| # Lint as: python3 |
| # Copyright 2019 Google LLC |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # https://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| |
| from absl import app |
| import numpy as np |
| from pyiree.tf.support import tf_test_utils |
| from pyiree.tf.support import tf_utils |
| import tensorflow.compat.v2 as tf |
| |
| NUM_BATCH = 7 |
| NUM_TIMESTEPS = 24 |
| NUM_UNITS = 10 |
| DYNAMIC_SHAPE = [None, None, NUM_UNITS] |
| INPUT_SHAPE = [NUM_BATCH, NUM_TIMESTEPS, NUM_UNITS] |
| |
| |
| class LstmModule(tf.Module): |
| |
| def __init__(self): |
| super(LstmModule, self).__init__() |
| tf_utils.set_random_seed() |
| inputs = tf.keras.layers.Input(batch_size=None, shape=DYNAMIC_SHAPE[1:]) |
| outputs = tf.keras.layers.LSTM(units=NUM_UNITS, |
| return_sequences=True)(inputs) |
| self.m = tf.keras.Model(inputs, outputs) |
| self.predict = tf.function( |
| input_signature=[tf.TensorSpec(DYNAMIC_SHAPE, tf.float32)])(self.m.call) |
| |
| |
| class LstmTest(tf_test_utils.TracedModuleTestCase): |
| |
| def __init__(self, methodName="runTest"): |
| super(LstmTest, self).__init__(methodName) |
| self._modules = tf_test_utils.compile_tf_module(LstmModule, |
| exported_names=["predict"]) |
| |
| def test_lstm(self): |
| |
| def predict(module): |
| inputs = tf_utils.ndarange(INPUT_SHAPE) |
| module.predict(inputs) |
| |
| self.compare_backends(predict, self._modules) |
| |
| |
| def main(argv): |
| del argv # Unused |
| if hasattr(tf, 'enable_v2_behavior'): |
| tf.enable_v2_behavior() |
| |
| tf.test.main() |
| |
| |
| if __name__ == '__main__': |
| app.run(main) |