85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
# Copyright The PyTorch Lightning 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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import torch
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from torch.nn import functional as F
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import pytorch_lightning as pl
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from pl_examples.basic_examples.mnist_datamodule import MNISTDataModule
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class LitClassifier(pl.LightningModule):
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def __init__(self, hidden_dim: int = 128, learning_rate: float = 0.0001):
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super().__init__()
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self.save_hyperparameters()
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self.l1 = torch.nn.Linear(28 * 28, self.hparams.hidden_dim)
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self.l2 = torch.nn.Linear(self.hparams.hidden_dim, 10)
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def forward(self, x):
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x = x.view(x.size(0), -1)
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x = torch.relu(self.l1(x))
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x = torch.relu(self.l2(x))
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return x
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def training_step(self, batch, batch_idx):
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x, y = batch
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y_hat = self(x)
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loss = F.cross_entropy(y_hat, y)
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return loss
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def validation_step(self, batch, batch_idx):
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x, y = batch
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probs = self(x)
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# we currently return the accuracy as the validation_step/test_step is run on the IPU devices.
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# Outputs from the step functions are sent to the host device, where we calculate the metrics in
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# validation_epoch_end and test_epoch_end for the test_step.
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acc = self.accuracy(probs, y)
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return acc
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def test_step(self, batch, batch_idx):
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x, y = batch
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logits = self(x)
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acc = self.accuracy(logits, y)
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return acc
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def accuracy(self, logits, y):
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# currently IPU poptorch doesn't implicit convert bools to tensor
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# hence we use an explicit calculation for accuracy here. Once fixed in poptorch
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# we can use the accuracy metric.
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acc = torch.sum(torch.eq(torch.argmax(logits, -1), y).to(torch.float32)) / len(y)
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return acc
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def validation_epoch_end(self, outputs) -> None:
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# since the training step/validation step and test step are run on the IPU device
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# we must log the average loss outside the step functions.
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self.log("val_acc", torch.stack(outputs).mean(), prog_bar=True)
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def test_epoch_end(self, outputs) -> None:
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self.log("test_acc", torch.stack(outputs).mean())
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def configure_optimizers(self):
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return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
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if __name__ == "__main__":
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dm = MNISTDataModule(batch_size=32)
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model = LitClassifier()
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trainer = pl.Trainer(max_epochs=2, ipus=8)
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trainer.fit(model, datamodule=dm)
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trainer.test(model, datamodule=dm)
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