Make example self-contained

Author: lantiga

docs/source-pytorch/common/tbptt.rst

@@ -12,52 +12,91 @@ hidden states should be kept in-between each time-dimension split.
 .. code-block:: python
 
     import torch
+    import torch.nn as nn
+    import torch.nn.functional as F
     import torch.optim as optim
+    from torch.utils.data import Dataset, DataLoader
     import pytorch_lightning as pl
     from pytorch_lightning import LightningModule
 
+
+    class AverageDataset(Dataset):
+        def __init__(self, dataset_len=300, sequence_len=100):
+            self.dataset_len = dataset_len
+            self.sequence_len = sequence_len
+            self.input_seq = torch.randn(dataset_len, sequence_len, 10)
+            top, bottom = self.input_seq.chunk(2, -1)
+            self.output_seq = top + bottom.roll(shifts=1, dims=-1)
+
+        def __len__(self):
+            return self.dataset_len
+
+        def __getitem__(self, item):
+            return self.input_seq[item], self.output_seq[item]
+
+
     class LitModel(LightningModule):
 
         def __init__(self):
             super().__init__()
 
+            self.batch_size = 10
+            self.in_features = 10
+            self.out_features = 5
+            self.hidden_dim = 20
+
             # 1. Switch to manual optimization
             self.automatic_optimization = False
-
             self.truncated_bptt_steps = 10
-            self.my_rnn = ParityModuleRNN() # Define RNN model using ParityModuleRNN
+
+            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
 
         # 2. Remove the `hiddens` argument
         def training_step(self, batch, batch_idx):
-
             # 3. Split the batch in chunks along the time dimension
-            split_batches = split_batch(batch, self.truncated_bptt_steps)
+            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)
+            ]
 
-            batch_size = 10
-            hidden_dim = 20
-            hiddens = torch.zeros(1, batch_size, hidden_dim, device=self.device)
-            # get optimizer
+            hiddens = None
             optimizer = self.optimizers()
+            losses = []
 
-            for split_batch in range(split_batches):
-                # 4. Perform the optimization in a loop
-                loss, hiddens = self.my_rnn(split_batch, hiddens)
+            # 4. Perform the optimization in a loop
+            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()
 
                 # 5. "Truncate"
-                hiddens = hiddens.detach()
+                hiddens = [h.detach() for h in hiddens]
+                losses.append(loss.detach())
+
+            avg_loss = sum(losses) / len(losses)
+            self.log("train_loss", avg_loss, prog_bar=True)
 
             # 6. Remove the return of `hiddens`
             # Returning loss in manual optimization is not needed
             return None
 
         def configure_optimizers(self):
-            return optim.Adam(self.my_rnn.parameters(), lr=0.001)
+            return optim.Adam(self.parameters(), lr=0.001)
+
+        def train_dataloader(self):
+            return DataLoader(AverageDataset(), batch_size=self.batch_size)
+
 
     if __name__ == "__main__":
         model = LitModel()
         trainer = pl.Trainer(max_epochs=5)
-        trainer.fit(model, train_dataloader) # Define your own dataloader
+        trainer.fit(model)