Add test

Author: lantiga

tests/tests_pytorch/helpers/advanced_models.py

@@ -219,3 +219,54 @@ def configure_optimizers(self):
 
     def train_dataloader(self):
         return DataLoader(MNIST(root=_PATH_DATASETS, train=True, download=True), batch_size=128, num_workers=1)
+
+
+class TBPTTModule(LightningModule):
+    def __init__(self):
+        super().__init__()
+
+        self.batch_size = 10
+        self.in_features = 10
+        self.out_features = 5
+        self.hidden_dim = 20
+
+        self.automatic_optimization = False
+        self.truncated_bptt_steps = 10
+
+        self.rnn = nn.LSTM(self.in_features, self.hidden_dim, batch_first=True)
+        self.linear_out = nn.Linear(in_features=self.hidden_dim, out_features=self.out_features)
+
+    def forward(self, x, hs):
+        seq, hs = self.rnn(x, hs)
+        return self.linear_out(seq), hs
+
+    def training_step(self, batch, batch_idx):
+        x, y = batch
+        split_x, split_y = [
+            x.tensor_split(self.truncated_bptt_steps, dim=1),
+            y.tensor_split(self.truncated_bptt_steps, dim=1),
+        ]
+
+        hiddens = None
+        optimizer = self.optimizers()
+        losses = []
+
+        for x, y in zip(split_x, split_y):
+            y_pred, hiddens = self(x, hiddens)
+            loss = F.mse_loss(y_pred, y)
+
+            optimizer.zero_grad()
+            self.manual_backward(loss)
+            optimizer.step()
+
+            # "Truncate"
+            hiddens = [h.detach() for h in hiddens]
+            losses.append(loss.detach())
+
+        return
+
+    def configure_optimizers(self):
+        return torch.optim.Adam(self.parameters(), lr=0.001)
+
+    def train_dataloader(self):
+        return DataLoader(AverageDataset(), batch_size=self.batch_size)

tests/tests_pytorch/helpers/test_models.py

@@ -17,7 +17,7 @@
 from lightning.pytorch import Trainer
 from lightning.pytorch.demos.boring_classes import BoringModel
 
-from tests_pytorch.helpers.advanced_models import BasicGAN, ParityModuleMNIST, ParityModuleRNN
+from tests_pytorch.helpers.advanced_models import BasicGAN, ParityModuleMNIST, ParityModuleRNN, TBPTTModule
 from tests_pytorch.helpers.datamodules import ClassifDataModule, RegressDataModule
 from tests_pytorch.helpers.runif import RunIf
 from tests_pytorch.helpers.simple_models import ClassificationModel, RegressionModel
@@ -49,3 +49,10 @@ def test_models(tmp_path, data_class, model_class):
     model.to_torchscript()
     if data_class:
         model.to_onnx(os.path.join(tmp_path, "my-model.onnx"), input_sample=dm.sample)
+
+
+def test_tbptt(tmp_path):
+    model = TBPTTModule()
+
+    trainer = Trainer(default_root_dir=tmp_path, max_epochs=1)
+    trainer.fit(model)