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
89 lines
3.3 KiB
Python
89 lines
3.3 KiB
Python
# Copyright 2018 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 XLA devices."""
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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 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.framework import errors
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gen_control_flow_ops
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from tensorflow.python.platform import test
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class XlaDeviceTest(xla_test.XLATestCase):
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def testCopies(self):
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"""Tests that copies onto and off XLA devices work."""
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shapes = [[0], [1], [1, 0], [1024, 0], [1024, 1], [3, 777], [777, 3],
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[16384, 1], [1, 16384], [1, 20000, 1, 1]]
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for dtype in self.numeric_types:
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for shape in shapes:
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with self.session() as sess:
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with ops.device("CPU"):
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x = array_ops.placeholder(dtype, shape)
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with self.test_scope():
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y = x + x
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with ops.device("CPU"):
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z = array_ops.identity(y)
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inputs = np.random.randint(-100, 100, shape).astype(dtype)
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result = sess.run(z, {x: inputs})
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self.assertAllCloseAccordingToType(result, inputs + inputs)
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def testCopiesOfUnsupportedTypesFailGracefully(self):
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"""Tests that copies of unsupported types don't crash."""
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test_types = set([
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np.uint8, np.uint16, np.uint32, np.uint64, np.int8, np.int16, np.int32,
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np.int64, np.float16, np.float32, np.float16,
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dtypes.bfloat16.as_numpy_dtype
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])
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shape = (10, 10)
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for unsupported_dtype in test_types - self.all_types:
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with self.session() as sess:
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with ops.device("CPU"):
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x = array_ops.placeholder(unsupported_dtype, shape)
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with self.test_scope():
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y, = array_ops.identity_n([x])
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with ops.device("CPU"):
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z = array_ops.identity(y)
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inputs = np.random.randint(-100, 100, shape)
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inputs = inputs.astype(unsupported_dtype)
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# Execution should either succeed or raise an InvalidArgumentError,
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# but not crash. Even "unsupported types" may succeed here since some
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# backends (e.g., the CPU backend) are happy to handle buffers of
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# unsupported types, even if they cannot compute with them.
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try:
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sess.run(z, {x: inputs})
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except errors.InvalidArgumentError:
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pass
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def testControlTrigger(self):
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with self.session() as sess:
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with self.test_scope():
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x = gen_control_flow_ops.control_trigger()
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self.evaluate(x)
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if __name__ == "__main__":
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test.main()
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