64e1b489bb
Usually, flex delegate is enabled by symbol override of AcquireFlexDelegate() function. But this approach doesn't work well with shared library. Since pywrap_tensorflow_internal.so is available for tensorflow PIP, I've made the following changes to enable flex delegate. - Included flex delegate module to the pywrap_tensorflow_internal.so. This file already contains most TF internal logic and having TFLite flex delegate impacts about 72K to the output. - Added new function of TF_AcquireFlexDelegate() in the delegate module. - Updated logic in AcquireFlexDelegate() of interpreter_builder.cc to check the availability of pywrap_tensorflow_internal.so and lookup the TF_AcquireFlexDelegate() symbol to enable flex delegate. Also updated python/lite_flex_test.py since flex delegate is supported with Python API PiperOrigin-RevId: 317044994 Change-Id: Ic5e953f4a675b3f5360a4c7d607568193103711a
140 lines
5.7 KiB
Python
140 lines
5.7 KiB
Python
# Copyright 2019 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 lite.py functionality related to select TF op usage."""
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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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from absl.testing import parameterized
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import numpy as np
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from tensorflow.lite.python import lite
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from tensorflow.lite.python.interpreter import Interpreter
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from tensorflow.python.client import session
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from tensorflow.python.eager import def_function
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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 ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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from tensorflow.python.training.tracking import tracking
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class FromSessionTest(test_util.TensorFlowTestCase, parameterized.TestCase):
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@parameterized.named_parameters(
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('EnableMlirConverter', True), # enable mlir
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('DisableMlirConverter', False)) # disable mlir
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def testFlexMode(self, enable_mlir):
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with ops.Graph().as_default():
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in_tensor = array_ops.placeholder(shape=[1, 4], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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[out_tensor])
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converter.target_spec.supported_ops = set([lite.OpsSet.SELECT_TF_OPS])
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converter.experimental_new_converter = enable_mlir
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tflite_model = converter.convert()
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self.assertTrue(tflite_model)
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# Check the model works with TensorFlow ops.
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interpreter = Interpreter(model_content=tflite_model)
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interpreter.allocate_tensors()
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input_details = interpreter.get_input_details()
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test_input = np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32)
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interpreter.set_tensor(input_details[0]['index'], test_input)
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interpreter.invoke()
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output_details = interpreter.get_output_details()
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expected_output = np.array([[2.0, 4.0, 6.0, 8.0]], dtype=np.float32)
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output_data = interpreter.get_tensor(output_details[0]['index'])
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self.assertTrue((expected_output == output_data).all())
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def testDeprecatedFlags(self):
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with ops.Graph().as_default():
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in_tensor = array_ops.placeholder(shape=[1, 4], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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[out_tensor])
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converter.target_ops = set([lite.OpsSet.SELECT_TF_OPS])
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# Ensure `target_ops` is set to the correct value after flag deprecation.
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self.assertEqual(converter.target_ops, set([lite.OpsSet.SELECT_TF_OPS]))
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self.assertEqual(converter.target_spec.supported_ops,
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set([lite.OpsSet.SELECT_TF_OPS]))
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tflite_model = converter.convert()
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self.assertTrue(tflite_model)
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# Check the model works with TensorFlow ops.
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interpreter = Interpreter(model_content=tflite_model)
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interpreter.allocate_tensors()
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input_details = interpreter.get_input_details()
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test_input = np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32)
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interpreter.set_tensor(input_details[0]['index'], test_input)
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interpreter.invoke()
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output_details = interpreter.get_output_details()
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expected_output = np.array([[2.0, 4.0, 6.0, 8.0]], dtype=np.float32)
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output_data = interpreter.get_tensor(output_details[0]['index'])
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self.assertTrue((expected_output == output_data).all())
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class FromConcreteFunctionTest(test_util.TensorFlowTestCase,
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parameterized.TestCase):
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@parameterized.named_parameters(
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('EnableMlirConverter', True), # enable mlir
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('DisableMlirConverter', False)) # disable mlir
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@test_util.run_v2_only
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def testFloat(self, enable_mlir):
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input_data = constant_op.constant(1., shape=[1])
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root = tracking.AutoTrackable()
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root.v1 = variables.Variable(3.)
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root.v2 = variables.Variable(2.)
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root.f = def_function.function(lambda x: root.v1 * root.v2 * x)
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concrete_func = root.f.get_concrete_function(input_data)
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# Convert model.
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converter = lite.TFLiteConverterV2.from_concrete_functions([concrete_func])
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converter.target_spec.supported_ops = set([lite.OpsSet.SELECT_TF_OPS])
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converter.experimental_new_converter = enable_mlir
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tflite_model = converter.convert()
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# Check the model works with TensorFlow ops.
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interpreter = Interpreter(model_content=tflite_model)
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interpreter.allocate_tensors()
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input_details = interpreter.get_input_details()
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test_input = np.array([4.0], dtype=np.float32)
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interpreter.set_tensor(input_details[0]['index'], test_input)
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interpreter.invoke()
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output_details = interpreter.get_output_details()
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expected_output = np.array([24.0], dtype=np.float32)
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output_data = interpreter.get_tensor(output_details[0]['index'])
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self.assertTrue((expected_output == output_data).all())
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if __name__ == '__main__':
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test.main()
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