wehub-resource-sync
112 lines
3.6 KiB
Python
112 lines
3.6 KiB
Python
# 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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import unittest
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import numpy as np
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from op_test import OpTest
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class TestMatchMatrixTensorOp(OpTest):
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def setUp(self):
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self.init_op_type()
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self.set_data()
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self.compute()
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def init_op_type(self):
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self.op_type = "match_matrix_tensor"
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def set_data(self):
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ix, iy, h, dim_t = [5, 8, 20, 4]
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x_lod = [[1, 2, 2]]
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y_lod = [[3, 1, 4]]
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self.init_data(ix, x_lod, iy, y_lod, h, dim_t)
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def init_data(self, ix, x_lod, iy, y_lod, h, dim_t):
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x_data = np.random.random((ix, h)).astype('float32')
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y_data = np.random.random((iy, h)).astype('float32')
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w_data = np.random.random((h, dim_t, h)).astype('float32')
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self.inputs = {'X': (x_data, x_lod), 'Y': (y_data, y_lod), 'W': w_data}
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self.attrs = {'dim_t': dim_t}
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def compute(self):
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x_data, x_lod = self.inputs['X']
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y_data, y_lod = self.inputs['Y']
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# [k, dim_t, k] -> [dim_t, k, k]
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w_data = self.inputs['W'].transpose(1, 0, 2)
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out = np.zeros((0, 1), dtype=x_data.dtype)
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# for x*w
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tmp = np.zeros((0, 1), dtype=x_data.dtype)
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out_lod = [[]]
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tmp_lod = [[]]
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x_offset, y_offset = 0, 0
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for idx in range(len(x_lod[0])):
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x_len = x_lod[0][idx]
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y_len = y_lod[0][idx]
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x_sub = x_data[x_offset : (x_offset + x_len), :]
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y_sub = y_data[y_offset : (y_offset + y_len), :]
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tmp_sub = np.dot(x_sub, w_data)
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tmp = np.vstack((tmp, tmp_sub.reshape(tmp_sub.size, 1)))
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out_sub = np.dot(tmp_sub, y_sub.T).transpose(1, 0, 2)
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out_lod[0].append(out_sub.size)
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out = np.vstack((out, out_sub.reshape(out_sub.size, 1)))
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x_offset += x_len
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y_offset += y_len
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self.outputs = {'Out': (out, out_lod), 'Tmp': tmp}
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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def test_check_grad(self):
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self.check_grad(['X', 'Y'], 'Out', check_dygraph=False)
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class TestMatchMatrixTensorOpCase1(TestMatchMatrixTensorOp):
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def set_data(self):
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ix, iy, h, dim_t = [5, 8, 25, 4]
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x_lod = [[5]]
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y_lod = [[8]]
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self.init_data(ix, x_lod, iy, y_lod, h, dim_t)
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class TestMatchMatrixTensorOpCase2(TestMatchMatrixTensorOp):
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def set_data(self):
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ix, iy, h, dim_t = [105, 120, 1, 4]
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x_lod = [[30, 45, 30]]
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y_lod = [[45, 15, 60]]
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self.init_data(ix, x_lod, iy, y_lod, h, dim_t)
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class TestMatchMatrixTensorOpCase3(TestMatchMatrixTensorOp):
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def set_data(self):
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ix, iy, h, dim_t = [5, 9, 32, 1]
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x_lod = [[1, 2, 2]]
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y_lod = [[3, 2, 4]]
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self.init_data(ix, x_lod, iy, y_lod, h, dim_t)
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class TestMatchMatrixTensorOpCase4(TestMatchMatrixTensorOp):
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def set_data(self):
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ix, iy, h, dim_t = [8, 12, 16, 5]
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x_lod = [[1, 2, 3, 1, 1]]
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y_lod = [[3, 2, 4, 1, 2]]
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self.init_data(ix, x_lod, iy, y_lod, h, dim_t)
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if __name__ == '__main__':
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unittest.main()
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