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160 lines
4.8 KiB
160 lines
4.8 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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from op_test import OpTest
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def row_conv_forward(x, lod, wt):
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out = np.zeros_like(x)
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num_sequences = len(lod[0])
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seq_info = [0]
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for seq_len in lod[0]:
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seq_info.append(seq_info[-1] + seq_len)
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context_length = wt.shape[0]
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for i in range(num_sequences): # loop over number of sequences
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start = seq_info[i]
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end = seq_info[i + 1]
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curinput = x[start:end, :]
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curoutput = out[start:end, :]
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cur_timesteps = end - start
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for j in range(cur_timesteps): # loop over different timesteps
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for k in range(context_length):
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if j + k >= cur_timesteps:
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continue
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curoutput[j, :] += curinput[j + k, :] * wt[k, :]
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return out
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class TestRowConvOp1(OpTest):
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def setUp(self):
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self.op_type = "row_conv"
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lod = [[2, 3, 2]]
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T = sum(lod[0])
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D = 16
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context_length = 2
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x = np.random.random((T, D)).astype("float32")
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wt = np.random.random((context_length, D)).astype("float32")
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self.inputs = {'X': (x, lod), 'Filter': wt}
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out = row_conv_forward(x, lod, wt)
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self.outputs = {'Out': (out, lod)}
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(['X', 'Filter'], 'Out', max_relative_error=0.05)
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def test_check_grad_ignore_x(self):
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self.check_grad(
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['Filter'], 'Out', max_relative_error=0.05, no_grad_set=set('X'))
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def test_check_grad_ignore_wt(self):
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self.check_grad(
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['X'], 'Out', max_relative_error=0.05, no_grad_set=set('Filter'))
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class TestRowConvOp2(OpTest):
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def setUp(self):
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self.op_type = "row_conv"
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lod = [[20, 30, 50]]
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T = sum(lod[0])
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D = 35
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context_length = 35
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x = np.random.random((T, D)).astype("float32")
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wt = np.random.random((context_length, D)).astype("float32")
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self.inputs = {'X': (x, lod), 'Filter': wt}
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out = row_conv_forward(x, lod, wt)
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self.outputs = {'Out': (out, lod)}
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def test_check_output(self):
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self.check_output()
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#max_relative_error is increased from 0.05 to 0.06 as for higher
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#dimensional input, the dX on CPU for some values has max_rel_error
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#slightly more than 0.05
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def test_check_grad_normal(self):
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self.check_grad(['X', 'Filter'], 'Out', max_relative_error=0.06)
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def test_check_grad_ignore_x(self):
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self.check_grad(
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['Filter'], 'Out', max_relative_error=0.06, no_grad_set=set('X'))
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def test_check_grad_ignore_wt(self):
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self.check_grad(
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['X'], 'Out', max_relative_error=0.06, no_grad_set=set('Filter'))
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def row_conv_foward_Tensor(x, wt):
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out = np.zeros_like(x)
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num_sequence = x.shape[0]
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timesteps = x.shape[1]
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context_length = wt.shape[0]
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for i in range(num_sequence):
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cur_in = x[i:i + 1, :][0]
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cur_out = out[i:i + 1, :][0]
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for j in range(timesteps):
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for k in range(context_length):
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if j + k >= timesteps:
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continue
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cur_out[j, :] += cur_in[j + k, :] * wt[k, :]
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return out
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class TestRowOpWithTensorInput(OpTest):
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def setUp(self):
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self.op_type = "row_conv"
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length = [3, 2, 4]
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B = 2
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T = sum(length)
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D = 16
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context_length = 2
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x = np.random.random((B, T, D)).astype("float32")
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wt = np.random.random((context_length, D)).astype("float32")
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self.inputs = {'X': x, 'Filter': wt}
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out = row_conv_foward_Tensor(x, wt)
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self.outputs = {'Out': out}
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def test_check_output(self):
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self.check_output()
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def test_check_grad_ignore_x(self):
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self.check_grad(
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['Filter'], 'Out', max_relative_error=0.05, no_grad_set=set('X'))
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def test_check_grad_normal(self):
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self.check_grad(['X', 'Filter'], 'Out', max_relative_error=0.05)
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def test_check_grad_ignore_wt(self):
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self.check_grad(
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['X'], 'Out', max_relative_error=0.05, no_grad_set=set('Filter'))
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if __name__ == '__main__':
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unittest.main()
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