chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,281 @@
|
||||
# Copyright (c) 2022 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 os
|
||||
import sys
|
||||
import tempfile
|
||||
import time
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
|
||||
EPOCH_NUM = 1
|
||||
BATCH_SIZE = 1024
|
||||
|
||||
|
||||
def train_func_base(epoch_id, train_loader, model, cost, optimizer):
|
||||
total_step = len(train_loader)
|
||||
epoch_start = time.time()
|
||||
for batch_id, (images, labels) in enumerate(train_loader()):
|
||||
# forward
|
||||
outputs = model(images)
|
||||
loss = cost(outputs, labels)
|
||||
# backward and optimize
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
optimizer.clear_grad()
|
||||
print(
|
||||
f"Epoch [{epoch_id + 1}/{EPOCH_NUM}], Step [{batch_id + 1}/{total_step}], Loss: {loss.numpy()}"
|
||||
)
|
||||
epoch_end = time.time()
|
||||
print(
|
||||
f"Epoch ID: {epoch_id + 1}, FP32 train epoch time: {(epoch_end - epoch_start) * 1000} ms"
|
||||
)
|
||||
|
||||
|
||||
def train_func_ampo1(epoch_id, train_loader, model, cost, optimizer, scaler):
|
||||
import paddle
|
||||
|
||||
total_step = len(train_loader)
|
||||
epoch_start = time.time()
|
||||
for batch_id, (images, labels) in enumerate(train_loader()):
|
||||
# forward
|
||||
with paddle.amp.auto_cast(
|
||||
custom_black_list={
|
||||
"flatten_contiguous_range",
|
||||
"greater_than",
|
||||
"matmul_v2",
|
||||
},
|
||||
level='O1',
|
||||
):
|
||||
outputs = model(images)
|
||||
loss = cost(outputs, labels)
|
||||
# backward and optimize
|
||||
scaled = scaler.scale(loss)
|
||||
scaled.backward()
|
||||
scaler.minimize(optimizer, scaled)
|
||||
optimizer.clear_grad()
|
||||
print(
|
||||
f"Epoch [{epoch_id + 1}/{EPOCH_NUM}], Step [{batch_id + 1}/{total_step}], Loss: {loss.numpy()}"
|
||||
)
|
||||
epoch_end = time.time()
|
||||
print(
|
||||
f"Epoch ID: {epoch_id + 1}, AMPO1 train epoch time: {(epoch_end - epoch_start) * 1000} ms"
|
||||
)
|
||||
|
||||
|
||||
def test_func(epoch_id, test_loader, model, cost):
|
||||
import paddle
|
||||
|
||||
# evaluation every epoch finish
|
||||
model.eval()
|
||||
avg_acc = [[], []]
|
||||
for batch_id, (images, labels) in enumerate(test_loader()):
|
||||
# forward
|
||||
outputs = model(images)
|
||||
loss = cost(outputs, labels)
|
||||
# accuracy
|
||||
acc_top1 = paddle.metric.accuracy(input=outputs, label=labels, k=1)
|
||||
acc_top5 = paddle.metric.accuracy(input=outputs, label=labels, k=5)
|
||||
avg_acc[0].append(acc_top1.numpy())
|
||||
avg_acc[1].append(acc_top5.numpy())
|
||||
model.train()
|
||||
print(
|
||||
f"Epoch ID: {epoch_id + 1}, Top1 accuracy: {np.array(avg_acc[0]).mean()}, Top5 accuracy: {np.array(avg_acc[1]).mean()}"
|
||||
)
|
||||
|
||||
|
||||
class TestCustomCPUPlugin(unittest.TestCase):
|
||||
def setUp(self):
|
||||
# compile so and set to current path
|
||||
cur_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
self.temp_dir = tempfile.TemporaryDirectory()
|
||||
cmd = 'cd {} \
|
||||
&& git clone --depth 1 {} \
|
||||
&& cd PaddleCustomDevice \
|
||||
&& git fetch origin \
|
||||
&& git checkout {} -b dev \
|
||||
&& cd backends/custom_cpu \
|
||||
&& mkdir build && cd build && cmake .. -DPython_EXECUTABLE={} -DWITH_TESTING=OFF && make -j8'.format(
|
||||
self.temp_dir.name,
|
||||
os.getenv('PLUGIN_URL'),
|
||||
os.getenv('PLUGIN_TAG'),
|
||||
sys.executable,
|
||||
)
|
||||
os.system(cmd)
|
||||
|
||||
# set environment for loading and registering compiled custom kernels
|
||||
# only valid in current process
|
||||
os.environ['CUSTOM_DEVICE_ROOT'] = os.path.join(
|
||||
cur_dir,
|
||||
f'{self.temp_dir.name}/PaddleCustomDevice/backends/custom_cpu/build',
|
||||
)
|
||||
|
||||
def tearDown(self):
|
||||
self.temp_dir.cleanup()
|
||||
|
||||
def test_custom_cpu_plugin(self):
|
||||
self._test_to_static()
|
||||
self._test_amp_o1()
|
||||
|
||||
def _test_to_static(self):
|
||||
import paddle
|
||||
|
||||
class LeNet5(paddle.nn.Layer):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.fc = paddle.nn.Linear(in_features=1024, out_features=10)
|
||||
