wehub-resource-sync
133 lines
4.2 KiB
Python
133 lines
4.2 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 unittest
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import numpy as np
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from op_test import get_device, is_custom_device
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import paddle
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from paddle.base import core
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class Optimization_ex1(paddle.nn.Layer):
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def __init__(
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self,
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shape,
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dtype,
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param_attr=paddle.nn.initializer.Uniform(low=-5.0, high=5.0),
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):
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super().__init__()
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self.theta0 = self.create_parameter(
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shape=shape, attr=param_attr, dtype=dtype, is_bias=False
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)
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self.theta1 = self.create_parameter(
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shape=shape, attr=param_attr, dtype=dtype, is_bias=False
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)
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self.A = paddle.to_tensor(
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np.random.random((4, 4)).astype(dtype)
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+ np.random.random((4, 4)).astype(dtype) * 1j
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)
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self.B = paddle.to_tensor(
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np.random.random((4, 4)).astype(dtype)
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+ np.random.random((4, 4)).astype(dtype) * 1j,
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stop_gradient=False,
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)
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def forward(self, mode=1):
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jj = paddle.to_tensor(np.array([1j]).astype(np.complex64))
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if mode == 1:
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# run all calc in one step
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loss = paddle.sum(self.A + (self.theta0 + self.theta1 * jj)) * (
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paddle.sum(self.A + (self.theta0 + self.theta1 * jj)).conj()
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)
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return loss.real()
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elif mode == 2:
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# run in two step
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self.theta = self.theta0 + self.theta1 * jj
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loss = paddle.sum(self.A + self.theta) * (
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paddle.sum(self.A + self.theta).conj()
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)
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return loss.real()
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elif mode == 3:
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# run without param
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loss = paddle.sum(self.A + self.B) * (
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paddle.sum(self.A + self.B).conj()
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)
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return loss.real()
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else:
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raise NotImplementedError
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class TestComplexGradAccumulated(unittest.TestCase):
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def setUp(self):
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self.devices = ['cpu']
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if core.is_compiled_with_cuda() or is_custom_device():
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self.devices.append(get_device())
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self.iter = 3
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self.learning_rate = 0.5
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self.dtypes = ['float32', 'float64']
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self.theta_size = [4, 4]
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def train(self, device, dtype, mode):
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paddle.set_device(device)
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myLayer = Optimization_ex1(self.theta_size, dtype)
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optimizer = paddle.optimizer.SGD(
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learning_rate=self.learning_rate, parameters=myLayer.parameters()
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)
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for iter in range(self.iter):
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loss = myLayer(mode)
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loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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def train_no_clear_grad(self, device, dtype, mode):
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paddle.set_device(device)
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myLayer = Optimization_ex1(self.theta_size, dtype)
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optimizer = paddle.optimizer.SGD(
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learning_rate=self.learning_rate, parameters=myLayer.parameters()
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)
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for iter in range(self.iter):
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loss = myLayer(mode)
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loss.backward()
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optimizer.step()
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def test_case_one_step(self):
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for dev in self.devices:
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for dtype in self.dtypes:
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self.train(dev, dtype, 1)
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self.train_no_clear_grad(dev, dtype, 1)
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def test_case_two_step(self):
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for dev in self.devices:
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for dtype in self.dtypes:
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self.train(dev, dtype, 2)
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self.train_no_clear_grad(dev, dtype, 2)
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def test_case_non_param(self):
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for dev in self.devices:
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for dtype in self.dtypes:
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self.train(dev, dtype, 3)
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self.train_no_clear_grad(dev, dtype, 3)
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if __name__ == '__main__':
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unittest.main()
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