126 lines
3.9 KiB
Python
126 lines
3.9 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 pytest
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import torch
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from torch.utils.data import DataLoader, Dataset
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from pytorch_lightning import Trainer
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from tests.helpers.boring_model import BoringModel, RandomDataset
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class RandomFloatIntDataset(Dataset):
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def __init__(self, size, length):
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self.len = length
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self.float_data = torch.randn(length, size)
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self.int_data = torch.randint(10, (length, 1))
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def __getitem__(self, index):
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return self.float_data[index], self.int_data[index]
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def __len__(self):
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return self.len
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class DoublePrecisionBoringModel(BoringModel):
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def training_step(self, batch, batch_idx):
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float_data, int_data = batch
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assert float_data.dtype == torch.float64
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output = self(float_data)
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loss = self.loss(batch, output)
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return {"loss": loss}
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def validation_step(self, batch, batch_idx):
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assert batch.dtype == torch.float64
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output = self(batch)
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loss = self.loss(batch, output)
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return {"x": loss}
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def test_step(self, batch, batch_idx):
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assert batch.dtype == torch.float64
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output = self(batch)
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loss = self.loss(batch, output)
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return {"y": loss}
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def predict_step(self, batch, batch_idx, dataloader_idx=None):
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assert batch.dtype == torch.float64
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return self(batch)
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def on_fit_start(self):
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assert self.layer.weight.dtype == torch.float64
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def on_after_backward(self):
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assert self.layer.weight.grad.dtype == torch.float64
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def train_dataloader(self):
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dataset = RandomFloatIntDataset(32, 64)
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assert dataset.float_data.dtype == torch.float32 # Don't start with double data
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return DataLoader(dataset)
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def predict_dataloader(self):
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return DataLoader(RandomDataset(32, 64))
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class DoublePrecisionBoringModelNoForward(BoringModel):
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def training_step(self, batch, batch_idx):
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assert batch.dtype == torch.float64
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output = self.layer(batch)
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assert output.dtype == torch.float64
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loss = self.loss(batch, output)
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return {"loss": loss}
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def validation_step(self, batch, batch_idx):
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assert batch.dtype == torch.float64
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output = self.layer(batch)
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assert output.dtype == torch.float64
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loss = self.loss(batch, output)
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return {"x": loss}
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def test_step(self, batch, batch_idx):
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assert batch.dtype == torch.float64
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output = self.layer(batch)
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assert output.dtype == torch.float64
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loss = self.loss(batch, output)
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return {"y": loss}
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def predict_step(self, batch, batch_idx, dataloader_idx=None):
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assert batch.dtype == torch.float64
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output = self.layer(batch)
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assert output.dtype == torch.float64
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return output
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def predict_dataloader(self):
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return DataLoader(RandomDataset(32, 64))
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@pytest.mark.parametrize('boring_model', (DoublePrecisionBoringModel, DoublePrecisionBoringModelNoForward))
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def test_double_precision(tmpdir, boring_model):
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model = boring_model()
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original_training_step = model.training_step
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trainer = Trainer(
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max_epochs=2,
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default_root_dir=tmpdir,
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fast_dev_run=2,
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precision=64,
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log_every_n_steps=1,
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)
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trainer.fit(model)
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trainer.test(model)
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trainer.predict(model)
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assert model.training_step == original_training_step
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