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142 lines
3.6 KiB
142 lines
3.6 KiB
# Copyright (c) 2018 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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from __future__ import print_function
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import unittest
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import numpy as np
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import math
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from op_test import OpTest
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from test_gru_op import gru
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from test_fusion_lstm_op import fc, ACTIVATION
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def fusion_gru(
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x, # T x M
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lod, # 1 x N
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h0, # N x D
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wx, # M x 3D
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wh, # D x 3D
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bias, # 1 x 3D
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is_reverse,
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act_state,
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act_gate):
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return gru(fc(x, wx, bias),
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lod,
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h0,
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wh,
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np.zeros(
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(1, wh.shape[1]), dtype='float32'),
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is_reverse,
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act_state,
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act_gate)
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class TestFusionGRUOp(OpTest):
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def set_confs(self):
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pass
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def setUp(self):
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self.op_type = "fusion_gru"
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self.lod = [[2, 4, 3]]
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self.M = 3
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self.D = 5
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self.is_reverse = False
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self.with_h0 = True
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self.with_bias = True
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self.act_state = 'tanh'
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self.act_gate = 'sigmoid'
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self.set_confs()
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T = sum(self.lod[0])
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N = len(self.lod[0])
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x = np.random.rand(T, self.M).astype('float32')
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wx = np.random.rand(self.M, 3 * self.D).astype('float32')
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wh = np.random.rand(self.D, 3 * self.D).astype('float32')
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bias = np.random.rand(
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1, 3 * self.D).astype('float32') if self.with_bias else np.zeros(
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(1, 3 * self.D), dtype='float32')
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h0 = np.random.rand(
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N, self.D).astype('float32') if self.with_h0 else np.zeros(
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(N, self.D), dtype='float32')
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_, _, _, hidden = fusion_gru(
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x, self.lod, h0, wx, wh, bias, self.is_reverse,
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ACTIVATION[self.act_state], ACTIVATION[self.act_gate])
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self.inputs = {'X': (x, self.lod), 'WeightX': wx, 'WeightH': wh}
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if self.with_bias:
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self.inputs['Bias'] = bias
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if self.with_h0:
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self.inputs['H0'] = h0
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self.outputs = {'Hidden': (hidden, self.lod)}
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self.attrs = {
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'activation': self.act_state,
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'gate_activation': self.act_gate,
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'is_reverse': self.is_reverse
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}
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def test_check_output(self):
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for use_seq in {True, False}:
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self.attrs['use_seq'] = use_seq
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self.check_output()
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class TestFusionGRUOpNoInitial(TestFusionGRUOp):
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def set_confs(self):
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self.with_h0 = False
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class TestFusionGRUOpNoBias(TestFusionGRUOp):
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def set_confs(self):
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self.with_bias = False
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class TestFusionGRUOpReverse(TestFusionGRUOp):
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def set_confs(self):
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self.is_reverse = True
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class TestFusionGRUOpMD1(TestFusionGRUOp):
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def set_confs(self):
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self.M = 36
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self.D = 8
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class TestFusionGRUOpMD2(TestFusionGRUOp):
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def set_confs(self):
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self.M = 8
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self.D = 8
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class TestFusionGRUOpMD3(TestFusionGRUOp):
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def set_confs(self):
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self.M = 17
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self.D = 15
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class TestFusionGRUOpBS1(TestFusionGRUOp):
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def set_confs(self):
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self.lod = [[3]]
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self.D = 16
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if __name__ == "__main__":
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unittest.main()
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