import torch from torch import nn from torch.utils.data import Dataset, DataLoader from pytorch_lightning import TrainResult, EvalResult from pytorch_lightning.core.lightning import LightningModule class DeterministicModel(LightningModule): def __init__(self, weights=None): super().__init__() self.training_step_called = False self.training_step_end_called = False self.training_epoch_end_called = False self.validation_step_called = False self.validation_step_end_called = False self.validation_epoch_end_called = False self.assert_backward = True self.l1 = nn.Linear(2, 3, bias=False) if weights is None: weights = torch.tensor([ [4, 3, 5], [10, 11, 13] ]).float() p = torch.nn.Parameter(weights, requires_grad=True) self.l1.weight = p def forward(self, x): return self.l1(x) def step(self, batch, batch_idx): x = batch bs = x.size(0) y_hat = self.l1(x) test_hat = y_hat.cpu().detach() assert torch.all(test_hat[:, 0] == 15.0) assert torch.all(test_hat[:, 1] == 42.0) out = y_hat.sum() assert out == (42.0 * bs) + (15.0 * bs) return out def assert_graph_count(self, result, count=1): counts = self.count_num_graphs(result) assert counts == count def count_num_graphs(self, result, num_graphs=0): for k, v in result.items(): if isinstance(v, torch.Tensor) and v.grad_fn is not None: num_graphs += 1 if isinstance(v, dict): num_graphs += self.count_num_graphs(v) return num_graphs # --------------------------- # scalar return # --------------------------- def training_step_scalar_return(self, batch, batch_idx): acc = self.step(batch, batch_idx) self.training_step_called = True return acc def training_step_end_scalar(self, output): self.training_step_end_called = True # make sure loss has the grad assert isinstance(output, torch.Tensor) assert output.grad_fn is not None # make sure nothing else has grads assert self.count_num_graphs({'loss': output}) == 1 assert output == 171 return output def training_epoch_end_scalar(self, outputs): """ There should be an array of scalars without graphs that are all 171 (4 of them) """ self.training_epoch_end_called = True if self.use_dp or self.use_ddp2: pass else: # only saw 4 batches assert len(outputs) == 4 for batch_out in outputs: assert batch_out == 171 assert batch_out.grad_fn is None assert isinstance(batch_out, torch.Tensor) prototype_loss = outputs[0] return prototype_loss def training_step_no_default_callbacks_for_train_loop(self, batch, batch_idx): """ Early stop and checkpoint only on these values """ acc = self.step(batch, batch_idx) result = TrainResult(minimize=acc) assert 'early_step_on' not in result assert 'checkpoint_on' in result return result def training_step_no_callbacks_result_obj(self, batch, batch_idx): """ Early stop and checkpoint only on these values """ acc = self.step(batch, batch_idx) result = TrainResult(minimize=acc, checkpoint_on=False) assert 'early_step_on' not in result assert 'checkpoint_on' not in result return result def training_step_result_log_epoch_and_step_for_callbacks(self, batch, batch_idx): """ Early stop and checkpoint only on these values """ acc = self.step(batch, batch_idx) self.assert_backward = False losses = [20, 19, 18, 10, 15, 14, 9, 11, 11, 20] idx = self.current_epoch loss = acc + losses[idx] result = TrainResult(minimize=loss, early_stop_on=loss, checkpoint_on=loss) return result def training_step_result_log_step_only(self, batch, batch_idx): acc = self.step(batch, batch_idx) result = TrainResult(minimize=acc) # step only metrics result.log(f'step_log_and_pbar_acc1_b{batch_idx}', torch.tensor(11).type_as(acc), prog_bar=True) result.log(f'step_log_acc2_b{batch_idx}', torch.tensor(12).type_as(acc)) result.log(f'step_pbar_acc3_b{batch_idx}', torch.tensor(13).type_as(acc), logger=False, prog_bar=True) self.training_step_called = True return result def training_step_result_log_epoch_only(self, batch, batch_idx): acc = self.step(batch, batch_idx) result = TrainResult(minimize=acc) result.log(f'epoch_log_and_pbar_acc1_e{self.current_epoch}', torch.tensor(14).type_as(acc), on_epoch=True, prog_bar=True, on_step=False) result.log(f'epoch_log_acc2_e{self.current_epoch}', torch.tensor(15).type_as(acc), on_epoch=True, on_step=False) result.log(f'epoch_pbar_acc3_e{self.current_epoch}', torch.tensor(16).type_as(acc), on_epoch=True, logger=False, prog_bar=True, on_step=False) self.training_step_called = True return result def training_step_result_log_epoch_and_step(self, batch, batch_idx): acc = self.step(batch, batch_idx) result = TrainResult(minimize=acc) val_1 = (5 + batch_idx) * (self.current_epoch + 1) val_2 = (6 + batch_idx) * (self.current_epoch + 1) val_3 = (7 + batch_idx) * (self.current_epoch + 1) result.log('step_epoch_log_and_pbar_acc1', torch.tensor(val_1).type_as(acc), on_epoch=True, prog_bar=True) result.log('step_epoch_log_acc2', torch.tensor(val_2).type_as(acc), on_epoch=True) result.log('step_epoch_pbar_acc3', torch.tensor(val_3).type_as(acc), on_epoch=True, logger=False, prog_bar=True) self.training_step_called = True return result def training_epoch_end_return_for_log_epoch_and_step(self, result): """ There should be an array of scalars without graphs that are all 171 (4 of them) """ self.training_epoch_end_called = True if self.use_dp or self.use_ddp2: pass else: # only saw 4 batches assert isinstance(result, TrainResult) result.step_step_epoch_log_and_pbar_acc1 = result.step_step_epoch_log_and_pbar_acc1.prod() result.epoch_step_epoch_log_and_pbar_acc1 = result.epoch_step_epoch_log_and_pbar_acc1.prod() result.step_step_epoch_log_acc2 = result.step_step_epoch_log_acc2.prod() result.epoch_step_epoch_log_acc2 = result.epoch_step_epoch_log_acc2.prod() result.step_step_epoch_pbar_acc3 = result.step_step_epoch_pbar_acc3.prod() result.epoch_step_epoch_pbar_acc3 = result.epoch_step_epoch_pbar_acc3.prod() result.log('epoch_end_log_acc', torch.tensor(1212).type_as(result.epoch_step_epoch_log_acc2), logger=True, on_epoch=True) result.log('epoch_end_pbar_acc', torch.tensor(1213).type_as(result.epoch_step_epoch_log_acc2), logger=False, prog_bar=True, on_epoch=True) result.log('epoch_end_log_pbar_acc', torch.tensor(1214).type_as(result.epoch_step_epoch_log_acc2), logger=True, prog_bar=True, on_epoch=True) return result # -------------------------- # EvalResults # -------------------------- def validation_step_result_callbacks(self, batch, batch_idx): acc = self.step(batch, batch_idx) self.assert_backward = False losses = [20, 19, 20, 21, 22, 23] idx = self.current_epoch loss = acc + losses[idx] result = EvalResult(early_stop_on=loss, checkpoint_on=loss) self.validation_step_called = True return result def validation_step_result_no_callbacks(self, batch, batch_idx): acc = self.step(batch, batch_idx) self.assert_backward = False losses = [20, 19, 20, 21, 22, 23, 50, 50, 50, 50, 50, 50] idx = self.current_epoch loss = acc + losses[idx] result = EvalResult(checkpoint_on=loss) self.validation_step_called = True return result def validation_step_result_only_epoch_metrics(self, batch, batch_idx): """ Only track epoch level metrics """ acc = self.step(batch, batch_idx) result = EvalResult(checkpoint_on=acc, early_stop_on=acc) # step only metrics result.log('no_val_no_pbar', torch.tensor(11 + batch_idx).type_as(acc), prog_bar=False, logger=False) result.log('val_step_log_acc', torch.tensor(11 + batch_idx).type_as(acc), prog_bar=False, logger=True) result.log('val_step_log_pbar_acc', torch.tensor(12 + batch_idx).type_as(acc), prog_bar=True, logger=True) result.log('val_step_pbar_acc', torch.tensor(13 + batch_idx).type_as(acc), prog_bar=True, logger=False) self.validation_step_called = True return result def validation_step_result_only_step_metrics(self, batch, batch_idx): """ Only track epoch level metrics """ acc = self.step(batch, batch_idx) result = EvalResult(checkpoint_on=acc, early_stop_on=acc) # step only metrics result.log('no_val_no_pbar', torch.tensor(11 + batch_idx).type_as(acc), prog_bar=False, logger=False, on_epoch=False, on_step=True) result.log('val_step_log_acc', torch.tensor(11 + batch_idx).type_as(acc), prog_bar=False, logger=True, on_epoch=False, on_step=True) result.log('val_step_log_pbar_acc', torch.tensor(12 + batch_idx).type_as(acc), prog_bar=True, logger=True, on_epoch=False, on_step=True) result.log('val_step_pbar_acc', torch.tensor(13 + batch_idx).type_as(acc), prog_bar=True, logger=False, on_epoch=False, on_step=True) result.log('val_step_batch_idx', torch.tensor(batch_idx).type_as(acc), prog_bar=True, logger=True, on_epoch=False, on_step=True) self.validation_step_called = True return result def validation_step_result_epoch_step_metrics(self, batch, batch_idx): """ Only track epoch level metrics """ acc = self.step(batch, batch_idx) result = EvalResult(checkpoint_on=acc, early_stop_on=acc) # step only metrics result.log('no_val_no_pbar', torch.tensor(11 + batch_idx).type_as(acc), prog_bar=False, logger=False, on_epoch=True, on_step=True) result.log('val_step_log_acc', torch.tensor(11 + batch_idx).type_as(acc), prog_bar=False, logger=True, on_epoch=True, on_step=True) result.log('val_step_log_pbar_acc', torch.tensor(12 + batch_idx).type_as(acc), prog_bar=True, logger=True, on_epoch=True, on_step=True) result.log('val_step_pbar_acc', torch.tensor(13 + batch_idx).type_as(acc), prog_bar=True, logger=False, on_epoch=True, on_step=True) result.log('val_step_batch_idx', torch.tensor(batch_idx).type_as(acc), prog_bar=True, logger=True, on_epoch=True, on_step=True) self.validation_step_called = True return result def validation_step_for_epoch_end_result(self, batch, batch_idx): """ EvalResult flows to epoch end (without step_end) """ acc = self.step(batch, batch_idx) result = EvalResult(checkpoint_on=acc, early_stop_on=acc) # step only metrics result.log('val_step_metric', torch.tensor(batch_idx).type_as(acc), prog_bar=True, logger=True, on_epoch=True, on_step=False) result.log('batch_idx', torch.tensor(batch_idx).type_as(acc), prog_bar=True, logger=True, on_epoch=True, on_step=False) self.validation_step_called = True return result def validation_epoch_end_result(self, result): self.validation_epoch_end_called = True if self.trainer.running_sanity_check: assert len(result.batch_idx) == 2 else: assert len(result.batch_idx) == self.trainer.limit_val_batches expected_val = result.val_step_metric.sum() / len(result.batch_idx) result.val_step_metric = result.val_step_metric.mean() result.batch_idx = result.batch_idx.mean() assert result.val_step_metric == expected_val result.log('val_epoch_end_metric', torch.tensor(189).type_as(result.val_step_metric), prog_bar=True) return result # -------------------------- # dictionary returns # -------------------------- def training_step_dict_return(self, batch, batch_idx): acc = self.step(batch, batch_idx) logs = {'log_acc1': torch.tensor(12).type_as(acc), 'log_acc2': torch.tensor(7).type_as(acc)} pbar = {'pbar_acc1': torch.tensor(17).type_as(acc), 'pbar_acc2': torch.tensor(19).type_as(acc)} self.training_step_called = True return {'loss': acc, 'log': logs, 'progress_bar': pbar, 'train_step_test': torch.tensor(549).type_as(acc)} def training_step_for_step_end_dict(self, batch, batch_idx): """sends outputs to training_batch_end""" acc = self.step(batch, batch_idx) logs = {'log_acc1': torch.tensor(12).type_as(acc), 'log_acc2': torch.tensor(7).type_as(acc)} pbar = {'pbar_acc1': torch.tensor(17).type_as(acc), 'pbar_acc2': torch.tensor(19).type_as(acc)} self.training_step_called = True result = {'loss': acc} result.update(logs) result.update(pbar) return result def training_step_end_dict(self, output): self.training_step_end_called = True # make sure loss has the grad assert 'loss' in output assert output['loss'].grad_fn is not None # make sure nothing else has grads assert self.count_num_graphs(output) == 1 # make sure the other keys are there assert 'log_acc1' in output assert 'log_acc2' in