integrations/tensorflow/e2e/keras/lstm_static_test.py

# 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.

# This test is the same as keras_lstm_test, but all shapes are static.
# This stresses the TensorList lowering more specifically.

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
INPUT_SHAPE = [NUM_BATCH, NUM_TIMESTEPS, NUM_UNITS]


class LstmStaticModule(tf.Module):

  def __init__(self):
    super().__init__()
    tf_utils.set_random_seed()
    inputs = tf.keras.layers.Input(batch_size=NUM_BATCH, shape=INPUT_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(INPUT_SHAPE, tf.float32)])(self.m.call)


class LstmStaticTest(tf_test_utils.TracedModuleTestCase):

  def __init__(self, *args, **kwargs):
    super().__init__(*args, **kwargs)
    self._modules = tf_test_utils.compile_tf_module(LstmStaticModule,
                                                    exported_names=["predict"])

  def test_lstm(self):

    def predict(module):
      inputs = tf_utils.ndarange(INPUT_SHAPE)
      module.predict(inputs, rtol=1e-5, atol=1e-5)

    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)