wehub-resource-sync
943 lines
30 KiB
Python
943 lines
30 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 itertools
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import unittest
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import numpy as np
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from op_test import get_device_place
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from test_imperative_base import new_program_scope
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import paddle
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from paddle import base
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from paddle.autograd.backward_utils import ValueDict
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from paddle.base import core
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from paddle.distributed.fleet.meta_optimizers import DGCMomentumOptimizer
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# Note(wangzhongpu)
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# In dygraph, don't support ModelAverage, DGCMomentumOptimizer, ExponentialMovingAverage, PipelineOptimizer, LookaheadOptimizer, RecomputeOptimizer.
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def create_parameter_mapping(startup_program, main_program):
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startup_params = {}
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main_params = {}
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parameter_mapping = ValueDict()
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for op in startup_program.global_block().ops:
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if op.name() == "builtin.set_parameter":
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name = op.attrs()["parameter_name"]
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param = op.operand(0).source()
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startup_params[name] = param
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for op in main_program.global_block().ops:
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if op.name() == "builtin.parameter":
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name = op.attrs()["parameter_name"]
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param = op.result(0)
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main_params[name] = param
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assert len(startup_params) == len(main_params)
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for name, startup_param in startup_params.items():
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assert name in main_params
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main_param = main_params[name]
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parameter_mapping[main_param] = startup_param
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return parameter_mapping
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class MLP(paddle.nn.Layer):
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def __init__(self, param_attr=None, bias_attr=None):
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super().__init__()
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self._fc1 = paddle.nn.Linear(784, 10)
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self._fc2 = paddle.nn.Linear(10, 10)
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def forward(self, inputs):
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y = self._fc1(inputs)
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y = self._fc2(y)
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return y
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class TestImperativeOptimizerBase(unittest.TestCase):
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def setUp(self):
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self.batch_num = 20
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def get_optimizer_dygraph(self, parameter_list):
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raise NotImplementedError
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def get_optimizer(self):
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raise NotImplementedError
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def reader_decorator(self, reader):
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def _reader_simple():
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for item in reader():
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image = np.array(item[0]).reshape(1, 784)
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label = np.array(item[1]).astype('int64').reshape(1)
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yield image, label
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return _reader_simple
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def _check_exception(self, exception_message, place=None):
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seed = 90
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batch_size = 128
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if place is None:
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place = get_device_place()
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try:
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paddle.disable_static()
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paddle.seed(seed)
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if paddle.framework.use_pir_api():
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with paddle.pir_utils.OldIrGuard():
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# Note: dygraph use self.main_program.global_block().create_parameter(), it's need manual seed to old Program
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paddle.framework.random._manual_program_seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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else:
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paddle.framework.random._manual_program_seed(seed)
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mlp = MLP()
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optimizer = self.get_optimizer_dygraph(
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parameter_list=mlp.parameters()
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)
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except Exception as e:
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assert str(e) == exception_message
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finally:
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paddle.enable_static()
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def _check_mlp(self, place=None):
