wehub-resource-sync
130 lines
4.4 KiB
Python
130 lines
4.4 KiB
Python
# Copyright 2020 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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"""Tests for DLPack functions."""
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from absl.testing import parameterized
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import numpy as np
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from tensorflow.python.dlpack import dlpack
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from tensorflow.python.eager import context
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from tensorflow.python.framework import constant_op
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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.platform import test
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int_dtypes = [
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np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32,
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np.uint64
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]
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float_dtypes = [np.float16, np.float32, np.float64]
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complex_dtypes = [np.complex64, np.complex128]
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dlpack_dtypes = (
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int_dtypes + float_dtypes + [dtypes.bfloat16] + complex_dtypes + [np.bool_]
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)
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testcase_shapes = [(), (1,), (2, 3), (2, 0), (0, 7), (4, 1, 2)]
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def FormatShapeAndDtype(shape, dtype):
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return "_{}[{}]".format(str(dtype), ",".join(map(str, shape)))
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def GetNamedTestParameters():
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result = []
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for dtype in dlpack_dtypes:
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for shape in testcase_shapes:
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result.append({
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"testcase_name": FormatShapeAndDtype(shape, dtype),
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"dtype": dtype,
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"shape": shape
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})
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return result
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class DLPackTest(parameterized.TestCase, test.TestCase):
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@parameterized.named_parameters(GetNamedTestParameters())
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def testRoundTrip(self, dtype, shape):
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np.random.seed(42)
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if dtype == np.bool_:
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np_array = np.random.randint(0, 1, shape, np.bool_)
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else:
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np_array = np.random.randint(0, 10, shape)
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# copy to gpu if available
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tf_tensor = array_ops.identity(constant_op.constant(np_array, dtype=dtype))
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tf_tensor_device = tf_tensor.device
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tf_tensor_dtype = tf_tensor.dtype
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dlcapsule = dlpack.to_dlpack(tf_tensor)
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del tf_tensor # should still work
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tf_tensor2 = dlpack.from_dlpack(dlcapsule)
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self.assertAllClose(np_array, tf_tensor2)
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if tf_tensor_dtype == dtypes.int32:
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# int32 tensor is always on cpu for now
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self.assertEqual(tf_tensor2.device,
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"/job:localhost/replica:0/task:0/device:CPU:0")
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else:
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self.assertEqual(tf_tensor_device, tf_tensor2.device)
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def testRoundTripWithoutToDlpack(self):
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np_array = np.random.randint(0, 10, [42])
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self.assertAllEqual(
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np.from_dlpack(constant_op.constant(np_array).cpu()), np_array
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)
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def testTensorsCanBeConsumedOnceOnly(self):
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np.random.seed(42)
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np_array = np.random.randint(0, 10, (2, 3, 4))
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tf_tensor = constant_op.constant(np_array, dtype=np.float32)
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dlcapsule = dlpack.to_dlpack(tf_tensor)
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del tf_tensor # should still work
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_ = dlpack.from_dlpack(dlcapsule)
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def ConsumeDLPackTensor():
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dlpack.from_dlpack(dlcapsule) # Should can be consumed only once
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self.assertRaisesRegex(Exception,
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".*a DLPack tensor may be consumed at most once.*",
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ConsumeDLPackTensor)
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def testDLPackFromWithoutContextInitialization(self):
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tf_tensor = constant_op.constant(1)
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dlcapsule = dlpack.to_dlpack(tf_tensor)
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# Resetting the context doesn't cause an error.
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context._reset_context()
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_ = dlpack.from_dlpack(dlcapsule)
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def testUnsupportedTypeToDLPack(self):
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def UnsupportedQint16():
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tf_tensor = constant_op.constant([[1, 4], [5, 2]], dtype=dtypes.qint16)
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_ = dlpack.to_dlpack(tf_tensor)
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self.assertRaisesRegex(Exception, ".* is not supported by dlpack",
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UnsupportedQint16)
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def testMustPassTensorArgumentToDLPack(self):
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with self.assertRaisesRegex(
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errors.InvalidArgumentError,
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"The argument to `to_dlpack` must be a TF tensor, not Python object"):
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dlpack.to_dlpack([1])
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if __name__ == "__main__":
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ops.enable_eager_execution()
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test.main()
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