docs: Fix order of zero_grad (#512)

Author: ValerianRey

README.md

@@ -111,11 +111,11 @@ using [UPGrad](https://torchjd.org/stable/docs/aggregation/upgrad/).
       loss1 = loss_fn(output1, target1)
       loss2 = loss_fn(output2, target2)
 
-      optimizer.zero_grad()
 -     loss = loss1 + loss2
 -     loss.backward()
 +     mtl_backward(losses=[loss1, loss2], features=features, aggregator=aggregator)
       optimizer.step()
+      optimizer.zero_grad()
 ```
 
 > [!NOTE]
@@ -150,12 +150,12 @@ Jacobian descent using [UPGrad](https://torchjd.org/stable/docs/aggregation/upgr
 -     loss = loss_fn(output, target)  # shape [1]
 +     losses = loss_fn(output, target)  # shape [16]
 
-      optimizer.zero_grad()
 -     loss.backward()
 +     gramian = engine.compute_gramian(losses)  # shape: [16, 16]
 +     weights = weighting(gramian)  # shape: [16]
 +     losses.backward(weights)
       optimizer.step()
+      optimizer.zero_grad()
 ```
 
 Lastly, you can even combine the two approaches by considering multiple tasks and each element of
@@ -201,10 +201,10 @@ for input, target1, target2 in zip(inputs, task1_targets, task2_targets):
     # Obtain the weights that lead to no conflict between reweighted gradients
     weights = weighting(gramian)  # shape: [16, 2]
 