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seed = 90
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batch_size = 128
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if place is None:
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place = get_device_place()
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paddle.disable_static(place)
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paddle.seed(seed)
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if paddle.framework.use_pir_api():
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with paddle.pir_utils.OldIrGuard():
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# Note: dygraph use self.main_program.global_block().create_parameter(), it's need manual seed to old Program
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paddle.framework.random._manual_program_seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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else:
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paddle.framework.random._manual_program_seed(seed)
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mlp = MLP()
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optimizer = self.get_optimizer_dygraph(parameter_list=mlp.parameters())
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batch_py_reader = base.io.PyReader(capacity=1)
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batch_py_reader.decorate_sample_list_generator(
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paddle.batch(
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self.reader_decorator(paddle.dataset.mnist.train()),
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batch_size=batch_size,
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drop_last=True,
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),
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places=base.CPUPlace(),
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)
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dy_param_init_value = {}
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for batch_id, data in enumerate(batch_py_reader()):
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if batch_id >= self.batch_num:
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break
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img = data[0]
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label = data[1]
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label.stop_gradient = True
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img = paddle.reshape(img, shape=[batch_size, -1])
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cost = mlp(img)
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avg_loss = paddle.mean(cost)
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dy_out = avg_loss.numpy()
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if batch_id == 0:
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for param in mlp.parameters():
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dy_param_init_value[param.name] = param.numpy()
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avg_loss.backward()
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optimizer.minimize(avg_loss)
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if isinstance(
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optimizer._learning_rate, paddle.optimizer.lr.LRScheduler
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):
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if isinstance(
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optimizer._learning_rate,
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paddle.optimizer.lr.ReduceOnPlateau,
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):
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optimizer._learning_rate.step(avg_loss)
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else:
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optimizer._learning_rate.step()
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mlp.clear_gradients()
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dy_param_value = {}
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for param in mlp.parameters():
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dy_param_value[param.name] = param.numpy()
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paddle.enable_static()
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with new_program_scope():
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paddle.seed(seed)
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if paddle.framework.use_pir_api():
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with paddle.pir_utils.OldIrGuard():
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# Note: dygraph use self.main_program.global_block().create_parameter(), it's need manual seed to old Program
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paddle.framework.random._manual_program_seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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else:
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paddle.framework.random._manual_program_seed(seed)
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if place is None:
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place = get_device_place()
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exe = base.Executor(place)
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mlp = MLP()
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optimizer = self.get_optimizer()
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train_reader = paddle.batch(
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paddle.dataset.mnist.train(), batch_size=128, drop_last=True
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)
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img = paddle.static.data(
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name='pixel', shape=[-1, 1, 28, 28], dtype='float32'
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)
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label = paddle.static.data(
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name='label', shape=[-1, 1], dtype='int64'
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)
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img = paddle.reshape(img, shape=[batch_size, 784])
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cost = mlp(img)
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avg_loss = paddle.mean(cost)
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optimizer.minimize(avg_loss)
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# initialize params and fetch them
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static_param_init_value = {}