output assert 'pbar_acc1' in output assert 'pbar_acc2' in output logs = {'log_acc1': output['log_acc1'] + 2, 'log_acc2': output['log_acc2'] + 2} pbar = {'pbar_acc1': output['pbar_acc1'] + 2, 'pbar_acc2': output['pbar_acc2'] + 2} acc = output['loss'] return {'loss': acc, 'log': logs, 'progress_bar': pbar, 'train_step_end': acc} def training_epoch_end_dict(self, outputs): self.training_epoch_end_called = True if self.use_dp or self.use_ddp2: pass else: # only saw 4 batches assert len(outputs) == 4 for batch_out in outputs: assert len(batch_out.keys()) == 4 assert self.count_num_graphs(batch_out) == 0 last_key = 'train_step_end' if self.training_step_end_called else 'train_step_test' keys = ['loss', 'log', 'progress_bar', last_key] for key in keys: assert key in batch_out prototype_loss = outputs[0]['loss'] logs = {'epoch_end_log_1': torch.tensor(178).type_as(prototype_loss)} pbar = {'epoch_end_pbar_1': torch.tensor(234).type_as(prototype_loss)} return {'log': logs, 'progress_bar': pbar} def validation_step_no_return(self, batch, batch_idx): self.validation_step_called = True acc = self.step(batch, batch_idx) def validation_step_scalar_return(self, batch, batch_idx): self.validation_step_called = True acc = self.step(batch, batch_idx) return acc def validation_step_arbitary_dict_return(self, batch, batch_idx): self.validation_step_called = True acc = self.step(batch, batch_idx) return {'some': acc, 'value': 'a'} def validation_step_dict_return(self, batch, batch_idx): self.validation_step_called = True acc = self.step(batch, batch_idx) logs = {'log_acc1': torch.tensor(12 + batch_idx).type_as(acc), 'log_acc2': torch.tensor(7).type_as(acc)} pbar = {'pbar_acc1': torch.tensor(17).type_as(acc), 'pbar_acc2': torch.tensor(19).type_as(acc)} return {'val_loss': acc, 'log': logs, 'progress_bar': pbar} def validation_step_end_no_return(self, val_step_output): assert len(val_step_output) == 3 assert val_step_output['val_loss'] == 171 assert val_step_output['log']['log_acc1'] >= 12 assert val_step_output['progress_bar']['pbar_acc1'] == 17 self.validation_step_end_called = True def validation_step_end(self, val_step_output): assert len(val_step_output) == 3 assert val_step_output['val_loss'] == 171 assert val_step_output['log']['log_acc1'] >= 12 assert val_step_output['progress_bar']['pbar_acc1'] == 17 self.validation_step_end_called = True val_step_output['val_step_end'] = torch.tensor(1802) return val_step_output def validation_epoch_end(self, outputs): assert len(outputs) == self.trainer.num_val_batches[0] for i, out in enumerate(outputs): assert out['log']['log_acc1'] >= 12 + i self.validation_epoch_end_called = True result = outputs[-1] result['val_epoch_end'] = torch.tensor(1233) return result # ----------------------------- # DATA # ----------------------------- def train_dataloader(self): return DataLoader(DummyDataset(), batch_size=3, shuffle=False) def val_dataloader(self): return DataLoader(DummyDataset(), batch_size=3, shuffle=False) def configure_optimizers(self): return torch.optim.Adam(self.parameters(), lr=0) def configure_optimizers__lr_on_plateau_epoch(self): optimizer = torch.optim.Adam(self.parameters(), lr=0) lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer) scheduler = {'scheduler': lr_scheduler, 'interval': 'epoch', 'monitor': 'epoch_end_log_1'} return [optimizer], [scheduler] def configure_optimizers__lr_on_plateau_step(self): optimizer = torch.optim.Adam(self.parameters(), lr=0) lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer) scheduler = {'scheduler': lr_scheduler, 'interval': 'step', 'monitor': 'pbar_acc1'} return [optimizer], [scheduler] def backward(self, trainer, loss, optimizer, optimizer_idx): if self.assert_backward: if self.trainer.precision == 16: assert loss > 171 * 1000 else: assert loss == 171.0 loss.backward() class DummyDataset(Dataset): def __len__(self): return 12 def __getitem__(self, idx): return torch.tensor([0.5, 1.0, 2.0])