-    optimizer.zero_grad()
     # Do the standard backward pass, but weighted using the obtained weights
     losses.backward(weights)
     optimizer.step()
+    optimizer.zero_grad()
 ```
 
 > [!NOTE]

docs/source/examples/amp.rst

@@ -12,7 +12,7 @@ case, the losses) should preferably be scaled with a `GradScaler
 following example shows the resulting code for a multi-task learning use-case.
 
 .. code-block:: python
-    :emphasize-lines: 2, 17, 27, 34, 36-38
+    :emphasize-lines: 2, 17, 27, 34-37
 
     import torch
     from torch.amp import GradScaler
@@ -48,10 +48,10 @@ following example shows the resulting code for a multi-task learning use-case.
             loss2 = loss_fn(output2, target2)
 
         scaled_losses = scaler.scale([loss1, loss2])
-        optimizer.zero_grad()
         mtl_backward(losses=scaled_losses, features=features, aggregator=aggregator)
         scaler.step(optimizer)
         scaler.update()
+        optimizer.zero_grad()
 
 .. hint::
     Within the ``torch.autocast`` context, some operations may be done in ``float16`` type. For

docs/source/examples/basic_usage.rst

@@ -59,12 +59,6 @@ We can now compute the losses associated to each element of the batch.
 
 The last steps are similar to gradient descent-based optimization, but using the two losses.
 
-Reset the ``.grad`` field of each model parameter:
-
-.. code-block:: python
-
-    optimizer.zero_grad()
-
 Perform the Jacobian descent backward pass:
 
 .. code-block:: python
@@ -81,3 +75,9 @@ Update each parameter based on its ``.grad`` field, using the ``optimizer``:
     optimizer.step()
 
 The model's parameters have been updated!
+
+As usual, you should now reset the ``.grad`` field of each model parameter:
+
+.. code-block:: python
+
+    optimizer.zero_grad()

docs/source/examples/iwmtl.rst

@@ -10,7 +10,7 @@ this Gramian to reweight the gradients and resolve conflict entirely.
 The following example shows how to do that.
 
 .. code-block:: python
-    :emphasize-lines: 5-6, 18-20, 31-32, 34-35, 37-38, 41-42
+    :emphasize-lines: 5-6, 18-20, 31-32, 34-35, 37-38, 40-41
 
     import torch
     from torch.nn import Linear, MSELoss, ReLU, Sequential
@@ -51,10 +51,10 @@ The following example shows how to do that.
         # Obtain the weights that lead to no conflict between reweighted gradients
         weights = weighting(gramian)  # shape: [16, 2]
 
-        optimizer.zero_grad()
         # Do the standard backward pass, but weighted using the obtained weights
         losses.backward(weights)
         optimizer.step()
+        optimizer.zero_grad()
 
 .. note::
     In this example, the tensor of losses is a matrix rather than a vector. The gramian is thus a

docs/source/examples/iwrm.rst

@@ -64,19 +64,19 @@ batch of data. When minimizing per-instance losses (IWRM), we use either autojac
             for x, y in zip(X, Y):
                 y_hat = model(x).squeeze(dim=1)  # shape: [16]
                 loss = loss_fn(y_hat, y)  # shape: [] (scalar)
-                optimizer.zero_grad()
                 loss.backward()
 
 
                 optimizer.step()
+                optimizer.zero_grad()
 
         In this baseline example, the update may negatively affect the loss of some elements of the
         batch.
 
     .. tab-item:: autojac
 
         .. code-block:: python
-            :emphasize-lines: 5-6, 12, 16, 21, 23
+            :emphasize-lines: 5-6, 12, 16, 21-22
 
             import torch
             from torch.nn import Linear, MSELoss, ReLU, Sequential
@@ -99,19 +99,19 @@ batch of data. When minimizing per-instance losses (IWRM), we use either autojac
             for x, y in zip(X, Y):
                 y_hat = model(x).squeeze(dim=1)  # shape: [16]
                 losses = loss_fn(y_hat, y)  # shape: [16]
-                optimizer.zero_grad()
                 backward(losses, aggregator)
 
 
                 optimizer.step()
+                optimizer.zero_grad()
 
         Here, we compute the Jacobian of the per-sample losses with respect to the model parameters
         and use it to update the model such that no loss from the batch is (locally) increased.
 
     .. tab-item:: autogram (recommended)
 
         .. code-block:: python
-            :emphasize-lines: 5-6, 12, 16-17, 21, 23-25
+            :emphasize-lines: 5-6, 12, 16-17, 21-24
 
             import torch
             from torch.nn import Linear, MSELoss, ReLU, Sequential
@@ -134,11 +134,11 @@ batch of data. When minimizing per-instance losses (IWRM), we use either autojac
             for x, y in zip(X, Y):
                 y_hat = model(x).squeeze(dim=1)  # shape: [16]
                 losses = loss_fn(y_hat, y)  # shape: [16]
-                optimizer.zero_grad()
                 gramian = engine.compute_gramian(losses)  # shape: [16, 16]
                 weights = weighting(gramian)  # shape: [16]
                 losses.backward(weights)
                 optimizer.step()
+                optimizer.zero_grad()
 
         Here, the per-sample gradients are never fully stored in memory, leading to large
         improvements in memory usage and speed compared to autojac, in most practical cases. The

docs/source/examples/lightning_integration.rst

@@ -11,7 +11,7 @@ The following code example demonstrates a basic multi-task learning setup using
 <../docs/autojac/mtl_backward>` at each training iteration.
 
 .. code-block:: python
-    :emphasize-lines: 9-10, 18, 32
+    :emphasize-lines: 9-10, 18, 31
 
     import torch
     from lightning import LightningModule, Trainer
@@ -43,9 +43,9 @@ The following code example demonstrates a basic multi-task learning setup using
             loss2 = mse_loss(output2, target2)
 
             opt = self.optimizers()
-            opt.zero_grad()
             mtl_backward(losses=[loss1, loss2], features=features, aggregator=UPGrad())
             opt.step()
+            opt.zero_grad()
 
         def configure_optimizers(self) -> OptimizerLRScheduler:
             optimizer = Adam(self.parameters(), lr=1e-3)

docs/source/examples/monitoring.rst

@@ -63,6 +63,6 @@ they have a negative inner product).
         loss1 = loss_fn(output1, target1)
         loss2 = loss_fn(output2, target2)
 
-        optimizer.zero_grad()
         mtl_backward(losses=[loss1, loss2], features=features, aggregator=aggregator)
         optimizer.step()
+        optimizer.zero_grad()

docs/source/examples/mtl.rst

@@ -19,7 +19,7 @@ vectors of dimension 10, and their corresponding scalar labels for both tasks.
 
 
 .. code-block:: python
-    :emphasize-lines: 5-6, 19, 33
+    :emphasize-lines: 5-6, 19, 32
 
     import torch
     from torch.nn import Linear, MSELoss, ReLU, Sequential
@@ -52,9 +52,9 @@ vectors of dimension 10, and their corresponding scalar labels for both tasks.
         loss1 = loss_fn(output1, target1)
         loss2 = loss_fn(output2, target2)
 
-        optimizer.zero_grad()
         mtl_backward(losses=[loss1, loss2], features=features, aggregator=aggregator)
         optimizer.step()
+        optimizer.zero_grad()
 
 .. note::
     In this example, the Jacobian is only with respect to the shared parameters. The task-specific

docs/source/examples/partial_jd.rst

@@ -41,8 +41,8 @@ first ``Linear`` layer, thereby reducing memory usage and computation time.
     for x, y in zip(X, Y):
         y_hat = model(x).squeeze(dim=1)  # shape: [16]
         losses = loss_fn(y_hat, y)  # shape: [16]
-        optimizer.zero_grad()
         gramian = engine.compute_gramian(losses)
         weights = weighting(gramian)
         losses.backward(weights)
         optimizer.step()
+        optimizer.zero_grad()

docs/source/examples/rnn.rst

@@ -6,7 +6,7 @@ element of the output sequences. If the gradients of these losses are likely to
 descent can be leveraged to enhance optimization.
 
 .. code-block:: python
-    :emphasize-lines: 5-6, 10, 17, 20
+    :emphasize-lines: 5-6, 10, 17, 19
 
     import torch
     from torch.nn import RNN
@@ -26,9 +26,9 @@ descent can be leveraged to enhance optimization.
         output, _ = rnn(input)  # output is of shape [5, 3, 20].
         losses = ((output - target) ** 2).mean(dim=[1, 2])  # 1 loss per sequence element.
 
-        optimizer.zero_grad()
         backward(losses, aggregator, parallel_chunk_size=1)
         optimizer.step()
+        optimizer.zero_grad()
 
 .. note::
     At the time of writing, there seems to be an incompatibility between ``torch.vmap`` and