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static_param_name_list = []
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static_params = []
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for param in mlp.parameters():
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static_param_name_list.append(param.name)
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static_params.append(param)
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if paddle.framework.use_pir_api():
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parameter_mapping = create_parameter_mapping(
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paddle.static.default_startup_program(),
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paddle.static.default_main_program(),
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)
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startup_params = [
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parameter_mapping[param] for param in static_params
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]
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else:
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startup_params = static_params
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out = exe.run(
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paddle.static.default_startup_program(),
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fetch_list=startup_params,
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)
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for i in range(len(static_params)):
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param_name = static_param_name_list[i]
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static_param_init_value[param_name] = out[i]
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for batch_id, data in enumerate(train_reader()):
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if batch_id >= self.batch_num:
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break
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static_x_data = np.array(
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[x[0].reshape(1, 28, 28) for x in data]
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).astype('float32')
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y_data = (
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np.array([x[1] for x in data])
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.astype('int64')
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.reshape([128, 1])
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)
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fetch_list = [avg_loss]
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fetch_list.extend(static_params)
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out = exe.run(
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base.default_main_program(),
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feed={"pixel": static_x_data, "label": y_data},
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fetch_list=fetch_list,
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)
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if isinstance(
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optimizer._learning_rate, paddle.optimizer.lr.LRScheduler
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):
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if isinstance(
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optimizer._learning_rate,
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paddle.optimizer.lr.ReduceOnPlateau,
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):
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optimizer._learning_rate.step(out[0])
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else:
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optimizer._learning_rate.step()
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static_param_value = {}
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static_out = out[0]
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for i in range(1, len(out)):
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static_param_value[static_param_name_list[i - 1]] = out[i]
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for key, value in static_param_init_value.items():
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np.testing.assert_allclose(
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value, dy_param_init_value[key], rtol=1e-05
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)
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if core.is_compiled_with_rocm():
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np.testing.assert_allclose(
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static_out, dy_out, rtol=1e-05, atol=0.001
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)
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else:
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np.testing.assert_allclose(static_out, dy_out, rtol=1e-05)
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for key, value in static_param_value.items():
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if core.is_compiled_with_rocm():
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np.testing.assert_allclose(
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value, dy_param_value[key], rtol=1e-05, atol=0.001
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)
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else:
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np.testing.assert_allclose(
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value, dy_param_value[key], rtol=1e-05
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)
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class TestImperativeOptimizerPiecewiseDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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bd = [3, 6, 9]
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.PiecewiseDecay(
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boundaries=bd,
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values=[0.1 * (0.1**i) for i in range(len(bd) + 1)],
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),
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parameters=parameter_list,
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)
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return optimizer
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def get_optimizer(self):
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bd = [3, 6, 9]
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.PiecewiseDecay(
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boundaries=bd,
