6762ca15c4
The session returned by cached_session uses soft placement, something we don't want for XLA_* devices. With soft placement ops lacking XLA kernels silently fall back and run on the CPU, misleading us into thinking we have more test coverage than we actually do. With this test some tests (rightly) start failing because they were testing ops with dtypes the XLA kernels do not support. I've removed these dtypes from the tests. This CL partially addresses b/132430685. It stubs out "cached_session" and "test_session" to raise errors, so we have more confidence that the compiler is being exercised. However, we still use XLA_* devices to exercise XLA, which has a different code path than xla.compile and tpu.rewrite. This needs to be incrementally fixed. PiperOrigin-RevId: 248437673
284 lines
12 KiB
Python
284 lines
12 KiB
Python
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Test cases for operators with > 3 or arbitrary numbers of arguments."""
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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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import unittest
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import numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.python.framework import dtypes
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import googletest
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class NAryOpsTest(xla_test.XLATestCase):
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def _testNAry(self, op, args, expected, equality_fn=None):
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with self.session() as session:
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with self.test_scope():
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placeholders = [
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array_ops.placeholder(dtypes.as_dtype(arg.dtype), arg.shape)
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for arg in args
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]
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feeds = {placeholders[i]: args[i] for i in range(0, len(args))}
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output = op(placeholders)
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result = session.run(output, feeds)
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if not equality_fn:
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equality_fn = self.assertAllClose
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equality_fn(result, expected, rtol=1e-3)
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def _nAryListCheck(self, results, expected, **kwargs):
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self.assertEqual(len(results), len(expected))
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for (r, e) in zip(results, expected):
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self.assertAllClose(r, e, **kwargs)
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def _testNAryLists(self, op, args, expected):
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self._testNAry(op, args, expected, equality_fn=self._nAryListCheck)
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def testFloat(self):
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self._testNAry(math_ops.add_n,
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[np.array([[1, 2, 3]], dtype=np.float32)],
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expected=np.array([[1, 2, 3]], dtype=np.float32))
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self._testNAry(math_ops.add_n,
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[np.array([1, 2], dtype=np.float32),
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np.array([10, 20], dtype=np.float32)],
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expected=np.array([11, 22], dtype=np.float32))
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self._testNAry(math_ops.add_n,
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[np.array([-4], dtype=np.float32),
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np.array([10], dtype=np.float32),
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np.array([42], dtype=np.float32)],
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expected=np.array([48], dtype=np.float32))
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def testComplex(self):
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for dtype in self.complex_types:
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self._testNAry(
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math_ops.add_n, [np.array([[1 + 2j, 2 - 3j, 3 + 4j]], dtype=dtype)],
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expected=np.array([[1 + 2j, 2 - 3j, 3 + 4j]], dtype=dtype))
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self._testNAry(
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math_ops.add_n, [
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np.array([1 + 2j, 2 - 3j], dtype=dtype),
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np.array([10j, 20], dtype=dtype)
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],
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expected=np.array([1 + 12j, 22 - 3j], dtype=dtype))
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self._testNAry(
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math_ops.add_n, [
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np.array([-4, 5j], dtype=dtype),
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np.array([2 + 10j, -2], dtype=dtype),
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np.array([42j, 3 + 3j], dtype=dtype)
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],
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expected=np.array([-2 + 52j, 1 + 8j], dtype=dtype))
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@unittest.skip("IdentityN is temporarily CompilationOnly as workaround")
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def testIdentityN(self):
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self._testNAryLists(array_ops.identity_n,
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[np.array([[1, 2, 3]], dtype=np.float32)],
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expected=[np.array([[1, 2, 3]], dtype=np.float32)])
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self._testNAryLists(array_ops.identity_n,
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[np.array([[1, 2], [3, 4]], dtype=np.float32),
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np.array([[3, 2, 1], [6, 5, 1]], dtype=np.float32)],
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expected=[
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np.array([[1, 2], [3, 4]], dtype=np.float32),
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np.array([[3, 2, 1], [6, 5, 1]], dtype=np.float32)])
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self._testNAryLists(array_ops.identity_n,
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[np.array([[1], [2], [3], [4]], dtype=np.int32),
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np.array([[3, 2, 1], [6, 5, 1]], dtype=np.float32)],
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expected=[
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np.array([[1], [2], [3], [4]], dtype=np.int32),
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np.array([[3, 2, 1], [6, 5, 1]], dtype=np.float32)])
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def testConcat(self):
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self._testNAry(
