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test_rnn_cells.py
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test_rnn_cells.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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
#
# http://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.
import paddle
paddle.framework.set_default_dtype("float64")
import unittest
import numpy as np
from convert import convert_params_for_cell
from rnn_numpy import GRUCell, LSTMCell, SimpleRNNCell
class TestSimpleRNNCell(unittest.TestCase):
def __init__(self, weight=True, bias=True, place="cpu"):
super().__init__(methodName="runTest")
self.weight = weight
self.bias = bias
self.place = (
paddle.CPUPlace() if place == "cpu" else paddle.CUDAPlace(0)
)
def setUp(self):
paddle.disable_static(self.place)
rnn1 = SimpleRNNCell(16, 32, weight=self.weight, bias=self.bias)
rnn2 = paddle.nn.SimpleRNNCell(
16,
32,
weight_ih_attr=self.weight,
weight_hh_attr=self.weight,
bias_ih_attr=self.bias,
bias_hh_attr=self.bias,
)
convert_params_for_cell(rnn1, rnn2)
self.rnn1 = rnn1
self.rnn2 = rnn2
def test_with_initial_state(self):
rnn1 = self.rnn1
rnn2 = self.rnn2
x = np.random.randn(4, 16)
prev_h = np.random.randn(4, 32)
y1, h1 = rnn1(x, prev_h)
y2, h2 = rnn2(paddle.to_tensor(x), paddle.to_tensor(prev_h))
np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
def test_with_zero_state(self):
rnn1 = self.rnn1
rnn2 = self.rnn2
x = np.random.randn(4, 16)
y1, h1 = rnn1(x)
y2, h2 = rnn2(paddle.to_tensor(x))
np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
def test_errors(self):
def test_zero_hidden_size():
cell = paddle.nn.SimpleRNNCell(-1, 0)
self.assertRaises(ValueError, test_zero_hidden_size)
def runTest(self):
self.test_with_initial_state()
self.test_with_zero_state()
self.test_errors()
class TestGRUCell(unittest.TestCase):
def __init__(self, weight=True, bias=True, place="cpu"):
super().__init__(methodName="runTest")
self.weight = weight
self.bias = bias
self.place = (
paddle.CPUPlace() if place == "cpu" else paddle.CUDAPlace(0)
)
def setUp(self):
paddle.disable_static(self.place)
rnn1 = GRUCell(16, 32, weight=self.weight, bias=self.bias)
rnn2 = paddle.nn.GRUCell(
16,
32,
weight_ih_attr=self.weight,
weight_hh_attr=self.weight,
bias_ih_attr=self.bias,
bias_hh_attr=self.bias,
)
convert_params_for_cell(rnn1, rnn2)
self.rnn1 = rnn1
self.rnn2 = rnn2
def test_with_initial_state(self):
rnn1 = self.rnn1
rnn2 = self.rnn2
x = np.random.randn(4, 16)
prev_h = np.random.randn(4, 32)
y1, h1 = rnn1(x, prev_h)
y2, h2 = rnn2(paddle.to_tensor(x), paddle.to_tensor(prev_h))
np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
def test_with_zero_state(self):
rnn1 = self.rnn1
rnn2 = self.rnn2
x = np.random.randn(4, 16)
y1, h1 = rnn1(x)
y2, h2 = rnn2(paddle.to_tensor(x))
np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
def test_errors(self):
def test_zero_hidden_size():
cell = paddle.nn.GRUCell(-1, 0)
self.assertRaises(ValueError, test_zero_hidden_size)
def runTest(self):
self.test_with_initial_state()
self.test_with_zero_state()
self.test_errors()
class TestLSTMCell(unittest.TestCase):
def __init__(self, weight=True, bias=True, place="cpu"):
super().__init__(methodName="runTest")
self.weight = weight
self.bias = bias
self.place = (
paddle.CPUPlace() if place == "cpu" else paddle.CUDAPlace(0)
)
def setUp(self):
rnn1 = LSTMCell(16, 32, weight=self.weight, bias=self.bias)
rnn2 = paddle.nn.LSTMCell(
16,
32,
weight_ih_attr=self.weight,
weight_hh_attr=self.weight,
bias_ih_attr=self.bias,
bias_hh_attr=self.bias,
)
convert_params_for_cell(rnn1, rnn2)
self.rnn1 = rnn1
self.rnn2 = rnn2
def test_with_initial_state(self):
rnn1 = self.rnn1
rnn2 = self.rnn2
x = np.random.randn(4, 16)
prev_h = np.random.randn(4, 32)
prev_c = np.random.randn(4, 32)
y1, (h1, c1) = rnn1(x, (prev_h, prev_c))
y2, (h2, c2) = rnn2(
paddle.to_tensor(x),
(paddle.to_tensor(prev_h), paddle.to_tensor(prev_c)),
)
np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
np.testing.assert_allclose(c1, c2.numpy(), atol=1e-8, rtol=1e-5)
def test_with_zero_state(self):
rnn1 = self.rnn1
rnn2 = self.rnn2
x = np.random.randn(4, 16)
y1, (h1, c1) = rnn1(x)
y2, (h2, c2) = rnn2(paddle.to_tensor(x))
np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
np.testing.assert_allclose(c1, c2.numpy(), atol=1e-8, rtol=1e-5)
def test_errors(self):
def test_zero_hidden_size():
cell = paddle.nn.LSTMCell(-1, 0)
self.assertRaises(ValueError, test_zero_hidden_size)
def runTest(self):
self.test_with_initial_state()
self.test_with_zero_state()
self.test_errors()
def load_tests(loader, tests, pattern):
suite = unittest.TestSuite()
devices = ["cpu", "gpu"] if paddle.base.is_compiled_with_cuda() else ["cpu"]
for weight in [True, False]:
for bias in [True, False]:
for device in devices:
for test_class in [
TestSimpleRNNCell,
TestGRUCell,
TestLSTMCell,
]:
suite.addTest(test_class(weight, bias, device))
return suite