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values=[0.1 * (0.1**i) for i in range(len(bd) + 1)],
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)
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)
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return optimizer
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def test_sgd(self):
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self._check_mlp()
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class TestImperativeOptimizerNaturalExpDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.NaturalExpDecay(
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learning_rate=0.5, gamma=0.9
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),
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parameters=parameter_list,
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)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.NaturalExpDecay(
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learning_rate=0.5, gamma=0.9
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)
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)
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return optimizer
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def test_sgd(self):
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self._check_mlp()
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class TestImperativeOptimizerExponentialDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.ExponentialDecay(
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learning_rate=0.5, gamma=0.9
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),
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parameters=parameter_list,
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)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.ExponentialDecay(
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learning_rate=0.5, gamma=0.9
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)
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)
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return optimizer
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def test_sgd(self):
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self._check_mlp()
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class TestImperativeOptimizerInverseTimeDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.Adam(
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learning_rate=paddle.optimizer.lr.InverseTimeDecay(
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learning_rate=0.5, gamma=0.9
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),
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parameters=parameter_list,
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)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.Adam(
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learning_rate=paddle.optimizer.lr.InverseTimeDecay(
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learning_rate=0.5, gamma=0.9
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)
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)
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return optimizer
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def test_adam(self):
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self._check_mlp()
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class TestImperativeOptimizerPolynomialDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.PolynomialDecay(
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learning_rate=0.5, decay_steps=5, cycle=self.cycle
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),
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parameters=parameter_list,
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)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.PolynomialDecay(
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learning_rate=0.5, decay_steps=5, cycle=self.cycle
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)
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)
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return optimizer
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def test_sgd_cycle(self):
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self.cycle = True
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self._check_mlp()
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def test_sgd(self):
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self.cycle = False
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self._check_mlp()
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class TestImperativeOptimizerCosineAnnealingDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.CosineAnnealingDecay(
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learning_rate=0.5, T_max=5
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),
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parameters=parameter_list,
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)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.CosineAnnealingDecay(
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learning_rate=0.5, T_max=5
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)
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)
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return optimizer
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def test_sgd(self):
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self._check_mlp()
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class TestImperativeOptimizerNoamDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.NoamDecay(
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d_model=0.01, warmup_steps=100, verbose=True
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),
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parameters=parameter_list,
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)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.NoamDecay(
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d_model=0.01, warmup_steps=100
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)
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)
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return optimizer
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def test_sgd(self):
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self._check_mlp()
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class TestImperativeOptimizerLambdaDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.LambdaDecay(
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learning_rate=0.5, lr_lambda=lambda epoch: 0.9**epoch
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),
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parameters=parameter_list,