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lambda x: array_ops.concat(x, 0), [
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np.array(
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[[1, 2, 3], [4, 5, 6]], dtype=np.float32), np.array(
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[[7, 8, 9], [10, 11, 12]], dtype=np.float32)
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],
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expected=np.array(
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[[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], dtype=np.float32))
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self._testNAry(
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lambda x: array_ops.concat(x, 1), [
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np.array(
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[[1, 2, 3], [4, 5, 6]], dtype=np.float32), np.array(
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[[7, 8, 9], [10, 11, 12]], dtype=np.float32)
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],
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expected=np.array(
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[[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]], dtype=np.float32))
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def testOneHot(self):
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with self.session() as session, self.test_scope():
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indices = array_ops.constant(np.array([[2, 3], [0, 1]], dtype=np.int32))
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op = array_ops.one_hot(indices,
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np.int32(4),
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on_value=np.float32(7), off_value=np.float32(3))
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output = session.run(op)
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expected = np.array([[[3, 3, 7, 3], [3, 3, 3, 7]],
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[[7, 3, 3, 3], [3, 7, 3, 3]]],
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dtype=np.float32)
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self.assertAllEqual(output, expected)
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op = array_ops.one_hot(indices,
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np.int32(4),
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on_value=np.int32(2), off_value=np.int32(1),
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axis=1)
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output = session.run(op)
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expected = np.array([[[1, 1], [1, 1], [2, 1], [1, 2]],
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[[2, 1], [1, 2], [1, 1], [1, 1]]],
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dtype=np.int32)
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self.assertAllEqual(output, expected)
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def testSplitV(self):
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with self.session() as session:
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with self.test_scope():
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output = session.run(
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array_ops.split(np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 0, 1, 2]],
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dtype=np.float32),
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[2, 2], 1))
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expected = [np.array([[1, 2], [5, 6], [9, 0]], dtype=np.float32),
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np.array([[3, 4], [7, 8], [1, 2]], dtype=np.float32)]
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self.assertAllEqual(output, expected)
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def testStridedSlice(self):
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self._testNAry(lambda x: array_ops.strided_slice(*x),
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[np.array([[], [], []], dtype=np.float32),
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np.array([1, 0], dtype=np.int32),
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np.array([3, 0], dtype=np.int32),
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np.array([1, 1], dtype=np.int32)],
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expected=np.array([[], []], dtype=np.float32))
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if np.int64 in self.int_types:
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self._testNAry(
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lambda x: array_ops.strided_slice(*x), [
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np.array([[], [], []], dtype=np.float32), np.array(
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[1, 0], dtype=np.int64), np.array([3, 0], dtype=np.int64),
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np.array([1, 1], dtype=np.int64)
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],
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expected=np.array([[], []], dtype=np.float32))
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self._testNAry(lambda x: array_ops.strided_slice(*x),
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[np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
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dtype=np.float32),
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np.array([1, 1], dtype=np.int32),
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np.array([3, 3], dtype=np.int32),
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np.array([1, 1], dtype=np.int32)],
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expected=np.array([[5, 6], [8, 9]], dtype=np.float32))
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self._testNAry(lambda x: array_ops.strided_slice(*x),
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[np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
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dtype=np.float32),
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np.array([0, 2], dtype=np.int32),
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np.array([2, 0], dtype=np.int32),
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np.array([1, -1], dtype=np.int32)],
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expected=np.array([[3, 2], [6, 5]], dtype=np.float32))
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self._testNAry(lambda x: x[0][0:2, array_ops.newaxis, ::-1],
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[np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
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dtype=np.float32)],
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expected=np.array([[[3, 2, 1]], [[6, 5, 4]]],
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dtype=np.float32))
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self._testNAry(lambda x: x[0][1, :, array_ops.newaxis],
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[np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
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dtype=np.float32)],
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expected=np.array([[4], [5], [6]], dtype=np.float32))
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def testStridedSliceGrad(self):
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# Tests cases where input shape is empty.
