chore: rename variables

metaopt · Aug 29, 2022 · 169ee5b · 169ee5b
1 parent ecc8361
commit 169ee5b
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Showing 3 changed files with 22 additions and 20 deletions.
diff --git a/tests/test_alias.py b/tests/test_alias.py
@@ -76,8 +76,8 @@ def test_sgd(
         loss = F.cross_entropy(pred, ys)
         loss_ref = F.cross_entropy(pred_ref, ys)
 
-        grad = torch.autograd.grad(loss, params)
-        updates, optim_state = optim.update(grad, optim_state, params=params, inplace=inplace)
+        grads = torch.autograd.grad(loss, params)
+        updates, optim_state = optim.update(grads, optim_state, params=params, inplace=inplace)
         params = torchopt.apply_updates(params, updates, inplace=inplace)
 
         optim_ref.zero_grad()
@@ -134,8 +134,8 @@ def test_adam(
         loss = F.cross_entropy(pred, ys)
         loss_ref = F.cross_entropy(pred_ref, ys)
 
-        grad = torch.autograd.grad(loss, params)
-        updates, optim_state = optim.update(grad, optim_state, params=params, inplace=inplace)
+        grads = torch.autograd.grad(loss, params)
+        updates, optim_state = optim.update(grads, optim_state, params=params, inplace=inplace)
         params = torchopt.apply_updates(params, updates, inplace=inplace)
 
         optim_ref.zero_grad()
@@ -193,8 +193,8 @@ def test_adam_accelerated_cpu(
         loss = F.cross_entropy(pred, ys)
         loss_ref = F.cross_entropy(pred_ref, ys)
 
-        grad = torch.autograd.grad(loss, params)
-        updates, optim_state = optim.update(grad, optim_state, params=params, inplace=inplace)
+        grads = torch.autograd.grad(loss, params)
+        updates, optim_state = optim.update(grads, optim_state, params=params, inplace=inplace)
         params = torchopt.apply_updates(params, updates, inplace=inplace)
 
         optim_ref.zero_grad()
@@ -255,8 +255,8 @@ def test_adam_accelerated_cuda(
         loss = F.cross_entropy(pred, ys)
         loss_ref = F.cross_entropy(pred_ref, ys)
 
-        grad = torch.autograd.grad(loss, params)
-        updates, optim_state = optim.update(grad, optim_state, params=params, inplace=inplace)
+        grads = torch.autograd.grad(loss, params)
+        updates, optim_state = optim.update(grads, optim_state, params=params, inplace=inplace)
         params = torchopt.apply_updates(params, updates, inplace=inplace)
 
         optim_ref.zero_grad()
@@ -316,8 +316,8 @@ def test_rmsprop(
         loss = F.cross_entropy(pred, ys)
         loss_ref = F.cross_entropy(pred_ref, ys)
 
-        grad = torch.autograd.grad(loss, params)
-        updates, optim_state = optim.update(grad, optim_state, params=params, inplace=inplace)
+        grads = torch.autograd.grad(loss, params)
+        updates, optim_state = optim.update(grads, optim_state, params=params, inplace=inplace)
         params = torchopt.apply_updates(params, updates, inplace=inplace)
 
         optim_ref.zero_grad()

diff --git a/torchopt/_src/optimizer/base.py b/torchopt/_src/optimizer/base.py
@@ -103,8 +103,8 @@ def f(p):
             return p.grad
 
         for i, (params, state) in enumerate(zip(self.param_groups, self.state_groups)):
-            grad = pytree.tree_map(f, params)
-            updates, new_state = self.impl.update(grad, state, params=params, inplace=True)
+            grads = pytree.tree_map(f, params)
+            updates, new_state = self.impl.update(grads, state, params=params, inplace=True)
             self.param_groups[i] = apply_updates(params, updates, inplace=True)
             self.state_groups[i] = new_state
 

diff --git a/torchopt/_src/optimizer/meta/base.py b/torchopt/_src/optimizer/meta/base.py
@@ -53,24 +53,26 @@ def step(self, loss: torch.Tensor):
             loss (torch.Tensor): The loss that is used to compute the gradients to the network
                 parameters.
         """  # pylint: disable=line-too-long
-        # step parameter only
+        # Step parameter only
         for i, (param_container, new_state) in enumerate(
             zip(self.param_containers_groups, self.state_groups)
         ):
-            flattened_params, container_tree = pytree.tree_flatten(param_container)
+            flattened_params, container_treedef = pytree.tree_flatten(param_container)
             flattened_params = tuple(flattened_params)
-            grad = torch.autograd.grad(loss, flattened_params, create_graph=True, allow_unused=True)
+            grads = torch.autograd.grad(
+                loss, flattened_params, create_graph=True, allow_unused=True
+            )
             updates, new_state = self.impl.update(
-                grad,
+                grads,
                 new_state,
                 params=flattened_params,
                 inplace=False,
             )
             self.state_groups[i] = new_state
-            new_params = apply_updates(flattened_params, updates, inplace=False)
-            unflattened_new_params = container_tree.unflatten(new_params)
-            for container, unflatten_param in zip(param_container, unflattened_new_params):
-                container.update(unflatten_param)
+            flattened_new_params = apply_updates(flattened_params, updates, inplace=False)
+            new_params = pytree.tree_unflatten(container_treedef, flattened_new_params)
+            for container, new_param in zip(param_container, new_params):
+                container.update(new_param)
 
     def add_param_group(self, net):
         """Add a param group to the optimizer's :attr:`state_groups`."""