# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import numpy as np
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.op import Operator
from op_test import OpTest
import paddle
class TestSGDOp(OpTest):
def setUp(self):
self.op_type = "sgd"
self.conf()
w = np.random.random((self.h, self.w)).astype("float32")
g = np.random.random((self.h, self.w)).astype("float32")
lr = np.array([0.1]).astype("float32")
self.inputs = {'Param': w, 'Grad': g, 'LearningRate': lr}
self.outputs = {'ParamOut': w - lr * g}
def conf(self):
self.h = 102
self.w = 105
def test_check_output(self):
self.check_output()
class TestSGDOpCase8X(TestSGDOp):
def conf(self):
self.h = 10
self.w = 64
class TestSparseSGDOp(unittest.TestCase):
def check_with_place(self, place):
scope = core.Scope()
# create and initialize Grad Variable
height = 10
rows = [0, 4, 7]
self.conf()
grad_selected_rows = scope.var('Grad').get_selected_rows()
grad_selected_rows.set_height(height)
grad_selected_rows.set_rows(rows)
np_array = np.ones((len(rows), self.row_numel)).astype("float32")
np_array[0, 0] = 2.0
np_array[2, 8] = 4.0
grad_tensor = grad_selected_rows.get_tensor()
grad_tensor.set(np_array, place)
# create and initialize Param Variable
param = scope.var('Param').get_tensor()
param_array = np.full((height, self.row_numel), 5.0).astype("float32")
param.set(param_array, place)
# create and initialize LeraningRate Variable
lr = scope.var('LearningRate').get_tensor()
lr_array = np.full((1), 2.0).astype("float32")
lr.set(lr_array, place)
# create and run sgd operator
sgd_op = Operator(
"sgd",
Param='Param',
Grad='Grad',
ParamOut='Param',
LearningRate='LearningRate')
sgd_op.run(scope, place)
# get and compare result
result_array = np.array(param)
# rows[0] = 0, 5.0 - 2.0 * 2.0
self.assertAlmostEqual(1.0, result_array[rows[0], 0])
# rows[0] = 0, 5.0 - 2.0 * 1.0
self.assertAlmostEqual(3.0, result_array[rows[0], 2])
# 5.0 - 2.0 * 0.0
self.assertAlmostEqual(5.0, result_array[1, 0])
# rows[1] = 4, 5.0 - 2.0 * 1.0
self.assertAlmostEqual(3.0, result_array[rows[1], 10])
# 5.0 - 2.0 * 0.0
self.assertAlmostEqual(5.0, result_array[5, 8])
# rows[2] = 7, 5.0 - 2.0 * 1.0
self.assertAlmostEqual(3.0, result_array[rows[2], 1])
# rows[2] = 7, 5.0 - 2.0 * 4.0
self.assertAlmostEqual(-3.0, result_array[rows[2], 8])
def test_sparse_sgd(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
for place in places:
self.check_with_place(place)
def conf(self):
self.row_numel = 12
class TestSparseSGDOpCase8X(TestSparseSGDOp):
def conf(self):
self.row_numel = 16
class TestSGDOpOptimizeSelectedRows(unittest.TestCase):
def check_with_place(self, place):
scope = core.Scope()
row_width = 12
# create and initialize Grad Variable
grad_height = 10
grad_rows = [0, 4, 7]
grad_selected_rows = scope.var('Grad').get_selected_rows()
grad_selected_rows.set_height(grad_height)
grad_selected_rows.set_rows(grad_rows)
grad_array = np.ones((len(grad_rows), row_width)).astype("float32")
grad_array[0, 0] = 2.0
grad_array[2, 8] = 4.0
grad_tensor = grad_selected_rows.get_tensor()
grad_tensor.set(grad_array, place)
# create and initialize Param Variable
# create and initialize W Variable
param_rows = [0, 1, 2, 3, 4, 5, 6, 7]
# init Param
w_selected_rows = scope.var('Param').get_selected_rows()
w_selected_rows.set_height(len(param_rows))
w_selected_rows.set_rows(param_rows)
w_selected_rows.sync_index()
w_array = np.ones((len(param_rows), row_width)).astype("float32")
for i in range(len(param_rows)):
w_array[i] *= i
w_tensor = w_selected_rows.get_tensor()
w_tensor.set(w_array, place)
w_before_optimize = np.array(w_tensor)
# create and initialize LeraningRate Variable
lr_value = 0.1
lr = scope.var('LearningRate').get_tensor()
lr_array = np.full((1), lr_value).astype("float32")
lr.set(lr_array, place)
# optimize with Python
w_after_optimize = np.copy(w_before_optimize)
for index, id in enumerate(grad_rows):
w_after_optimize[id] = w_before_optimize[
id] - lr_value * grad_array[index]
# create and run sgd operator
sgd_op = Operator(
"sgd",
Param='Param',
Grad='Grad',
ParamOut='Param',
LearningRate='LearningRate')
sgd_op.run(scope, place)
# get and compare result
result_array = np.array(w_tensor)
assert (result_array == w_after_optimize).all()
def test_sparse_parameter_sgd(self):
places = [core.CPUPlace()]
# do not support GPU kernel currently
for place in places:
self.check_with_place(place)
class TestSGDOpWithLargeInput(unittest.TestCase):
def runTest(self):
data = fluid.layers.fill_constant(shape=[1], value=128, dtype='int64')
label = fluid.layers.fill_constant(
shape=[1, 150], value=0.5, dtype='float32')
emb = fluid.embedding(input=data, size=(10000000, 150), dtype='float32')
out = fluid.layers.l2_normalize(x=emb, axis=-1)
cost = fluid.layers.square_error_cost(input=out, label=label)
avg_cost = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
compiled_prog = fluid.compiler.CompiledProgram(
fluid.default_main_program())
result = exe.run(compiled_prog, fetch_list=[avg_cost])
class TestSGDV2(unittest.TestCase):
def test_sgd_dygraph(self):
paddle.disable_static()
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.to_tensor(value)
linear = paddle.nn.Linear(13, 5)
# This can be any optimizer supported by dygraph.
adam = paddle.optimizer.SGD(learning_rate=0.01,
parameters=linear.parameters(),
weight_decay=0.01)
out = linear(a)
out.backward()
adam.step()
adam.clear_gradients()
def test_sgd(self):
place = fluid.CPUPlace()
main = fluid.Program()
with fluid.program_guard(main):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
rms_optimizer = paddle.optimizer.SGD(learning_rate=0.1)
rms_optimizer.minimize(avg_cost)
fetch_list = [avg_cost]
train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), batch_size=1)
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
for data in train_reader():
exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
def test_raise_error(self):
self.assertRaises(ValueError, paddle.optimizer.SGD, learning_rate=None)
if __name__ == "__main__":
unittest.main()