wehub-resource-sync
247 lines
8.0 KiB
Python
247 lines
8.0 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 os
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import tempfile
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import unittest
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import numpy
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import numpy as np
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from op_test import is_custom_device
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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.base.framework import (
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convert_nptype_to_datatype_or_vartype,
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)
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from paddle.io import Dataset
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class TestOptimizerDtype(unittest.TestCase):
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'''
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The dtype of optimizer should be inferred by parameters, and the learning rate
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is created with the same dtype.
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'''
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def check_with_dtype(self, dtype):
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class MyLayer(paddle.nn.Layer):
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def __init__(self, dtype):
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super().__init__()
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self._w = self.create_parameter([2, 3], dtype=dtype)
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self._b = self.create_parameter([2, 3], dtype=dtype)
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def forward(self, x):
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return x * self._w + self._b
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with paddle.base.dygraph.guard():
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model = MyLayer(dtype)
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x = paddle.rand([10, 2, 3], dtype=dtype)
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loss = model(x)
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adam = paddle.optimizer.Adam(parameters=model.parameters())
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loss.backward()
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adam.step()
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self.assertEqual(
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adam._dtype, convert_nptype_to_datatype_or_vartype(dtype)
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)
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def test_float64(self):
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self.check_with_dtype('float64')
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def test_float32(self):
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self.check_with_dtype('float32')
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or paddle.device.cuda.get_device_capability()[0] < 7.0,
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"run test when gpu's compute capability is at least 7.0.",
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)
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class TestMasterWeightSaveForFP16(unittest.TestCase):
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'''
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For Amp-O2, some optimizer(Momentum, Adam ...) will create master weights for parameters to improve the accuracy.
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Master weights will be saved by optimizer::state_dict.
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'''
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def setUp(self):
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self.temp_dir = tempfile.TemporaryDirectory()
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def tearDown(self):
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self.temp_dir.cleanup()
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def check_with_opt_state_dict(self, use_save_load=True):
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paddle.seed(100)
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numpy.random.seed(100)
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class SimpleNet(paddle.nn.Layer):
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def __init__(self, input_size, output_size):
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super().__init__()
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self.linears = paddle.nn.LayerList(
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[
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paddle.nn.Linear(input_size, output_size)
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for i in range(1)
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]
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)
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def forward(self, x):
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for i, l in enumerate(self.linears):
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x = self.linears[i](x)
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return x
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input_size = 2 # 设为较大的值
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output_size = 2 # 设为较大的值
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batch_size = 2 # batch_size 为8的倍数
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nums_batch = 10
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class RandomDataset(Dataset):
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def __init__(self, num_samples):
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self.num_samples = num_samples
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def __getitem__(self, idx):
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data = numpy.random.random([input_size]).astype('float16')
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label = numpy.random.random([output_size]).astype('float16')
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return data, label
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def __len__(self):
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return self.num_samples
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dataset = RandomDataset(nums_batch * batch_size)
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loader = paddle.io.DataLoader(
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dataset,
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batch_size=batch_size,
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shuffle=False,
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drop_last=True,
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num_workers=0,
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)
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mse = paddle.nn.MSELoss()
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model = SimpleNet(input_size, output_size) # 定义模型
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optimizer = paddle.optimizer.Momentum(
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learning_rate=0.0001,
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parameters=model.parameters(),
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multi_precision=True,
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) # 定义优化器
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scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
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model = paddle.amp.decorate(models=model, level='O2')
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for i, (data, label) in enumerate(loader):
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with paddle.amp.auto_cast(level='O2'):
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output = model(data)
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loss = mse(output, label)
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scaled = scaler.scale(loss)
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scaled.backward()
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scaler.step(optimizer)
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scaler.update()
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optimizer.clear_grad(set_to_zero=False)
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if use_save_load and i == 5:
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model_path = os.path.join(self.temp_dir.name, "model.pdparams")
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optimizer_path = os.path.join(self.temp_dir.name, "opt.pdopt")
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paddle.save(model.state_dict(), model_path)
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paddle.save(optimizer.state_dict(), optimizer_path)
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model.set_state_dict(paddle.load(model_path))
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optimizer.set_state_dict(paddle.load(optimizer_path))
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return loss.numpy()
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def test_with_state_dict(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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with base.dygraph.guard():
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out_use_state_dict = self.check_with_opt_state_dict(
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use_save_load=True
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)
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out_no_state_dict = self.check_with_opt_state_dict(
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use_save_load=False
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)
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np.testing.assert_array_equal(out_use_state_dict, out_no_state_dict)
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class TestOptimizerAPI(unittest.TestCase):
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def test_weight_decay_int(self):
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paddle.disable_static()
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value = np.arange(26).reshape(2, 13).astype("float32")
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a = paddle.to_tensor(value)
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linear = paddle.nn.Linear(13, 5)
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adam = paddle.optimizer.SGD(
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learning_rate=0.01,
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parameters=linear.parameters(),
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weight_decay=1,
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)
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out = linear(a)
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out.backward()
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adam.step()
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adam.zero_grad(False)
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def test_step_without_closure(self):
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paddle.seed(100)
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numpy.random.seed(100)
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paddle.disable_static()
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x = paddle.arange(26, dtype="float32").reshape([2, 13])
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linear = paddle.nn.Linear(13, 5)
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optimizers = [
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paddle.optimizer.Adam(
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learning_rate=0.01,
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parameters=linear.parameters(),
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),
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paddle.optimizer.AdamW(
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learning_rate=0.01,
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parameters=linear.parameters(),
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),
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paddle.optimizer.ASGD(
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learning_rate=0.01,
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parameters=linear.parameters(),
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),
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]
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for optimizer in optimizers:
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optimizer.zero_grad()
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output = linear(x)
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loss = paddle.mean(output)
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loss.backward()
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optimizer.step()
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def test_step_with_closure(self):
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paddle.seed(100)
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numpy.random.seed(100)
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paddle.disable_static()
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x = paddle.arange(26, dtype="float32").reshape([2, 13])
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linear = paddle.nn.Linear(13, 5)
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optimizers = [
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paddle.optimizer.Adam(
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learning_rate=0.01,
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parameters=linear.parameters(),
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),
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paddle.optimizer.AdamW(
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learning_rate=0.01,
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parameters=linear.parameters(),
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),
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paddle.optimizer.ASGD(
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learning_rate=0.01,
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parameters=linear.parameters(),
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),
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]
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for optimizer in optimizers:
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def closure():
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optimizer.zero_grad()
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output = linear(x)
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loss = paddle.mean(output)
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loss.backward()
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return loss
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loss = optimizer.step(closure)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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