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