210 lines
6.8 KiB
Python
210 lines
6.8 KiB
Python
import os
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import torch
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import pytest
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from tests.base.boring_model import BoringModel, RandomDataset
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from pytorch_lightning import Trainer
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from pytorch_lightning.utilities import APEX_AVAILABLE
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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def test_multiple_optimizers_manual(tmpdir):
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os.environ['PL_DEV_DEBUG'] = '1'
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"""
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Tests that only training_step can be used
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"""
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class TestModel(BoringModel):
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def training_step(self, batch, batch_idx, optimizer_idx):
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# manual
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(opt_a, opt_b) = self.optimizers()
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loss_1 = self.step(batch[0])
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# make sure there are no grads
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if batch_idx > 0:
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assert torch.all(self.layer.weight.grad == 0)
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self.manual_backward(loss_1, opt_a)
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opt_a.step()
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opt_a.zero_grad()
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assert torch.all(self.layer.weight.grad == 0)
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# fake discriminator
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loss_2 = self.step(batch[0])
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# ensure we forward the correct params to the optimizer
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# without retain_graph we can't do multiple backward passes
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self.manual_backward(loss_2, opt_b, retain_graph=True)
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self.manual_backward(loss_2, opt_a, retain_graph=True)
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assert self.layer.weight.grad is not None
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opt_b.step()
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opt_b.zero_grad()
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assert torch.all(self.layer.weight.grad == 0)
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def training_epoch_end(self, outputs) -> None:
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# outputs should be an array with an entry per optimizer
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assert len(outputs) == 2
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def configure_optimizers(self):
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optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
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optimizer_2 = torch.optim.SGD(self.layer.parameters(), lr=0.1)
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return optimizer, optimizer_2
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model = TestModel()
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model.val_dataloader = None
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limit_train_batches = 2
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trainer = Trainer(
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automatic_optimization=False,
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default_root_dir=tmpdir,
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limit_train_batches=limit_train_batches,
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limit_val_batches=2,
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max_epochs=1,
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log_every_n_steps=1,
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weights_summary=None,
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)
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trainer.fit(model)
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num_manual_backward_calls = 3
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assert trainer.dev_debugger.count_events('backward_call') == limit_train_batches * num_manual_backward_calls
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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def test_multiple_optimizers_manual_native_amp(tmpdir):
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os.environ['PL_DEV_DEBUG'] = '1'
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"""
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Tests that only training_step can be used
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"""
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class TestModel(BoringModel):
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def training_step(self, batch, batch_idx, optimizer_idx):
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# manual
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(opt_a, opt_b) = self.optimizers()
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loss_1 = self.step(batch[0])
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# make sure there are no grads
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if batch_idx > 0:
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assert torch.all(self.layer.weight.grad == 0)
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self.manual_backward(loss_1, opt_a)
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opt_a.step()
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opt_a.zero_grad()
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assert torch.all(self.layer.weight.grad == 0)
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# fake discriminator
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loss_2 = self.step(batch[0])
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# ensure we forward the correct params to the optimizer
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# without retain_graph we can't do multiple backward passes
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self.manual_backward(loss_2, opt_b, retain_graph=True)
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self.manual_backward(loss_2, opt_a, retain_graph=True)
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assert self.layer.weight.grad is not None
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opt_b.step()
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opt_b.zero_grad()
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assert torch.all(self.layer.weight.grad == 0)
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def training_epoch_end(self, outputs) -> None:
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# outputs should be an array with an entry per optimizer
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assert len(outputs) == 2
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def configure_optimizers(self):
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optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
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optimizer_2 = torch.optim.SGD(self.layer.parameters(), lr=0.1)
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return optimizer, optimizer_2
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model = TestModel()
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model.val_dataloader = None
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limit_train_batches = 2
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trainer = Trainer(
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automatic_optimization=False,
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default_root_dir=tmpdir,
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limit_train_batches=limit_train_batches,
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limit_val_batches=2,
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max_epochs=1,
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log_every_n_steps=1,
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weights_summary=None,
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precision=16,
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gpus=1
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)
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trainer.fit(model)
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num_manual_backward_calls = 3
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assert trainer.dev_debugger.count_events('backward_call') == limit_train_batches * num_manual_backward_calls
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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@pytest.mark.skipif(not APEX_AVAILABLE, reason="test requires apex")
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def test_multiple_optimizers_manual_apex(tmpdir):
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os.environ['PL_DEV_DEBUG'] = '1'
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"""
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Tests that only training_step can be used
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"""
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class TestModel(BoringModel):
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def training_step(self, batch, batch_idx, optimizer_idx):
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# manual
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(opt_a, opt_b) = self.optimizers()
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x = batch[0]
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loss_1 = self(x)
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loss_1 = self.loss(loss_1, loss_1)
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# make sure there are no grads
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if batch_idx > 0:
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assert torch.all(self.layer.weight.grad == 0)
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self.manual_backward(loss_1, opt_a)
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opt_a.step()
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opt_a.zero_grad()
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assert torch.all(self.layer.weight.grad == 0)
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# fake discriminator
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loss_2 = self(x)
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loss_2 = self.loss(loss_2, loss_2)
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# ensure we forward the correct params to the optimizer
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# without retain_graph we can't do multiple backward passes
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self.manual_backward(loss_2, opt_b, retain_graph=True)
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self.manual_backward(loss_2, opt_a, retain_graph=True)
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assert self.layer.weight.grad is not None
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opt_b.step()
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opt_b.zero_grad()
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assert torch.all(self.layer.weight.grad == 0)
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def training_epoch_end(self, outputs) -> None:
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# outputs should be an array with an entry per optimizer
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assert len(outputs) == 2
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def configure_optimizers(self):
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optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
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optimizer_2 = torch.optim.SGD(self.layer.parameters(), lr=0.1)
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return optimizer, optimizer_2
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model = TestModel()
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model.val_dataloader = None
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limit_train_batches = 2
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trainer = Trainer(
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automatic_optimization=False,
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default_root_dir=tmpdir,
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limit_train_batches=limit_train_batches,
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limit_val_batches=2,
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max_epochs=1,
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log_every_n_steps=1,
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weights_summary=None,
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precision=16,
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amp_level='O2',
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amp_backend='apex',
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gpus=1
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)
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trainer.fit(model)
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num_manual_backward_calls = 3
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assert trainer.dev_debugger.count_events('backward_call') == limit_train_batches * num_manual_backward_calls
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