83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
# Copyright The Lightning AI team.
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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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from abc import ABC, abstractmethod
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from typing import Callable
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.optim import Optimizer
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from torch.utils.data import DataLoader, TensorDataset
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class ParityModel(ABC, nn.Module):
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"""Defines the interface for a model in a Fabric-PyTorch parity test."""
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# Benchmarking parameters that should be model-specific
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batch_size = 1
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num_steps = 1
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@abstractmethod
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def get_optimizer(self, *args, **kwargs) -> Optimizer:
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pass
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@abstractmethod
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def get_dataloader(self, *args, **kwargs) -> DataLoader:
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pass
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@abstractmethod
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def get_loss_function(self) -> Callable:
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pass
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class ConvNet(ParityModel):
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batch_size = 4
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num_steps = 1000
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def __init__(self):
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super().__init__()
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self.conv1 = nn.Conv2d(3, 6, 5)
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self.pool = nn.MaxPool2d(2, 2)
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self.conv2 = nn.Conv2d(6, 16, 5)
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self.fc1 = nn.Linear(16 * 5 * 5, 120)
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self.fc2 = nn.Linear(120, 84)
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self.fc3 = nn.Linear(84, 10)
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def forward(self, x):
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x = self.pool(F.relu(self.conv1(x)))
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x = self.pool(F.relu(self.conv2(x)))
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x = torch.flatten(x, 1) # flatten all dimensions except batch
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc2(x))
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x = self.fc3(x)
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return x
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def get_optimizer(self):
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return torch.optim.SGD(self.parameters(), lr=0.0001)
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def get_dataloader(self):
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# multiply * 8 just in case world size is larger than 1
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dataset_size = self.num_steps * self.batch_size * 8
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inputs = torch.rand(dataset_size, 3, 32, 32)
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labels = torch.randint(0, 10, (dataset_size,))
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dataset = TensorDataset(inputs, labels)
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return DataLoader(
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dataset,
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batch_size=self.batch_size,
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num_workers=2,
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)
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def get_loss_function(self):
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return F.cross_entropy
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