93 lines
3.5 KiB
Python
93 lines
3.5 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 unittest.mock import Mock
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import pytest
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import torch
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from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_0
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from lightning.pytorch import Trainer
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from lightning.pytorch.plugins import DoublePrecision, HalfPrecision, Precision
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from lightning.pytorch.strategies import SingleDeviceStrategy
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from tests_pytorch.helpers.datamodules import ClassifDataModule
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from tests_pytorch.helpers.runif import RunIf
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from tests_pytorch.helpers.simple_models import ClassificationModel
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@pytest.mark.parametrize(
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"trainer_kwargs",
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[
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pytest.param({"accelerator": "gpu", "devices": 1}, marks=RunIf(min_cuda_gpus=1)),
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pytest.param({"strategy": "ddp_spawn", "accelerator": "gpu", "devices": 2}, marks=RunIf(min_cuda_gpus=2)),
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pytest.param({"accelerator": "mps", "devices": 1}, marks=RunIf(mps=True)),
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],
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)
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@RunIf(sklearn=True)
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def test_evaluate(tmpdir, trainer_kwargs):
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dm = ClassifDataModule()
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model = ClassificationModel()
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trainer = Trainer(
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default_root_dir=tmpdir, max_epochs=2, limit_train_batches=10, limit_val_batches=10, **trainer_kwargs
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)
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trainer.fit(model, datamodule=dm)
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assert "ckpt" in trainer.checkpoint_callback.best_model_path
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old_weights = model.layer_0.weight.clone().detach().cpu()
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trainer.validate(datamodule=dm)
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trainer.test(datamodule=dm)
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# make sure weights didn't change
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new_weights = model.layer_0.weight.clone().detach().cpu()
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torch.testing.assert_close(old_weights, new_weights)
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@RunIf(min_torch="1.13")
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@pytest.mark.parametrize(
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"device",
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[
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"cpu",
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pytest.param("cuda:0", marks=RunIf(min_cuda_gpus=1)),
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pytest.param("mps:0", marks=RunIf(mps=True)),
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],
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)
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@pytest.mark.parametrize(
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("precision", "dtype"),
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[
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(Precision(), torch.float32),
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pytest.param(DoublePrecision(), torch.float64, marks=RunIf(mps=False)),
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(HalfPrecision("16-true"), torch.float16),
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pytest.param(HalfPrecision("bf16-true"), torch.bfloat16, marks=RunIf(bf16_cuda=True)),
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],
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)
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@pytest.mark.parametrize("empty_init", [None, True, False])
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def test_module_init_context(device, precision, dtype, empty_init, monkeypatch):
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"""Test that the module under the init-module-context gets moved to the right device and dtype."""
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init_mock = Mock()
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monkeypatch.setattr(torch.Tensor, "uniform_", init_mock)
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device = torch.device(device)
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strategy = SingleDeviceStrategy(device=device, precision_plugin=precision) # surrogate class to test base class
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with strategy.tensor_init_context(empty_init=empty_init):
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module = torch.nn.Linear(2, 2)
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expected_device = device if _TORCH_GREATER_EQUAL_2_0 else torch.device("cpu")
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assert module.weight.device == module.bias.device == expected_device
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assert module.weight.dtype == module.bias.dtype == dtype
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if not empty_init:
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init_mock.assert_called()
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else:
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init_mock.assert_not_called()
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