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)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.LambdaDecay(
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learning_rate=0.5, lr_lambda=lambda epoch: 0.9**epoch
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)
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)
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return optimizer
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def test_sgd(self):
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self._check_mlp()
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class TestImperativeOptimizerLinearWarmup(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.LinearWarmup(
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learning_rate=0.5, warmup_steps=20, start_lr=0, end_lr=0.5
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),
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parameters=parameter_list,
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)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.LinearWarmup(
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learning_rate=0.5,
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warmup_steps=20,
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start_lr=0,
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end_lr=0.5,
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verbose=True,
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)
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)
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return optimizer
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def test_sgd(self):
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self._check_mlp()
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class TestImperativeOptimizerMultiStepDecay(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.MultiStepDecay(
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learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8
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),
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parameters=parameter_list,
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)
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return optimizer
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def get_optimizer(self):
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optimizer = paddle.optimizer.SGD(
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learning_rate=paddle.optimizer.lr.MultiStepDecay(
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learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8
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)
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)
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return optimizer
|
|
|
|
def test_sgd(self):
|
|
self._check_mlp()
|
|
|
|
|
|
class TestImperativeOptimizerStepLR(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = paddle.optimizer.SGD(
|
|
learning_rate=paddle.optimizer.lr.StepDecay(
|
|
learning_rate=0.5, step_size=5, gamma=0.8
|
|
),
|
|
parameters=parameter_list,
|
|
)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = paddle.optimizer.SGD(
|
|
learning_rate=paddle.optimizer.lr.StepDecay(
|
|
learning_rate=0.5, step_size=5, gamma=0.8
|
|
)
|
|
)
|
|
return optimizer
|
|
|
|
def test_sgd(self):
|
|
self._check_mlp()
|
|
|
|
|
|
class TestImperativeOptimizerReduceOnPlateau(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = paddle.optimizer.SGD(
|
|
learning_rate=paddle.optimizer.lr.ReduceOnPlateau(
|
|
learning_rate=0.5
|
|
),
|
|
parameters=parameter_list,
|
|
)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = paddle.optimizer.SGD(
|
|
learning_rate=paddle.optimizer.lr.ReduceOnPlateau(learning_rate=0.5)
|
|
)
|
|
return optimizer
|
|
|
|
def test_sgd(self):
|
|
self._check_mlp()
|
|
|
|
|
|
class TestOptimizerLearningRate(unittest.TestCase):
|
|
def test_constant_lr(self):
|
|
with base.dygraph.guard():
|
|
a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
|
|
|
|
linear = paddle.nn.Linear(10, 10)
|
|
|
|
a = paddle.to_tensor(a)
|
|
|
|
b = linear(a)
|
|
|
|
loss = paddle.mean(b)
|
|
|
|
adam = paddle.optimizer.Adam(0.001, parameters=linear.parameters())
|
|
|
|
np.testing.assert_allclose(
|
|
adam.get_lr(), 0.001, rtol=1e-06, atol=0.0
|
|
)
|
|
|
|
for i in range(10):
|
|
adam.minimize(loss)
|
|
lr = adam.get_lr()
|
|
|
|
np.testing.assert_allclose(lr, 0.001, rtol=1e-06, atol=0.0)
|
|
|
|
def test_lr_decay(self):
|
|
with base.dygraph.guard():
|
|
a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
|
|
|
|
linear = paddle.nn.Linear(10, 10)
|
|
|
|
a = paddle.to_tensor(a)
|
|
|
|
b = linear(a)
|
|
|
|
loss = paddle.mean(b)
|
|
|
|
bd = [2, 4, 6, 8]
|
|
value = [0.2, 0.4, 0.6, 0.8, 1.0]
|
|
|
|
scheduler = paddle.optimizer.lr.PiecewiseDecay(bd, value)
|
|
adam = paddle.optimizer.Adam(
|
|
scheduler, parameters=linear.parameters()
|
|
)
|
|
|
|
np.testing.assert_allclose(adam.get_lr(), 0.2, rtol=1e-06, atol=0.0)
|
|
|
|
ret = [0.2, 0.2, 0.4, 0.4, 0.6, 0.6, 0.8, 0.8, 1.0, 1.0, 1.0, 1.0]
|
|
for i in range(12):
|
|
adam.minimize(loss)
|
|
lr = adam.get_lr()
|
|
np.testing.assert_allclose(lr, ret[i], rtol=1e-06, atol=0.0)
|
|
scheduler.step()
|
|
|
|
def test_lr_scheduler_natural_exp(self):
|
|
with base.dygraph.guard():
|
|
a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
|
|
|
|
linear = paddle.nn.Linear(10, 10)
|
|
a = paddle.to_tensor(a)
|
|
b = linear(a)
|
|
|
|
loss = paddle.mean(b)
|
|
base_lr = 1.0
|
|
|
|
scheduler = paddle.optimizer.lr.NaturalExpDecay(1.0, gamma=0.5)
|
|
adam = paddle.optimizer.Adam(
|
|
scheduler, parameters=linear.parameters()
|
|
)
|
|
|
|
np.testing.assert_allclose(adam.get_lr(), 1.0, rtol=1e-06, atol=0.0)
|
|
|
|
ret = [1.0, np.exp(-0.5), np.exp(-1)]
|
|
for i in range(3):
|
|
adam.minimize(loss)
|
|
lr = adam.get_lr()
|
|
np.testing.assert_allclose(lr, ret[i], rtol=1e-06, atol=0.0)
|
|
scheduler.step()
|
|
|
|
def test_set_lr(self):
|
|
with base.dygraph.guard():
|
|
a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
|
|
|
|
linear = paddle.nn.Linear(10, 10)
|
|
|
|
a = paddle.to_tensor(a)
|
|
|
|
b = linear(a)
|
|
|
|
loss = paddle.mean(b)
|
|
|
|
adam = paddle.optimizer.Adam(0.1, parameters=linear.parameters())
|
|
|
|
lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
|
|
for i in range(5):
|
|
adam.set_lr(lr_list[i])
|
|
adam.minimize(loss)
|
|
lr = adam.get_lr()
|
|
np.testing.assert_allclose(lr, lr_list[i], rtol=1e-06, atol=0.0)
|
|
|
|
with self.assertRaises(TypeError):
|
|
lr_var = paddle.static.create_global_var(
|
|
shape=[1], value=0.7, dtype='float32'
|
|
)
|
|
adam.set_lr(lr_var)
|
|
|
|
with self.assertRaises(RuntimeError):
|
|
adam = paddle.optimizer.Adam(
