python/test_support/pytorch_training_test_file.py - master - githubmirror/spark

python/test_support/pytorch_training_test_file.py
115 строк4 KB
Tian Gao
[SPARK-55488][PYTHON][TESTS] Add all dangling test files to modules
18 фев 2026, 04:56
18 фев 2026, 04:5620d0c2a
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You 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.
#
# type: ignore

batch_size = 100
num_epochs = 3
momentum = 0.5
log_interval = 100

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import tempfile
import shutil


class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x)


def train_one_epoch(model, data_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(data_loader):
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % log_interval == 0:
            print(
                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                    epoch,
                    batch_idx * len(data),
                    len(data_loader) * len(data),
                    100.0 * batch_idx / len(data_loader),
                    loss.item(),
                )
            )


def train(learning_rate):
    import torch.distributed as dist
    from torch.nn.parallel import DistributedDataParallel as DDP
    from torch.utils.data.distributed import DistributedSampler

    print("Running distributed training")
    dist.init_process_group("gloo")

    temp_dir = tempfile.mkdtemp()

    train_dataset = datasets.MNIST(
        temp_dir,
        train=True,
        download=True,
        transform=transforms.Compose(
            [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
        ),
    )

    train_sampler = DistributedSampler(dataset=train_dataset)
    data_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=batch_size, sampler=train_sampler
    )

    model = Net()
    ddp_model = DDP(model)

    optimizer = optim.SGD(ddp_model.parameters(), lr=learning_rate, momentum=momentum)
    for epoch in range(1, num_epochs + 1):
        train_one_epoch(ddp_model, data_loader, optimizer, epoch)

    dist.destroy_process_group()

    shutil.rmtree(temp_dir)


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument("lr", help="learning_rate", default=0.001)
    args = parser.parse_args()
    print("learning rate chosen: ", float(args.lr))
    train(float(args.lr))