wehub-resource-sync
357 lines
11 KiB
Python
357 lines
11 KiB
Python
# 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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import os
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import sys
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import tempfile
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import unittest
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import numpy as np
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from convert import convert_params_for_net
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sys.path.append("../../rnn")
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from rnn_numpy import GRU, LSTM, SimpleRNN
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bidirectional_list = ["bidirectional", "bidirect"]
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class TestSimpleRNN(unittest.TestCase):
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def __init__(
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self, time_major=True, direction="forward", place="cpu", mode='RNN_TANH'
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):
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super().__init__("runTest")
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self.time_major = time_major
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self.direction = direction
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self.num_directions = 2 if direction in bidirectional_list else 1
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self.place = place
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self.mode = mode
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def setUp(self):
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# Since `set_device` is global, set `set_device` in `setUp` rather than
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# `__init__` to avoid using an error device set by another test case.
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place = paddle.set_device(self.place)
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paddle.disable_static(place)
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rnn1 = SimpleRNN(
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16,
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32,
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2,
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time_major=self.time_major,
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direction=self.direction,
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nonlinearity=self.mode,
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)
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rnn2 = paddle.nn.SimpleRNN(
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16,
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32,
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2,
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time_major=self.time_major,
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direction=self.direction,
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activation=self.mode[4:].lower(),
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)
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convert_params_for_net(rnn1, rnn2)
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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(2 * self.num_directions, 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_predict(self):
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predict_test_util(self.place, "SimpleRNN")
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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_predict()
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class TestGRU(unittest.TestCase):
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def __init__(self, time_major=True, direction="forward", place="cpu"):
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super().__init__("runTest")
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self.time_major = time_major
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self.direction = direction
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self.num_directions = 2 if direction in bidirectional_list else 1
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self.place = place
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def setUp(self):
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# Since `set_device` is global, set `set_device` in `setUp` rather than
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# `__init__` to avoid using an error device set by another test case.
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place = paddle.set_device(self.place)
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paddle.disable_static(place)
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rnn1 = GRU(
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16, 32, 2, time_major=self.time_major, direction=self.direction
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)
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rnn2 = paddle.nn.GRU(
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16, 32, 2, time_major=self.time_major, direction=self.direction
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)
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convert_params_for_net(rnn1, rnn2)
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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(2 * self.num_directions, 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_predict(self):
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predict_test_util(self.place, "GRU")
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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_predict()
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class TestLSTM(unittest.TestCase):
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def __init__(self, time_major=True, direction="forward", place="cpu"):
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super().__init__("runTest")
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self.time_major = time_major
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self.direction = direction
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self.num_directions = 2 if direction in bidirectional_list else 1
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self.place = place
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def setUp(self):
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# Since `set_device` is global, set `set_device` in `setUp` rather than
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# `__init__` to avoid using an error device set by another test case.
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place = paddle.set_device(self.place)
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paddle.disable_static(place)
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rnn1 = LSTM(
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16, 32, 2, time_major=self.time_major, direction=self.direction
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)
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rnn2 = paddle.nn.LSTM(
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16, 32, 2, time_major=self.time_major, direction=self.direction
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)
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convert_params_for_net(rnn1, rnn2)
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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(
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2 * self.num_directions, 4, getattr(self, "proj_size", 32)
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)
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prev_c = np.random.randn(2 * self.num_directions, 4, 32)
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y1, (h1, c1) = rnn1(x, (prev_h, prev_c))
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y2, (h2, c2) = rnn2(
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paddle.to_tensor(x),
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(paddle.to_tensor(prev_h), paddle.to_tensor(prev_c)),
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)
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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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np.testing.assert_allclose(c1, c2.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, c1) = rnn1(x)
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y2, (h2, c2) = 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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np.testing.assert_allclose(c1, c2.numpy(), atol=1e-8, rtol=1e-5)
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def test_predict(self):
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predict_test_util(self.place, "LSTM")
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predict_test_util(self.place, "LSTM", False)
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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_predict()
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class TestLSTMWithProjSize(TestLSTM):
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def setUp(self):
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# Since `set_device` is global, set `set_device` in `setUp` rather than
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# `__init__` to avoid using an error device set by another test case.
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place = paddle.set_device(self.place)
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paddle.disable_static(place)
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rnn1 = LSTM(
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16,
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32,
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2,
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time_major=self.time_major,
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direction=self.direction,
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proj_size=8,
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)
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rnn2 = paddle.nn.LSTM(
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16,
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32,
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2,
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time_major=self.time_major,
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direction=self.direction,
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proj_size=8,
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)
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convert_params_for_net(rnn1, rnn2)
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self.rnn1 = rnn1
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self.rnn2 = rnn2
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self.proj_size = 8
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def predict_test_util(place, mode, stop_gradient=True):
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place = paddle.set_device(place)
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paddle.seed(123)
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np.random.seed(123)
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class Net(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.rnn = getattr(paddle.nn, mode)(
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16, 32, 2, direction="bidirectional", dropout=0.1
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)
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def forward(self, input):
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return self.rnn(input)
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x = paddle.randn((4, 10, 16))
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x.stop_gradient = stop_gradient
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seq_len = paddle.to_tensor(np.array([10, 6, 8, 5]))
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mask = paddle.static.nn.sequence_lod.sequence_mask(
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seq_len, maxlen=10, dtype=x.dtype
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)
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mask = paddle.unsqueeze(mask, [2])
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rnn = Net()
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y, _ = rnn(x)
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y = y * mask
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loss = paddle.mean(y)
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loss.backward()
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optimizer = paddle.optimizer.Adam(
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learning_rate=0.1, parameters=rnn.parameters()
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)
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optimizer.step()
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rnn.eval()
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y, _ = rnn(x)
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# `jit.to_static` would include a train_program, eval mode might cause
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# some errors currently, such as dropout grad op gets `is_test == True`.
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rnn.train()
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rnn = paddle.jit.to_static(
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rnn,
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[paddle.static.InputSpec(shape=[None, None, 16], dtype=x.dtype)],
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full_graph=True,
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)
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temp_dir = tempfile.TemporaryDirectory()
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save_dirname = os.path.join(temp_dir.name, f"./inference/{mode}_infer")
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paddle.jit.save(rnn, save_dirname)
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paddle.enable_static()
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new_scope = paddle.static.Scope()
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with paddle.static.scope_guard(new_scope):
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exe = paddle.static.Executor(place)
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[
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inference_program,
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feed_target_names,
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fetch_targets,
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] = paddle.static.load_inference_model(save_dirname, exe)
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results = exe.run(
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inference_program,
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feed={feed_target_names[0]: x.numpy()},
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fetch_list=fetch_targets,
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)
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np.testing.assert_equal(
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y.numpy(), results[0]
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) # eval results equal predict results
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paddle.disable_static()
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temp_dir.cleanup()
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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.base.is_compiled_with_cuda() else ["cpu"]
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for direction in ["forward", "bidirectional", "bidirect"]:
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for time_major in [True, False]:
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for device in devices:
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for test_class in [
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TestSimpleRNN,
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TestLSTM,
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TestGRU,
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TestLSTMWithProjSize,
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]:
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suite.addTest(test_class(time_major, direction, device))
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if test_class == TestSimpleRNN:
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suite.addTest(
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test_class(
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time_major, direction, device, mode="RNN_RELU"
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)
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)
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return suite
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if __name__ == '__main__':
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unittest.main()
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