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206 lines
7.0 KiB
206 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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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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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.dygraph as dg
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class TestKronOp(OpTest):
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def setUp(self):
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self.op_type = "kron"
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self.dtype = self._init_dtype()
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x = np.random.uniform(size=(10, 10)).astype(self.dtype)
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y = np.random.uniform(size=(10, 10)).astype(self.dtype)
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out_ref = np.kron(x, y)
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': out_ref}
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def _init_dtype(self):
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return "float64"
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X', 'Y'], 'Out')
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def test_check_grad_ignore_x(self):
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self.check_grad(['Y'], 'Out', no_grad_set=set('X'))
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def test_check_grad_ignore_y(self):
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self.check_grad(['X'], 'Out', no_grad_set=set('Y'))
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class TestKronOp2(TestKronOp):
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def setUp(self):
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self.op_type = "kron"
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self.dtype = self._init_dtype()
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x = np.random.uniform(size=(5, 5, 4)).astype(self.dtype)
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y = np.random.uniform(size=(10, 10)).astype(self.dtype)
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out_ref = np.kron(x, y)
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': out_ref}
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class TestKronOp3(TestKronOp):
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def setUp(self):
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self.op_type = "kron"
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self.dtype = self._init_dtype()
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x = np.random.uniform(size=(10, 10)).astype(self.dtype)
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y = np.random.uniform(size=(5, 5, 4)).astype(self.dtype)
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out_ref = np.kron(x, y)
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': out_ref}
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class TestKronLayer(unittest.TestCase):
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def test_case(self):
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a = np.random.randn(10, 10).astype(np.float64)
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b = np.random.randn(10, 10).astype(np.float64)
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place = fluid.CPUPlace()
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with dg.guard(place):
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a_var = dg.to_variable(a)
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b_var = dg.to_variable(b)
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c_var = paddle.kron(a_var, b_var)
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np.testing.assert_allclose(c_var.numpy(), np.kron(a, b))
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def test_case_with_output(self):
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a = np.random.randn(10, 10).astype(np.float64)
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b = np.random.randn(10, 10).astype(np.float64)
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main = fluid.Program()
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start = fluid.Program()
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with fluid.unique_name.guard():
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with fluid.program_guard(main, start):
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a_var = fluid.data("a", [-1, -1], dtype="float64")
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b_var = fluid.data("b", [-1, -1], dtype="float64")
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out_var = paddle.kron(a_var, b_var)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(start)
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c, = exe.run(main, feed={'a': a, 'b': b}, fetch_list=[out_var])
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np.testing.assert_allclose(c, np.kron(a, b))
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class TestComplexKronOp(OpTest):
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def setUp(self):
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self.op_type = "kron"
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self.x_shape = np.array([10, 10])
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self.y_shape = np.array([3, 35])
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self.out_shape = self.x_shape * self.y_shape
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self.init_base_dtype()
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self.init_input_output()
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self.init_grad_input_output()
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self.inputs = {
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'X': OpTest.np_dtype_to_fluid_dtype(self.x),
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'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
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}
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self.attrs = {'axis': -1, 'use_mkldnn': False}
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self.outputs = {'Out': self.out}
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def init_base_dtype(self):
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self.dtype = np.float64
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def init_input_output(self):
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self.x = np.random.random(self.x_shape).astype(
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self.dtype) + 1J * np.random.random(self.x_shape).astype(self.dtype)
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self.y = np.random.random(self.y_shape).astype(
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self.dtype) + 1J * np.random.random(self.y_shape).astype(self.dtype)
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self.out = np.kron(self.x, self.y)
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def init_grad_input_output(self):
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self.grad_out = np.ones(self.out_shape, self.dtype) + 1J * np.ones(
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self.out_shape, self.dtype)
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self.grad_x = self.get_grad_x_by_numpy()
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self.grad_y = self.get_grad_y_by_numpy()
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def get_grad_x_by_numpy(self):
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grad_x = np.zeros(self.x_shape, np.complex)
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for x_i in range(self.x_shape[0]):
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for x_j in range(self.x_shape[1]):
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for i in range(self.y_shape[0]):
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for j in range(self.y_shape[1]):
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idx_i = x_i * self.y_shape[0] + i
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idx_j = x_j * self.y_shape[1] + j
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grad_x[x_i][x_j] += self.grad_out[idx_i][
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idx_j] * np.conj(self.y[i][j])
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return grad_x
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def get_grad_y_by_numpy(self):
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grad_y = np.zeros(self.y_shape, np.complex)
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for y_i in range(self.y_shape[0]):
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for y_j in range(self.y_shape[1]):
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for x_i in range(self.x_shape[0]):
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for x_j in range(self.x_shape[1]):
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idx_i = x_i * self.y_shape[0] + y_i
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idx_j = x_j * self.y_shape[1] + y_j
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grad_y[y_i][y_j] += self.grad_out[idx_i][
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idx_j] * np.conj(self.x[x_i][x_j])
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return grad_y
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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(
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['X', 'Y'],
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'Out',
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user_defined_grads=[self.grad_x, self.grad_y],
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user_defined_grad_outputs=[self.grad_out])
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def test_check_grad_ingore_x(self):
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self.check_grad(
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['Y'],
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'Out',
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no_grad_set=set("X"),
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user_defined_grads=[self.grad_y],
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user_defined_grad_outputs=[self.grad_out])
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def test_check_grad_ingore_y(self):
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self.check_grad(
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['X'],
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'Out',
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no_grad_set=set('Y'),
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user_defined_grads=[self.grad_x],
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user_defined_grad_outputs=[self.grad_out])
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class TestKronOpTypePromotion(TestComplexKronOp):
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def init_input_output(self):
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self.x = np.random.random(self.x_shape).astype(self.dtype)
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self.y = np.random.random(self.y_shape).astype(
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self.dtype) + 1J * np.random.random(self.y_shape).astype(self.dtype)
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self.out = np.kron(self.x, self.y)
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def init_grad_input_output(self):
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self.grad_out = np.ones(self.out_shape, self.dtype) + 1J * np.ones(
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self.out_shape, self.dtype)
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self.grad_x = self.get_grad_x_by_numpy().real
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self.grad_y = self.get_grad_y_by_numpy()
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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