|
|
paddle.optimizer.lr.NaturalExpDecay(
|
|
learning_rate=0.1, gamma=0.5
|
|
),
|
|
parameters=linear.parameters(),
|
|
)
|
|
adam.set_lr(0.01)
|
|
|
|
def test_set_lr_scheduler(self):
|
|
with base.dygraph.guard():
|
|
a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
|
|
|
|
linear = paddle.nn.Linear(10, 10)
|
|
|
|
a = paddle.to_tensor(a)
|
|
|
|
b = linear(a)
|
|
|
|
loss = paddle.mean(b)
|
|
|
|
adam = paddle.optimizer.Adam(0.1, parameters=linear.parameters())
|
|
|
|
# float to LRScheduler
|
|
scheduler = paddle.optimizer.lr.StepDecay(
|
|
learning_rate=0.2, step_size=5, gamma=0.6
|
|
)
|
|
adam.set_lr_scheduler(scheduler)
|
|
adam.minimize(loss)
|
|
lr = adam.get_lr()
|
|
np.testing.assert_allclose(lr, 0.2, rtol=1e-06, atol=0.0)
|
|
|
|
# LRScheduler to another LRScheduler
|
|
scheduler = paddle.optimizer.lr.MultiStepDecay(
|
|
learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8
|
|
)
|
|
adam.set_lr_scheduler(scheduler)
|
|
adam.minimize(loss)
|
|
lr = adam.get_lr()
|
|
np.testing.assert_allclose(lr, 0.5, rtol=1e-06, atol=0.0)
|
|
|
|
|
|
class TestImperativeMomentumOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = paddle.optimizer.Momentum(
|
|
learning_rate=0.001, momentum=0.9, parameters=parameter_list
|
|
)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = paddle.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
|
|
return optimizer
|
|
|
|
def test_momentum(self):
|
|
self._check_mlp()
|
|
|
|
|
|
class TestImperativeLarsMomentumOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = paddle.incubate.optimizer.LarsMomentumOptimizer(
|
|
learning_rate=0.001, momentum=0.9, parameter_list=parameter_list
|
|
)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = paddle.incubate.optimizer.LarsMomentumOptimizer(
|
|
learning_rate=0.001, momentum=0.9
|
|
)
|
|
return optimizer
|
|
|
|
def test_larsmomentum(self):
|
|
self._check_mlp()
|
|
|
|
|
|
class TestImperativeAdagradOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = paddle.optimizer.Adagrad(
|
|
learning_rate=0.2, parameters=parameter_list
|
|
)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = paddle.optimizer.Adagrad(learning_rate=0.2)
|
|
return optimizer
|
|
|
|
def test_adagrad(self):
|
|
self._check_mlp()
|
|
|
|
|
|
class TestImperativeAdamaxOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = paddle.optimizer.Adamax(
|
|
learning_rate=0.2, parameters=parameter_list
|
|
)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = paddle.optimizer.Adamax(learning_rate=0.2)
|
|
return optimizer
|
|
|
|
def test_adamax(self):
|
|
self._check_mlp()
|
|
|
|
|
|
class TestImperativeAdadeltaOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = paddle.optimizer.Adadelta(
|
|
learning_rate=0.0003,
|
|
epsilon=1.0e-6,
|
|
rho=0.95,
|
|
parameters=parameter_list,
|
|
)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = paddle.optimizer.Adadelta(
|
|
learning_rate=0.0003, epsilon=1.0e-6, rho=0.95
|
|
)
|
|
return optimizer
|
|
|
|
def test_adadelta(self):
|
|
self._check_mlp()
|
|
|
|
|
|
class TestImperativeRMSPropOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = paddle.optimizer.RMSProp(
|
|
learning_rate=0.1, parameters=parameter_list
|
|
)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = paddle.optimizer.RMSProp(learning_rate=0.1)
|
|
return optimizer
|
|
|
|
def test_rmsprop(self):
|
|
self._check_mlp()
|
|
|
|
|
|
def exclude_fn(param):
|
|
return param.name.endswith('.b_0')
|
|
|
|
|
|
class TestImperativeLambOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = paddle.optimizer.Lamb(
|
|
learning_rate=0.002,
|
|
exclude_from_weight_decay_fn=exclude_fn,
|
|
parameters=parameter_list,
|
|
)
|
|
return optimizer
|
|
|
|
def get_optimizer(self):
|
|
optimizer = paddle.optimizer.Lamb(
|
|
learning_rate=0.002, exclude_from_weight_decay_fn=exclude_fn
|
|
)
|
|
return optimizer
|
|
|
|
# should fix: may fail in CI-windows
|
|
|
|
def _test_lamb(self):
|
|
self._check_mlp()
|
|
|
|
|
|
class TestImperativeDGCMomentumOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = DGCMomentumOptimizer(
|
|
learning_rate=0.0001,
|
|
momentum=0.9,
|
|
rampup_step=1000,
|
|
rampup_begin_step=1252,
|
|
sparsity=[0.999, 0.999],
|
|
)
|
|
return optimizer
|
|
|
|
def test_dgcmomentum(self):
|
|
exception_message = "In dygraph, don't support DGCMomentumOptimizer."
|
|
self._check_exception(exception_message)
|
|
|
|
|
|
class TestImperativeExponentialMovingAverage(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = paddle.static.ExponentialMovingAverage(0.999)
|
|
return optimizer
|
|
|
|
def test_exponentialmoving(self):
|
|
exception_message = (
|
|
"In dygraph, don't support ExponentialMovingAverage."
|
|
)
|
|
self._check_exception(exception_message)
|
|
|
|
|
|
class TestImperativePipelineOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = paddle.optimizer.SGD(
|
|
learning_rate=0.5, parameters=parameter_list
|
|
)
|
|
optimizer = paddle.incubate.optimizer.PipelineOptimizer(optimizer)
|
|
return optimizer
|
|
|
|
def test_pipeline(self):
|
|
exception_message = "In dygraph, don't support PipelineOptimizer."
|
|
self._check_exception(exception_message)
|
|
|
|
|
|
class TestImperativeLookaheadOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = paddle.optimizer.SGD(
|
|
learning_rate=0.5, parameters=parameter_list
|
|
)
|
|
optimizer = paddle.incubate.optimizer.LookAhead(
|
|
optimizer, alpha=0.5, k=5
|
|
)
|
|
return optimizer
|
|
|
|
def test_lookahead(self):
|
|
exception_message = "In dygraph, don't support LookaheadOptimizer."
|
|
self._check_exception(exception_message)
|
|
|
|
|
|
class TestImperativeRecomputeOptimizer(TestImperativeOptimizerBase):
|
|
def get_optimizer_dygraph(self, parameter_list):
|
|
optimizer = paddle.optimizer.SGD(
|
|
learning_rate=0.5, parameters=parameter_list
|
|
)
|
|
optimizer = paddle.incubate.optimizer.RecomputeOptimizer(optimizer)
|
|
return optimizer
|
|
|
|
def test_recompute(self):
|
|
exception_message = "In dygraph, don't support RecomputeOptimizer."
|
|
self._check_exception(exception_message)
|
|
|
|
|
|
class TestImperativeOptimizerList(unittest.TestCase):
|
|
def test_parameter_list(self):
|
|
with base.dygraph.guard():
|
|
linear_1 = paddle.nn.Linear(10, 10)
|
|
linear_2 = paddle.nn.Linear(10, 10)
|
|
|
|
sgd = paddle.optimizer.SGD(
|
|
1.0,
|
|
parameters=itertools.chain(
|
|
linear_1.parameters(), linear_2.parameters()
|
|
),
|
|
)
|
|
|
|
in_np = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
|
|
in_data = paddle.to_tensor(in_np)
|
|
|
|
y = linear_1(in_data)
|
|
y = linear_2(y)
|
|
loss = paddle.mean(y)
|
|
loss.backward()
|
|
sgd.minimize(loss)
|
|
|
|
self.assertTrue(
|
|
len(sgd._parameter_list)
|
|
== len(linear_1.parameters() + linear_2.parameters())
|
|
)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|