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self._testNAry(lambda x: array_ops.strided_slice_grad(*x),
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[np.array([], dtype=np.int32),
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np.array([], dtype=np.int32),
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np.array([], dtype=np.int32),
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np.array([], dtype=np.int32),
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np.float32(0.5)],
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expected=np.array(np.float32(0.5), dtype=np.float32))
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# Tests case where input shape is non-empty, but gradients are empty.
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self._testNAry(lambda x: array_ops.strided_slice_grad(*x),
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[np.array([3], dtype=np.int32),
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np.array([0], dtype=np.int32),
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np.array([0], dtype=np.int32),
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np.array([1], dtype=np.int32),
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np.array([], dtype=np.float32)],
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expected=np.array([0, 0, 0], dtype=np.float32))
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self._testNAry(lambda x: array_ops.strided_slice_grad(*x),
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[np.array([3, 0], dtype=np.int32),
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np.array([1, 0], dtype=np.int32),
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np.array([3, 0], dtype=np.int32),
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np.array([1, 1], dtype=np.int32),
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np.array([[], []], dtype=np.float32)],
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expected=np.array([[], [], []], dtype=np.float32))
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self._testNAry(lambda x: array_ops.strided_slice_grad(*x),
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[np.array([3, 3], dtype=np.int32),
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np.array([1, 1], dtype=np.int32),
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np.array([3, 3], dtype=np.int32),
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np.array([1, 1], dtype=np.int32),
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np.array([[5, 6], [8, 9]], dtype=np.float32)],
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expected=np.array([[0, 0, 0], [0, 5, 6], [0, 8, 9]],
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dtype=np.float32))
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def ssg_test(x):
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return array_ops.strided_slice_grad(*x, shrink_axis_mask=0x4,
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new_axis_mask=0x1)
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self._testNAry(ssg_test,
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[np.array([3, 1, 3], dtype=np.int32),
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np.array([0, 0, 0, 2], dtype=np.int32),
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np.array([0, 3, 1, -4], dtype=np.int32),
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np.array([1, 2, 1, -3], dtype=np.int32),
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np.array([[[1], [2]]], dtype=np.float32)],
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expected=np.array([[[0, 0, 1]], [[0, 0, 0]], [[0, 0, 2]]],
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dtype=np.float32))
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ssg_test2 = lambda x: array_ops.strided_slice_grad(*x, new_axis_mask=0x15)
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self._testNAry(ssg_test2,
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[np.array([4, 4], dtype=np.int32),
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np.array([0, 0, 0, 1, 0], dtype=np.int32),
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np.array([0, 3, 0, 4, 0], dtype=np.int32),
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np.array([1, 2, 1, 2, 1], dtype=np.int32),
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np.array([[[[[1], [2]]], [[[3], [4]]]]], dtype=np.float32)],
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expected=np.array([[0, 1, 0, 2], [0, 0, 0, 0], [0, 3, 0, 4],
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[0, 0, 0, 0]], dtype=np.float32))
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self._testNAry(lambda x: array_ops.strided_slice_grad(*x),
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[np.array([3, 3], dtype=np.int32),
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np.array([0, 2], dtype=np.int32),
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np.array([2, 0], dtype=np.int32),
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np.array([1, -1], dtype=np.int32),
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np.array([[1, 2], [3, 4]], dtype=np.float32)],
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expected=np.array([[0, 2, 1], [0, 4, 3], [0, 0, 0]],
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dtype=np.float32))
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self._testNAry(lambda x: array_ops.strided_slice_grad(*x),
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[np.array([3, 3], dtype=np.int32),
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np.array([2, 2], dtype=np.int32),
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np.array([0, 1], dtype=np.int32),
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np.array([-1, -2], dtype=np.int32),
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np.array([[1], [2]], dtype=np.float32)],
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expected=np.array([[0, 0, 0], [0, 0, 2], [0, 0, 1]],
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dtype=np.float32))
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if __name__ == "__main__":
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googletest.main()
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