test_lightning_module.py

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pickle
from argparse import ArgumentParser
from typing import Optional
from unittest.mock import MagicMock, patch

import pytest
import torch
from torch import nn
from torch.optim import Adam, SGD
from torch.utils.data import DataLoader, random_split

from pytorch_lightning import LightningDataModule, seed_everything, Trainer
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from tests.base import BoringModel


def test_automatic_optimization(tmpdir):
    class TestModel(BoringModel):
        def optimizer_step(self, *_, **__):
            pass

    model = TestModel()

    try:
        trainer = Trainer(
            default_root_dir=tmpdir,
            limit_train_batches=2,
            accumulate_grad_batches=2,
        )

        trainer.fit(model)
    except MisconfigurationException as e:
        assert "It ensures optimizer_step or optimizer_zero_grad are called on every batch" in str(e)


def test_automatic_optimization_num_calls(tmpdir):

    with patch("torch.optim.SGD.step") as sgd_step, \
         patch("torch.optim.SGD.zero_grad") as sgd_zero_grad, \
         patch("torch.optim.Adam.step") as adam_step, \
         patch("torch.optim.Adam.zero_grad") as adam_zero_grad:

        class TestModel(BoringModel):

            def training_step(self, batch, batch_idx, optimizer_idx):
                output = self.layer(batch)
                loss = self.loss(batch, output)
                return {"loss": loss}

            def configure_optimizers(self):
                optimizer = SGD(self.layer.parameters(), lr=0.1)
                optimizer_2 = Adam(self.layer.parameters(), lr=0.1)
                return [optimizer, optimizer_2]

            def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx,
                               optimizer_closure, on_tpu, using_native_amp, using_lbfgs):

                assert optimizer_closure.__name__ == "train_step_and_backward_closure"

                # update generator opt every 2 steps
                if optimizer_idx == 0:
                    if batch_idx % 2 == 0:
                        assert isinstance(optimizer, SGD)
                        optimizer.step(closure=optimizer_closure)

                # update discriminator opt every 4 steps
                if optimizer_idx == 1:
                    if batch_idx % 4 == 0:
                        assert isinstance(optimizer, Adam)
                        optimizer.step(closure=optimizer_closure)

        model = TestModel()
        model.training_epoch_end = None

        trainer = Trainer(
            max_epochs=1,
            default_root_dir=tmpdir,
            limit_train_batches=8,
            accumulate_grad_batches=1,
        )

        trainer.fit(model)

    assert sgd_step.call_count == 4
    assert sgd_zero_grad.call_count == 4
    assert adam_step.call_count == 2
    assert adam_zero_grad.call_count == 2


def test_params_groups_and_state_are_accessible(tmpdir):

    with patch("torch.optim.SGD.step") as sgd_step, \
         patch("torch.optim.SGD.zero_grad") as sgd_zero_grad, \
         patch("torch.optim.Adam.step") as adam_step, \
         patch("torch.optim.Adam.zero_grad") as adam_zero_grad:

        class TestModel(BoringModel):

            def training_step(self, batch, batch_idx, optimizer_idx):
                output = self.layer(batch)
                loss = self.loss(batch, output)
                return {"loss": loss}

            def configure_optimizers(self):
                optimizer = SGD(self.layer.parameters(), lr=0.1)
                optimizer_2 = Adam(self.layer.parameters(), lr=0.1)
                return [optimizer, optimizer_2]

            def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_idx, closure,
                               on_tpu=False, using_native_amp=False, using_lbfgs=False):
                # warm up lr
                if self.trainer.global_step < 500:
                    lr_scale = min(1., float(self.trainer.global_step + 1) / 500.)
                    for pg in optimizer.param_groups:
                        pg['lr'] = lr_scale * 0.01

                optimizer.step(closure=closure)

        model = TestModel()
        model.training_epoch_end = None

        trainer = Trainer(
            max_epochs=1,
            default_root_dir=tmpdir,
            limit_train_batches=8,
            accumulate_grad_batches=1,
        )

        trainer.fit(model)


def test_toggle_untoggle(tmpdir):

    class TestModel(BoringModel):

        def training_step(self, batch, batch_idx, optimizer_idx=None):
            return super().training_step(batch, batch_idx)

        def __init__(self):
            super().__init__()
            self.layer_1 = nn.Sequential(
                nn.Linear(32, 32),
                nn.ReLU(),
                nn.Linear(32, 32),
                nn.ReLU(),
                nn.Linear(32, 32),
            )

            self.layer_2 = nn.Sequential(
                nn.ReLU(),
                nn.Linear(32, 32),
                nn.ReLU(),
                nn.Linear(32, 32),
                nn.ReLU(),
                nn.Linear(32, 2)
            )

            # set some weights to False to check untoggle works as expected.
            self.layer_1[2].weight.requires_grad = False
            self.layer_1[4].weight.requires_grad = False

            self.layer_2[1].weight.requires_grad = False
            self.layer_2[3].weight.requires_grad = False

        def configure_optimizers(self):
            optimizer = SGD(self.layer_1.parameters(), lr=0.1)
            optimizer_2 = Adam(self.layer_2.parameters(), lr=0.1)
            return [optimizer, optimizer_2]

        def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_idx, closure, on_tpu=False, using_native_amp=False, using_lbfgs=False):
            if optimizer_idx == 0:
                assert self.layer_1[0].weight.requires_grad is True
                assert self.layer_1[2].weight.requires_grad is False
                assert self.layer_1[4].weight.requires_grad is False

                assert self.layer_2[1].weight.requires_grad is False
                assert self.layer_2[3].weight.requires_grad is False
                assert self.layer_2[5].weight.requires_grad is False

            if optimizer_idx == 1:
                assert self.layer_1[0].weight.requires_grad is False
                assert self.layer_1[2].weight.requires_grad is False
                assert self.layer_1[4].weight.requires_grad is False

                assert self.layer_2[1].weight.requires_grad is False
                assert self.layer_2[3].weight.requires_grad is False
                assert self.layer_2[5].weight.requires_grad is True
            optimizer.step(closure=closure)

    model = TestModel()
    model.training_epoch_end = None

    trainer = Trainer(
        max_epochs=1,
        default_root_dir=tmpdir,
        limit_train_batches=8,
        accumulate_grad_batches=1,
    )

    trainer.fit(model)