length=max_length,
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device_option=gc))
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workspace.RunOperatorOnce(core.CreateOperator(
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'UnpackSegments',
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['l', 't'],
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['newd'],
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max_length=max_length,
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device_option=gc))
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assert(workspace.FetchBlob('t').shape[1] == max_length)
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assert((workspace.FetchBlob('newd') == workspace.FetchBlob('d')).all())
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@given(
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num_seq=st.integers(10, 500),
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cell_size=st.integers(1, 10),
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**hu.gcs
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)
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def test_pack_ops(self, num_seq, cell_size, gc, dc):
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# create data
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lengths = np.arange(num_seq, dtype=np.int32) + 1
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num_cell = np.sum(lengths)
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data = np.zeros(num_cell * cell_size, dtype=np.float32)
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left = np.cumsum(np.arange(num_seq) * cell_size)
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right = np.cumsum(lengths * cell_size)
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for i in range(num_seq):
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data[left[i]:right[i]] = i + 1.0
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data.resize(num_cell, cell_size)
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print("\nnum seq:{}, num cell: {}, cell size:{}\n".format(
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num_seq, num_cell, cell_size)
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+ "=" * 60
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)
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# run test
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op = core.CreateOperator(
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'PackSegments', ['l', 'd'], ['t'])
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workspace.FeedBlob('l', lengths)
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workspace.FeedBlob('d', data)
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start = time.time()
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[lengths, data],
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reference=self.pack_segments_ref(),
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)
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end = time.time()
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print("{} used time: {}".format(gc, end - start).replace('\n', ' '))
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with core.DeviceScope(gc):
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workspace.FeedBlob('l', lengths)
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workspace.FeedBlob('d', data)
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workspace.RunOperatorOnce(core.CreateOperator(
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'PackSegments',
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['l', 'd'],
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['t'],
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device_option=gc))
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workspace.RunOperatorOnce(core.CreateOperator(
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'UnpackSegments',
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['l', 't'],
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['newd'],
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device_option=gc))
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assert((workspace.FetchBlob('newd') == workspace.FetchBlob('d')).all())
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@given(
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**hu.gcs_cpu_only
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)
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def test_pack_ops_str(self, gc, dc):
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# GPU does not support string. Test CPU implementation only.
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workspace.FeedBlob('l', np.array([1, 2, 3], dtype=np.int64))
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strs = np.array([
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["a", "a"],
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["b", "b"],
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["bb", "bb"],
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["c", "c"],
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["cc", "cc"],
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["ccc", "ccc"]],
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dtype='|S')
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workspace.FeedBlob('d', strs)
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workspace.RunOperatorOnce(core.CreateOperator(
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'PackSegments',
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['l', 'd'],
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['t'],
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device_option=gc))
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workspace.RunOperatorOnce(core.CreateOperator(
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'UnpackSegments',
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['l', 't'],
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['newd'],
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device_option=gc))
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assert((workspace.FetchBlob('newd') == workspace.FetchBlob('d')).all())
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def test_pad_minf(self):
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workspace.FeedBlob('l', np.array([1, 2, 3], dtype=np.int32))
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workspace.FeedBlob(
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'd',
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np.array([
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[1.0, 1.1],
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[2.0, 2.1],
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[2.2, 2.2],
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[3.0, 3.1],
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[3.2, 3.3],
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[3.4, 3.5]],
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dtype=np.float32))
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workspace.RunOperatorOnce(core.CreateOperator(
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'PackSegments', ['l', 'd'], ['t'], pad_minf=True))
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workspace.RunOperatorOnce(core.CreateOperator(
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'Exp', ['t'], ['r']
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))
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result = workspace.FetchBlob('t')
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assert(result[0, -1, 0] < -1000.0)
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# The whole point of padding with -inf is that when we exponentiate it
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# then it should be zero.
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exponentiated = workspace.FetchBlob('r')
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assert(exponentiated[0, -1, 0] == 0.0)
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@given(**hu.gcs_cpu_only)
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def test_presence_mask(self, gc, dc):
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lengths = np.array([1, 2, 3], dtype=np.int32)
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data = np.array(
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[
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[1.0, 1.0], [2.0, 2.0], [2.0, 2.0], [3.0, 3.0], [3.0, 3.0],
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[3.0, 3.0]
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],
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dtype=np.float32
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)
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op = core.CreateOperator(
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'PackSegments', ['l', 'd'], ['t', 'p'], return_presence_mask=True
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)
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workspace.FeedBlob('l', lengths)
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workspace.FeedBlob('d', data)
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inputs = [lengths, data]
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=inputs,
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reference=self.pack_segments_ref(return_presence_mask=True),
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)
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op = core.CreateOperator(
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'PackSegments', ['l', 'd'], ['t', 'p'], return_presence_mask=True
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)
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workspace.RunOperatorOnce(op)
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output = workspace.FetchBlob('t')
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expected_output_shape = (3, 3, 2)
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self.assertEquals(output.shape, expected_output_shape)
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presence_mask = workspace.FetchBlob('p')
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expected_presence_mask = np.array(
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[[True, False, False], [True, True, False], [True, True, True]],
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dtype=np.bool
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)
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self.assertEqual(presence_mask.shape, expected_presence_mask.shape)
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np.testing.assert_array_equal(presence_mask, expected_presence_mask)
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def test_presence_mask_empty(self):
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lengths = np.array([], dtype=np.int32)
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data = np.array([], dtype=np.float32)
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op = core.CreateOperator(
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'PackSegments', ['l', 'd'], ['t', 'p'], return_presence_mask=True
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)
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workspace.FeedBlob('l', lengths)
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workspace.FeedBlob('d', data)
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workspace.RunOperatorOnce(op)
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output = workspace.FetchBlob('p')
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expected_output_shape = (0, 0)
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self.assertEquals(output.shape, expected_output_shape)
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@given(**hu.gcs_cpu_only)
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def test_out_of_bounds(self, gc, dc):
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# Copy pasted from test_pack_ops but with 3 changed to 4
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lengths = np.array([1, 2, 4], dtype=np.int32)
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data = np.array([
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[1.0, 1.0],
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[2.0, 2.0],
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[2.0, 2.0],
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[3.0, 3.0],
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[3.0, 3.0],
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[3.0, 3.0]], dtype=np.float32)
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op = core.CreateOperator(
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'PackSegments', ['l', 'd'], ['t'])
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inputs = [lengths, data]
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self.assertRunOpRaises(
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device_option=gc,
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op=op,
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inputs=inputs,
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exception=RuntimeError
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)
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@given(**hu.gcs_cpu_only)
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def test_under_bounds(self, gc, dc):
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# Copy pasted from test_pack_ops but with 3 changed to 2
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lengths = np.array([1, 2, 2], dtype=np.int32)
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data = np.array([
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[1.0, 1.0],
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[2.0, 2.0],
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[2.0, 2.0],
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[3.0, 3.0],
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[3.0, 3.0],
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[3.0, 3.0]], dtype=np.float32)
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op = core.CreateOperator(
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'PackSegments', ['l', 'd'], ['t'])
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inputs = [lengths, data]
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self.assertRunOpRaises(
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device_option=gc,
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op=op,
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inputs=inputs,
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exception=RuntimeError
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)
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if __name__ == "__main__":
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import unittest
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unittest.main()
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