Merge branch 'main' into svekars-patch-3

Author: Svetlana Karslioglu

intermediate_source/reinforcement_ppo.py

@@ -604,7 +604,7 @@
         data_view = tensordict_data.reshape(-1)
         replay_buffer.extend(data_view.cpu())
         for _ in range(frames_per_batch // sub_batch_size):
-            subdata, *_ = replay_buffer.sample(sub_batch_size)
+            subdata = replay_buffer.sample(sub_batch_size)
             loss_vals = loss_module(subdata.to(device))
             loss_value = (
                 loss_vals["loss_objective"]

recipes_source/recipes/reasoning_about_shapes.py

@@ -0,0 +1,88 @@
+"""
+Reasoning about Shapes in PyTorch
+=================================
+
+When writing models with PyTorch, it is commonly the case that the parameters
+to a given layer depend on the shape of the output of the previous layer. For
+example, the ``in_features`` of an ``nn.Linear`` layer must match the
+``size(-1)`` of the input. For some layers, the shape computation involves
+complex equations, for example convolution operations.
+
+One way around this is to run the forward pass with random inputs, but this is
+wasteful in terms of memory and compute.
+
+Instead, we can make use of the ``meta`` device to determine the output shapes
+of a layer without materializing any data.
+"""
+
+import torch
+import timeit
+
+t = torch.rand(2, 3, 10, 10, device="meta")
+conv = torch.nn.Conv2d(3, 5, 2, device="meta")
+start = timeit.default_timer()
+out = conv(t)
+end = timeit.default_timer()
+
+print(out)
+print(f"Time taken: {end-start}")
+
+
+##########################################################################
+# Observe that since data is not materialized, passing arbitrarily large
+# inputs will not significantly alter the time taken for shape computation.
+
+t_large = torch.rand(2**10, 3, 2**16, 2**16, device="meta")
+start = timeit.default_timer()
+out = conv(t_large)
+end = timeit.default_timer()
+
+print(out)
+print(f"Time taken: {end-start}")
+
+
+######################################################
+# Consider an arbitrary network such as the following:
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = torch.flatten(x, 1) # flatten all dimensions except batch
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+
+###############################################################################
+# We can view the intermediate shapes within an entire network by registering a
+# forward hook to each layer that prints the shape of the output.
+
+def fw_hook(module, input, output):
+    print(f"Shape of output to {module} is {output.shape}.")
+
+
+# Any tensor created within this torch.device context manager will be
+# on the meta device.
+with torch.device("meta"):
+    net = Net()
+    inp = torch.randn((1024, 3, 32, 32))
+
+for name, layer in net.named_modules():
+    layer.register_forward_hook(fw_hook)
+
+out = net(inp)

recipes_source/recipes_index.rst

@@ -123,6 +123,13 @@ Recipes are bite-sized, actionable examples of how to use specific PyTorch featu
    :link: ../recipes/profile_with_itt.html
    :tags: Basics
 
+.. customcarditem::
+   :header: Reasoning about Shapes in PyTorch
+   :card_description: Learn how to use the meta device to reason about shapes in your model.
+   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
+   :link: ../recipes/recipes/reasoning_about_shapes.html
+   :tags: Basics
+
 .. Interpretability
 
 .. customcarditem::