206 lines
7.0 KiB
Python
206 lines
7.0 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 os
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import fsspec
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import pytest
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import torch
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from fsspec.implementations.local import LocalFileSystem
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from lightning_lite.utilities.cloud_io import get_filesystem
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from pytorch_lightning.core.module import LightningModule
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from pytorch_lightning.demos.boring_classes import BoringModel
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from tests_pytorch.helpers.advanced_models import BasicGAN, ParityModuleRNN
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from tests_pytorch.helpers.runif import RunIf
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@pytest.mark.parametrize("modelclass", [BoringModel, ParityModuleRNN, BasicGAN])
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def test_torchscript_input_output(modelclass):
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"""Test that scripted LightningModule forward works."""
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model = modelclass()
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if isinstance(model, BoringModel):
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model.example_input_array = torch.randn(5, 32)
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script = model.to_torchscript()
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assert isinstance(script, torch.jit.ScriptModule)
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model.eval()
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with torch.no_grad():
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model_output = model(model.example_input_array)
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script_output = script(model.example_input_array)
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assert torch.allclose(script_output, model_output)
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@pytest.mark.parametrize("modelclass", [BoringModel, ParityModuleRNN, BasicGAN])
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def test_torchscript_example_input_output_trace(modelclass):
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"""Test that traced LightningModule forward works with example_input_array."""
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model = modelclass()
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if isinstance(model, BoringModel):
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model.example_input_array = torch.randn(5, 32)
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script = model.to_torchscript(method="trace")
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assert isinstance(script, torch.jit.ScriptModule)
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model.eval()
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with torch.no_grad():
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model_output = model(model.example_input_array)
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script_output = script(model.example_input_array)
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assert torch.allclose(script_output, model_output)
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def test_torchscript_input_output_trace():
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"""Test that traced LightningModule forward works with example_inputs."""
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model = BoringModel()
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example_inputs = torch.randn(1, 32)
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script = model.to_torchscript(example_inputs=example_inputs, method="trace")
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assert isinstance(script, torch.jit.ScriptModule)
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model.eval()
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with torch.no_grad():
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model_output = model(example_inputs)
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script_output = script(example_inputs)
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assert torch.allclose(script_output, model_output)
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@pytest.mark.parametrize(
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"device_str",
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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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def test_torchscript_device(device_str):
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"""Test that scripted module is on the correct device."""
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device = torch.device(device_str)
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model = BoringModel().to(device)
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model.example_input_array = torch.randn(5, 32)
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script = model.to_torchscript()
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assert next(script.parameters()).device == device
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script_output = script(model.example_input_array.to(device))
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assert script_output.device == device
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def test_torchscript_retain_training_state():
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"""Test that torchscript export does not alter the training mode of original model."""
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model = BoringModel()
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model.train(True)
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script = model.to_torchscript()
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assert model.training
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assert not script.training
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model.train(False)
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_ = model.to_torchscript()
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assert not model.training
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assert not script.training
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@pytest.mark.parametrize("modelclass", [BoringModel, ParityModuleRNN, BasicGAN])
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def test_torchscript_properties(modelclass):
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"""Test that scripted LightningModule has unnecessary methods removed."""
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model = modelclass()
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script = model.to_torchscript()
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assert not hasattr(model, "batch_size") or hasattr(script, "batch_size")
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assert not hasattr(model, "learning_rate") or hasattr(script, "learning_rate")
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assert not callable(getattr(script, "training_step", None))
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@pytest.mark.parametrize("modelclass", [BoringModel, ParityModuleRNN, BasicGAN])
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def test_torchscript_save_load(tmpdir, modelclass):
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"""Test that scripted LightningModule is correctly saved and can be loaded."""
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model = modelclass()
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output_file = str(tmpdir / "model.pt")
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script = model.to_torchscript(file_path=output_file)
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loaded_script = torch.jit.load(output_file)
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assert torch.allclose(next(script.parameters()), next(loaded_script.parameters()))
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@pytest.mark.parametrize("modelclass", [BoringModel, ParityModuleRNN, BasicGAN])
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def test_torchscript_save_load_custom_filesystem(tmpdir, modelclass):
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"""Test that scripted LightningModule is correctly saved and can be loaded with custom filesystems."""
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_DUMMY_PRFEIX = "dummy"
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_PREFIX_SEPARATOR = "://"
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class DummyFileSystem(LocalFileSystem):
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...
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fsspec.register_implementation(_DUMMY_PRFEIX, DummyFileSystem, clobber=True)
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model = modelclass()
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output_file = os.path.join(_DUMMY_PRFEIX, _PREFIX_SEPARATOR, tmpdir, "model.pt")
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script = model.to_torchscript(file_path=output_file)
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fs = get_filesystem(output_file)
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with fs.open(output_file, "rb") as f:
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loaded_script = torch.jit.load(f)
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assert torch.allclose(next(script.parameters()), next(loaded_script.parameters()))
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def test_torchcript_invalid_method():
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"""Test that an error is thrown with invalid torchscript method."""
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model = BoringModel()
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model.train(True)
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with pytest.raises(ValueError, match="only supports 'script' or 'trace'"):
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model.to_torchscript(method="temp")
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def test_torchscript_with_no_input():
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"""Test that an error is thrown when there is no input tensor."""
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model = BoringModel()
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model.example_input_array = None
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with pytest.raises(ValueError, match="requires either `example_inputs` or `model.example_input_array`"):
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model.to_torchscript(method="trace")
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def test_torchscript_script_recursively():
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class GrandChild(LightningModule):
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def __init__(self):
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super().__init__()
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self.model = torch.nn.Linear(1, 1)
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def forward(self, inputs):
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return self.model(inputs)
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class Child(LightningModule):
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def __init__(self):
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super().__init__()
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self.model = torch.nn.Sequential(GrandChild(), GrandChild())
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def forward(self, inputs):
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return self.model(inputs)
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class Parent(LightningModule):
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def __init__(self):
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super().__init__()
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self.model = Child()
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def forward(self, inputs):
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return self.model(inputs)
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lm = Parent()
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assert not lm._jit_is_scripting
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script = lm.to_torchscript(method="script")
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assert not lm._jit_is_scripting
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assert isinstance(script, torch.jit.RecursiveScriptModule)
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