wehub-resource-sync
365 lines
11 KiB
Python
365 lines
11 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 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.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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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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with base.dygraph.guard(place):
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try:
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paddle.seed(seed)
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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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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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with base.dygraph.guard(place):
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paddle.seed(seed)
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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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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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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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with new_program_scope():
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paddle.seed(seed)
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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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for param in mlp.parameters():
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static_param_name_list.append(param.name)
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out = exe.run(
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base.default_startup_program(),
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fetch_list=static_param_name_list,
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)
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for i in range(len(static_param_name_list)):
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static_param_init_value[static_param_name_list[i]] = 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.name]
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fetch_list.extend(static_param_name_list)
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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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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 TestOptimizerLearningRate(unittest.TestCase):
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def test_constant_lr(self):
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with base.dygraph.guard():
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a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
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linear = paddle.nn.Linear(10, 10)
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a = paddle.to_tensor(a)
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b = linear(a)
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loss = paddle.mean(b)
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adam = paddle.optimizer.Adam(0.001, parameters=linear.parameters())
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np.testing.assert_allclose(
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adam.get_lr(), 0.001, rtol=1e-06, atol=0.0
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)
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for i in range(10):
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adam.minimize(loss)
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lr = adam.get_lr()
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np.testing.assert_allclose(lr, 0.001, rtol=1e-06, atol=0.0)
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def test_lr_decay(self):
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with base.dygraph.guard():
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a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
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linear = paddle.nn.Linear(10, 10)
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a = paddle.to_tensor(a)
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b = linear(a)
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loss = paddle.mean(b)
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bd = [2, 4, 6, 8]
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value = [0.2, 0.4, 0.6, 0.8, 1.0]
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scheduler = paddle.optimizer.lr.PiecewiseDecay(bd, value)
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adam = paddle.optimizer.Adam(
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scheduler,
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parameters=linear.parameters(),
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)
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np.testing.assert_allclose(adam.get_lr(), 0.2, rtol=1e-06, atol=0.0)
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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]
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for i in range(12):
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adam.minimize(loss)
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lr = adam.get_lr()
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adam.step()
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scheduler.step()
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np.testing.assert_allclose(lr, ret[i], rtol=1e-06, atol=0.0)
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def test_lr_decay_natural_exp(self):
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with base.dygraph.guard():
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a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
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linear = paddle.nn.Linear(10, 10)
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a = paddle.to_tensor(a)
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b = linear(a)
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loss = paddle.mean(b)
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base_lr = 1.0
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scheduler = paddle.optimizer.lr.NaturalExpDecay(
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learning_rate=base_lr,
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gamma=0.5,
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)
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adam = paddle.optimizer.Adam(
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learning_rate=scheduler,
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parameters=linear.parameters(),
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)
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np.testing.assert_allclose(adam.get_lr(), 1.0, rtol=1e-06, atol=0.0)
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ret = [1.0, 1.0, 1.0, np.exp(-0.5), np.exp(-0.5)]
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counter = 0
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for i in range(5):
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adam.minimize(loss)
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lr = adam.get_lr()
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counter += 1
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if counter % 3 == 0:
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adam.step()
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scheduler.step()
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np.testing.assert_allclose(lr, ret[i], rtol=1e-06, atol=0.0)
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def test_set_lr(self):
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with base.dygraph.guard():
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a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
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linear = paddle.nn.Linear(10, 10)
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a = paddle.to_tensor(a)
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b = linear(a)
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loss = paddle.mean(b)
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adam = paddle.optimizer.Adam(0.1, parameters=linear.parameters())
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lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
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for i in range(5):
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adam.set_lr(lr_list[i])
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adam.minimize(loss)
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lr = adam.get_lr()
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np.testing.assert_allclose(lr, lr_list[i], rtol=1e-06, atol=0.0)
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with self.assertRaises(RuntimeError):
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adam = paddle.optimizer.Adam(
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paddle.optimizer.lr.NaturalExpDecay(
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learning_rate=0.1,
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gamma=0.5,
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),
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parameters=linear.parameters(),
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)
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adam.set_lr(0.01)
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def exclude_fn(param):
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return param.name.endswith('.b_0')
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class TestImperativeDGCMomentumOptimizer(TestImperativeOptimizerBase):
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def get_optimizer_dygraph(self, parameter_list):
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optimizer = DGCMomentumOptimizer(
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learning_rate=0.0001,
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momentum=0.9,
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rampup_step=1000,
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rampup_begin_step=1252,
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sparsity=[0.999, 0.999],
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)
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return optimizer
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def test_dgcmomentum(self):
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exception_message = "In dygraph, don't support DGCMomentumOptimizer."
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self._check_exception(exception_message)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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