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196 lines
7.0 KiB
196 lines
7.0 KiB
# Copyright (c) 2020 PaddlePaddle 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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import paddle
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paddle.set_default_dtype("float64")
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from paddle.fluid.layers import sequence_mask
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import numpy as np
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import unittest
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from convert import convert_params_for_cell
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from rnn_numpy import GRUCell, RNN, BiRNN
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class TestRNNWrapper(unittest.TestCase):
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def __init__(self, time_major=True, direction="forward", place="cpu"):
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super(TestRNNWrapper, self).__init__("runTest")
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self.time_major = time_major
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self.direction = direction
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self.place = paddle.CPUPlace() if place == "cpu" \
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else paddle.CUDAPlace(0)
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def setUp(self):
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paddle.disable_static(self.place)
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cell1 = GRUCell(16, 32)
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cell2 = paddle.nn.GRUCell(16, 32)
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convert_params_for_cell(cell1, cell2)
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rnn1 = RNN(cell1,
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is_reverse=self.direction == "backward",
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time_major=self.time_major)
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rnn2 = paddle.nn.RNN(cell2,
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is_reverse=self.direction == "backward",
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time_major=self.time_major)
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self.rnn1 = rnn1
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self.rnn2 = rnn2
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def test_with_initial_state(self):
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rnn1 = self.rnn1
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rnn2 = self.rnn2
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x = np.random.randn(12, 4, 16)
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if not self.time_major:
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x = np.transpose(x, [1, 0, 2])
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prev_h = np.random.randn(4, 32)
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y1, h1 = rnn1(x, prev_h)
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y2, h2 = rnn2(paddle.to_tensor(x), paddle.to_tensor(prev_h))
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np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
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np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
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def test_with_zero_state(self):
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rnn1 = self.rnn1
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rnn2 = self.rnn2
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x = np.random.randn(12, 4, 16)
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if not self.time_major:
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x = np.transpose(x, [1, 0, 2])
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y1, h1 = rnn1(x)
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y2, h2 = rnn2(paddle.to_tensor(x))
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np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
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np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
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def test_with_input_lengths(self):
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rnn1 = self.rnn1
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rnn2 = self.rnn2
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x = np.random.randn(12, 4, 16)
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if not self.time_major:
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x = np.transpose(x, [1, 0, 2])
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sequence_length = np.array([12, 10, 9, 8], dtype=np.int64)
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y1, h1 = rnn1(x, sequence_length=sequence_length)
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seq_len = paddle.to_tensor(sequence_length)
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mask = sequence_mask(seq_len, dtype=paddle.get_default_dtype())
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if self.time_major:
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mask = paddle.transpose(mask, [1, 0])
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y2, h2 = rnn2(paddle.to_tensor(x), sequence_length=seq_len)
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mask = paddle.unsqueeze(mask, -1)
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y2 = paddle.multiply(y2, mask)
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np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
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np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
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def runTest(self):
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self.test_with_initial_state()
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self.test_with_zero_state()
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self.test_with_input_lengths()
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class TestBiRNNWrapper(unittest.TestCase):
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def __init__(self, time_major=True, place="cpu"):
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super(TestBiRNNWrapper, self).__init__("runTest")
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self.time_major = time_major
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self.place = paddle.CPUPlace() if place == "cpu" \
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else paddle.CUDAPlace(0)
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def setUp(self):
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paddle.disable_static(self.place)
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fw_cell1 = GRUCell(16, 32)
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bw_cell1 = GRUCell(16, 32)
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fw_cell2 = paddle.nn.GRUCell(16, 32)
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bw_cell2 = paddle.nn.GRUCell(16, 32)
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convert_params_for_cell(fw_cell1, fw_cell2)
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convert_params_for_cell(bw_cell1, bw_cell2)
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rnn1 = BiRNN(fw_cell1, bw_cell1, time_major=self.time_major)
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rnn2 = paddle.nn.BiRNN(fw_cell2, bw_cell2, time_major=self.time_major)
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self.rnn1 = rnn1
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self.rnn2 = rnn2
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def test_with_initial_state(self):
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rnn1 = self.rnn1
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rnn2 = self.rnn2
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x = np.random.randn(12, 4, 16)
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if not self.time_major:
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x = np.transpose(x, [1, 0, 2])
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fw_prev_h = np.random.randn(4, 32)
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bw_prev_h = np.random.randn(4, 32)
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y1, (fw_h1, bw_h1) = rnn1(x, (fw_prev_h, bw_prev_h))
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y2, (fw_h2, bw_h2) = rnn2(
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paddle.to_tensor(x),
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(paddle.to_tensor(fw_prev_h), paddle.to_tensor(bw_prev_h)))
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np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
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np.testing.assert_allclose(fw_h1, fw_h2.numpy(), atol=1e-8, rtol=1e-5)
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np.testing.assert_allclose(bw_h1, bw_h2.numpy(), atol=1e-8, rtol=1e-5)
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def test_with_zero_state(self):
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rnn1 = self.rnn1
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rnn2 = self.rnn2
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x = np.random.randn(12, 4, 16)
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if not self.time_major:
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x = np.transpose(x, [1, 0, 2])
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y1, (fw_h1, bw_h1) = rnn1(x)
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y2, (fw_h2, bw_h2) = rnn2(paddle.to_tensor(x))
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np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
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np.testing.assert_allclose(fw_h1, fw_h2.numpy(), atol=1e-8, rtol=1e-5)
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np.testing.assert_allclose(bw_h1, bw_h2.numpy(), atol=1e-8, rtol=1e-5)
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def test_with_input_lengths(self):
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rnn1 = self.rnn1
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rnn2 = self.rnn2
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x = np.random.randn(12, 4, 16)
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if not self.time_major:
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x = np.transpose(x, [1, 0, 2])
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sequence_length = np.array([12, 10, 9, 8], dtype=np.int64)
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y1, (fw_h1, bw_h1) = rnn1(x, sequence_length=sequence_length)
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seq_len = paddle.to_tensor(sequence_length)
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mask = sequence_mask(seq_len, dtype=paddle.get_default_dtype())
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if self.time_major:
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mask = paddle.transpose(mask, [1, 0])
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y2, (fw_h2, bw_h2) = rnn2(paddle.to_tensor(x), sequence_length=seq_len)
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mask = paddle.unsqueeze(mask, -1)
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y2 = paddle.multiply(y2, mask)
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np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
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np.testing.assert_allclose(fw_h1, fw_h2.numpy(), atol=1e-8, rtol=1e-5)
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np.testing.assert_allclose(bw_h1, bw_h2.numpy(), atol=1e-8, rtol=1e-5)
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def runTest(self):
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self.test_with_initial_state()
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self.test_with_zero_state()
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self.test_with_input_lengths()
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def load_tests(loader, tests, pattern):
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suite = unittest.TestSuite()
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devices = ["cpu", "gpu"] if paddle.fluid.is_compiled_with_cuda() \
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else ["cpu"]
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for direction in ["forward", "backward"]:
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for device in devices:
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for time_major in [False]:
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suite.addTest(TestRNNWrapper(time_major, direction, device))
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suite.addTest(TestBiRNNWrapper(time_major, device))
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return suite
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