blob: 747cc6f94eab2474836b1176e1dd3efb4864aae7 [file] [log] [blame]
# 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)