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test_train_step.py
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test_train_step.py
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# Copyright (c) 2023 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.
import random
import unittest
from functools import partial
import numpy as np
from dygraph_to_static_utils_new import Dy2StTestBase, test_legacy_and_pir
import paddle
def reset_seed():
paddle.seed(1010)
np.random.seed(1010)
random.seed(1010)
def loss_fn_tiny_model(x):
return x.mean()
def train_step_tiny_model(net, x, loss_fn, opt):
out = net(x)
loss = loss_fn(out)
loss.backward()
opt.step()
opt.clear_grad()
return loss
class TinyModel(paddle.nn.Layer):
def __init__(self):
super().__init__()
self.layer1 = paddle.nn.Linear(10, 10)
def forward(self, data):
return self.layer1(data)
class TestTrainStepTinyModel(Dy2StTestBase):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = lambda: 0.001
self.optimizer_creator = paddle.optimizer.SGD
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 5
self.rtol = 1e-4
def get_train_step_losses(self, func, steps):
losses = []
net = self.net_creator()
lr = self.lr_creator()
optimizer = self.optimizer_creator(
learning_rate=lr, parameters=net.parameters()
)
for _ in range(steps):
loss = func(net, self.input, self.loss_fn, optimizer)
if isinstance(lr, paddle.optimizer.lr.ReduceOnPlateau):
lr.step(loss)
elif isinstance(lr, paddle.optimizer.lr.LRScheduler):
lr.step()
losses.append(loss)
return losses
@test_legacy_and_pir
def test_train_step(self):
reset_seed()
dygraph_losses = self.get_train_step_losses(
self.train_step_func, self.steps
)
reset_seed()
static_func = paddle.jit.to_static(
self.train_step_func, full_graph=True
)
static_losses = self.get_train_step_losses(static_func, self.steps)
self.assertEqual(len(dygraph_losses), len(static_losses))
for dygraph_loss, static_loss in zip(dygraph_losses, static_losses):
dygraph_loss = dygraph_loss.numpy()
static_loss = static_loss.numpy()
np.testing.assert_allclose(
dygraph_loss, static_loss, rtol=self.rtol
)
class TestTrainStepTinyModelAdadelta(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = lambda: 0.001
self.optimizer_creator = paddle.optimizer.Adadelta
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelAdagrad(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = lambda: 0.001
self.optimizer_creator = paddle.optimizer.Adagrad
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelAdam(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = lambda: 0.001
self.optimizer_creator = paddle.optimizer.Adam
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelAdamax(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = lambda: 0.001
self.optimizer_creator = paddle.optimizer.Adamax
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelAdamW(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = lambda: 0.001
self.optimizer_creator = paddle.optimizer.AdamW
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelLamb(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = lambda: 0.001
self.optimizer_creator = partial(
paddle.optimizer.Lamb, lamb_weight_decay=0.01
)
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelMomentum(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = lambda: 0.001
self.optimizer_creator = paddle.optimizer.Momentum
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelRMSProp(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = lambda: 0.001
self.optimizer_creator = paddle.optimizer.RMSProp
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelLRNoamDecay(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = partial(
paddle.optimizer.lr.NoamDecay, d_model=0.01, warmup_steps=100
)
self.optimizer_creator = paddle.optimizer.SGD
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelLRPiecewiseDecay(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = partial(
paddle.optimizer.lr.PiecewiseDecay,
boundaries=[3, 6, 9],
values=[0.1, 0.2, 0.3, 0.4],
)
self.optimizer_creator = paddle.optimizer.SGD
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelLRNaturalExpDecay(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = partial(
paddle.optimizer.lr.NaturalExpDecay,
learning_rate=0.5,
gamma=0.1,
)
self.optimizer_creator = partial(paddle.optimizer.SGD)
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelLRInverseTimeDecay(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = partial(
paddle.optimizer.lr.InverseTimeDecay, learning_rate=0.5, gamma=0.1
)
self.optimizer_creator = paddle.optimizer.SGD
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelLRPolynomialDecay(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = partial(
paddle.optimizer.lr.PolynomialDecay,
learning_rate=0.5,
decay_steps=20,
)
self.optimizer_creator = paddle.optimizer.SGD
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelLRLinearWarmup(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = partial(
paddle.optimizer.lr.LinearWarmup,
learning_rate=0.5,
warmup_steps=2,
start_lr=0,
end_lr=0.5,
)
self.optimizer_creator = partial(paddle.optimizer.SGD)
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelLRExponentialDecay(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = partial(
paddle.optimizer.lr.ExponentialDecay, learning_rate=0.5, gamma=0.9
)
self.optimizer_creator = paddle.optimizer.SGD
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelLRMultiStepDecay(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = partial(
paddle.optimizer.lr.MultiStepDecay,
learning_rate=0.5,
milestones=[2, 4, 6],
gamma=0.8,
)
self.optimizer_creator = paddle.optimizer.SGD
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelLRStepDecay(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = partial(
paddle.optimizer.lr.StepDecay,
learning_rate=0.5,
step_size=5,
gamma=0.8,
)
self.optimizer_creator = paddle.optimizer.SGD
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelLRLambdaDecay(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = partial(
paddle.optimizer.lr.LambdaDecay,
learning_rate=0.5,
lr_lambda=lambda x: 0.95**x,
)
self.optimizer_creator = paddle.optimizer.SGD
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelLRReduceOnPlateau(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = partial(
paddle.optimizer.lr.ReduceOnPlateau,
learning_rate=1.0,
factor=0.5,
patience=5,
)
self.optimizer_creator = paddle.optimizer.SGD
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelLRCosineAnnealingDecay(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = partial(
paddle.optimizer.lr.CosineAnnealingDecay,
learning_rate=0.5,
T_max=10,
)
self.optimizer_creator = paddle.optimizer.SGD
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelLRMultiplicativeDecay(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = partial(
paddle.optimizer.lr.MultiplicativeDecay,
learning_rate=0.5,
lr_lambda=lambda x: 0.95,
)
self.optimizer_creator = paddle.optimizer.SGD
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelLROneCycleLR(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = partial(
paddle.optimizer.lr.OneCycleLR, max_learning_rate=1.0, total_steps=3
)
self.optimizer_creator = paddle.optimizer.SGD
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelLRCyclicLR(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = partial(
paddle.optimizer.lr.CyclicLR,
base_learning_rate=0.5,
max_learning_rate=1.0,
step_size_up=15,
step_size_down=5,
)
self.optimizer_creator = paddle.optimizer.SGD
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
class TestTrainStepTinyModelCosineAnnealingWarmRestarts(TestTrainStepTinyModel):
def setUp(self):
self.input = paddle.randn([10000, 10])
self.net_creator = TinyModel
self.lr_creator = partial(
paddle.optimizer.lr.CosineAnnealingWarmRestarts,
learning_rate=0.5,
T_0=1,
T_mult=1,
)
self.optimizer_creator = paddle.optimizer.SGD
self.loss_fn = loss_fn_tiny_model
self.train_step_func = train_step_tiny_model
self.steps = 3
self.rtol = 1e-4
if __name__ == "__main__":
unittest.main()