self.relu = paddle.nn.ReLU()
|
||||
self.fc1 = paddle.nn.Linear(in_features=10, out_features=10)
|
||||
|
||||
def forward(self, x):
|
||||
out = paddle.flatten(x, 1)
|
||||
out = self.fc(out)
|
||||
out = self.relu(out)
|
||||
out = self.fc1(out)
|
||||
return out
|
||||
|
||||
# set device
|
||||
paddle.set_device('custom_cpu')
|
||||
|
||||
# model
|
||||
model = LeNet5()
|
||||
|
||||
# cost and optimizer
|
||||
cost = paddle.nn.CrossEntropyLoss()
|
||||
optimizer = paddle.optimizer.Adam(
|
||||
learning_rate=0.001, parameters=model.parameters()
|
||||
)
|
||||
|
||||
# convert to static model
|
||||
build_strategy = paddle.static.BuildStrategy()
|
||||
mnist = paddle.jit.to_static(
|
||||
model, build_strategy=build_strategy, full_graph=True
|
||||
)
|
||||
|
||||
# data loader
|
||||
transform = paddle.vision.transforms.Compose(
|
||||
[
|
||||
paddle.vision.transforms.Resize((32, 32)),
|
||||
paddle.vision.transforms.ToTensor(),
|
||||
paddle.vision.transforms.Normalize(
|
||||
mean=(0.1307,), std=(0.3081,)
|
||||
),
|
||||
]
|
||||
)
|
||||
train_dataset = paddle.vision.datasets.MNIST(
|
||||
mode='train', transform=transform, download=True
|
||||
)
|
||||
test_dataset = paddle.vision.datasets.MNIST(
|
||||
mode='test', transform=transform, download=True
|
||||
)
|
||||
train_loader = paddle.io.DataLoader(
|
||||
train_dataset,
|
||||
batch_size=BATCH_SIZE,
|
||||
shuffle=True,
|
||||
drop_last=True,
|
||||
num_workers=2,
|
||||
)
|
||||
test_loader = paddle.io.DataLoader(
|
||||
test_dataset,
|
||||
batch_size=BATCH_SIZE,
|
||||
shuffle=True,
|
||||
drop_last=True,
|
||||
num_workers=2,
|
||||
)
|
||||
|
||||
# train and eval
|
||||
for epoch_id in range(EPOCH_NUM):
|
||||
train_func_base(epoch_id, train_loader, model, cost, optimizer)
|
||||
test_func(epoch_id, test_loader, model, cost)
|
||||
|
||||
def _test_amp_o1(self):
|
||||
import paddle
|
||||
|
||||
class LeNet5(paddle.nn.Layer):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.fc = paddle.nn.Linear(in_features=1024, out_features=10)
|
||||
self.relu = paddle.nn.ReLU()
|
||||
self.fc1 = paddle.nn.Linear(in_features=10, out_features=10)
|
||||
|
||||
def forward(self, x):
|
||||
out = paddle.flatten(x, 1)
|
||||
out = self.fc(out)
|
||||
out = self.relu(out)
|
||||
out = self.fc1(out)
|
||||
return out
|
||||
|
||||
# set device
|
||||
paddle.set_device('custom_cpu')
|
||||
|
||||
# model
|
||||
model = LeNet5()
|
||||
|
||||
# cost and optimizer
|
||||
cost = paddle.nn.CrossEntropyLoss()
|
||||
optimizer = paddle.optimizer.Adam(
|
||||
learning_rate=0.001, parameters=model.parameters()
|
||||
)
|
||||
|
||||
# convert to static model
|
||||
scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
|
||||
model, optimizer = paddle.amp.decorate(
|
||||
models=model, optimizers=optimizer, level='O1'
|
||||
)
|
||||
|
||||
# data loader
|
||||
transform = paddle.vision.transforms.Compose(
|
||||
[
|
||||
paddle.vision.transforms.Resize((32, 32)),
|
||||
paddle.vision.transforms.ToTensor(),
|
||||
paddle.vision.transforms.Normalize(
|
||||
mean=(0.1307,), std=(0.3081,)
|
||||
),
|
||||
]
|
||||
)
|
||||
train_dataset = paddle.vision.datasets.MNIST(
|
||||
mode='train', transform=transform, download=True
|
||||
)
|
||||
test_dataset = paddle.vision.datasets.MNIST(
|
||||
mode='test', transform=transform, download=True
|
||||
)
|
||||
train_loader = paddle.io.DataLoader(
|
||||
train_dataset,
|
||||
batch_size=BATCH_SIZE,
|
||||
shuffle=True,
|
||||
drop_last=True,
|
||||
num_workers=2,
|
||||
)
|
||||
test_loader = paddle.io.DataLoader(
|
||||
test_dataset,
|
||||
batch_size=BATCH_SIZE,
|
||||
shuffle=True,
|
||||
drop_last=True,
|
||||
num_workers=2,
|
||||
)
|
||||
|
||||
# train and eval
|
||||
for epoch_id in range(EPOCH_NUM):
|
||||
train_func_ampo1(
|
||||
epoch_id, train_loader, model, cost, optimizer, scaler
|
||||
)
|
||||
test_func(epoch_id, test_loader, model, cost)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if os.name == 'nt' or sys.platform.startswith('darwin'):
|
||||
# only support Linux now
|
||||
sys.exit()
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user