Merge branch 'main' into remove-tensorboard-profiler-tutorial

Author: jiannanWang

.ci/docker/requirements.txt

@@ -16,7 +16,7 @@ pandocfilters==1.5.1
 markdown==3.8.2
 
 # PyTorch Theme
-pytorch_sphinx_theme2==0.4.6
+pytorch_sphinx_theme2==0.4.9
 
 # Tutorial dependencies
 tqdm==4.66.1
@@ -25,7 +25,7 @@ pydantic>=2.10
 fastapi
 matplotlib
 librosa
-torch==2.11
+torch==2.12
 torchvision
 torchdata
 networkx
@@ -34,10 +34,10 @@ bs4
 awscliv2==2.1.1
 flask
 spacy==3.7.1  # Keep this version consistent with the model version in .jenkins/build.sh
-ray[serve,train,tune]==2.54.0
+ray[serve,train,tune]==2.55.0
 tiktoken
 tensorboard
-jinja2==3.1.3
+jinja2==3.1.6
 pytorch-lightning
 torchx
 torchrl==0.10.1

.github/workflows/docathon-assign.yml

@@ -28,14 +28,14 @@ jobs:
                   repo: context.repo.repo,
                   issue_number: issueNumber
                 });
-              const hasLabel = issue.labels.some(label => label.name === 'docathon-h1-2025');
+              const hasLabel = issue.labels.some(label => label.name === 'docathon-2026');
               if (hasLabel) {
                 if (issue.assignee !== null) {
                   await github.rest.issues.createComment({
                     owner: context.repo.owner,
                     repo: context.repo.repo,
                     issue_number: issueNumber,
-                    body: "The issue is already assigned. Please pick an opened and unnasigned issue with the [docathon-h1-2025 label](https://github.com/pytorch/pytorch/issues?q=is%3Aopen+is%3Aissue+label%3Adocathon-h1-2025)."
+                    body: "The issue is already assigned. Please pick an opened and unnasigned issue with the [docathon-2026 label](https://github.com/pytorch/pytorch/issues?q=is%3Aopen+is%3Aissue+label%3Adocathon-2026)."
                   });
                 } else {
                   await github.rest.issues.addAssignees({
@@ -46,7 +46,7 @@ jobs:
                   });
                 }
               } else {
-                const commmentMessage = "This issue does not have the correct label. Please pick an opened and unnasigned issue with the [docathon-h1-2025 label](https://github.com/pytorch/pytorch/issues?q=is%3Aopen+is%3Aissue+label%3Adocathon-h1-2025)."
+                const commmentMessage = "This issue does not have the correct label. Please pick an opened and unnasigned issue with the [docathon-2026 label](https://github.com/pytorch/pytorch/issues?q=is%3Aopen+is%3Aissue+label%3Adocathon-2026)."
                 await github.rest.issues.createComment({
                   owner: context.repo.owner,
                   repo: context.repo.repo,

.jenkins/build.sh

@@ -30,14 +30,14 @@ sudo apt-get install -y pandoc
 # Install PyTorch Nightly for test.
 if [ "${USE_NIGHTLY:-0}" -eq 1 ]; then
   sudo pip uninstall -y torch torchvision torchaudio
-  pip3 install torch==2.11.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu130
+  pip3 install torch==2.12.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu130
   pip show torch
 fi
 
 # Nightly - pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu102/torch_nightly.html
-# Install 2.11 to merge all 2.11 PRs - uncomment to install nightly binaries (update the version as needed).
+# Install 2.12 to merge all 2.12 PRs - uncomment to install nightly binaries (update the version as needed).
 # sudo pip uninstall -y torch torchvision torchaudio torchtext torchdata
-# pip3 install torch==2.11.0 torchvision torchaudio --no-cache-dir --index-url https://download.pytorch.org/whl/test/cu130
+# pip3 install torch==2.12.0 torchvision torchaudio --no-cache-dir --index-url https://download.pytorch.org/whl/test/cu130
 # Install two language tokenizers for Translation with TorchText tutorial
 # Note: keep this version consistent with the spacy version in requirements.txt
 pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl

advanced_source/privateuseone.rst

@@ -4,7 +4,6 @@ Facilitating New Backend Integration by PrivateUse1
 In this tutorial we will walk through some necessary steps to integrate a new backend
 living outside ``pytorch/pytorch`` repo by ``PrivateUse1``. Note that this tutorial assumes that
 you already have a basic understanding of PyTorch.
-you are an advanced user of PyTorch.
 
 .. note::
 

beginner_source/basics/data_tutorial.py

@@ -48,22 +48,22 @@
 import torch
 from torch.utils.data import Dataset
 from torchvision import datasets
-from torchvision.transforms import ToTensor
+from torchvision.transforms import v2
 import matplotlib.pyplot as plt
 
 
 training_data = datasets.FashionMNIST(
     root="data",
     train=True,
     download=True,
-    transform=ToTensor()
+    transform=v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])
 )
 
 test_data = datasets.FashionMNIST(
     root="data",
     train=False,
     download=True,
-    transform=ToTensor()
+    transform=v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])
 )
 
 

beginner_source/basics/optimization_tutorial.py

@@ -28,20 +28,20 @@
 from torch import nn
 from torch.utils.data import DataLoader
 from torchvision import datasets
-from torchvision.transforms import ToTensor
+from torchvision.transforms import v2
 
 training_data = datasets.FashionMNIST(
     root="data",
     train=True,
     download=True,
-    transform=ToTensor()
+    transform=v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])
 )
 
 test_data = datasets.FashionMNIST(
     root="data",
     train=False,
     download=True,
-    transform=ToTensor()
+    transform=v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])
 )
 
 train_dataloader = DataLoader(training_data, batch_size=64)

beginner_source/basics/quickstart_tutorial.py

@@ -26,7 +26,7 @@
 from torch import nn
 from torch.utils.data import DataLoader
 from torchvision import datasets
-from torchvision.transforms import ToTensor
+from torchvision.transforms import v2
 
 ######################################################################
 # PyTorch offers domain-specific libraries such as `TorchText <https://pytorch.org/text/stable/index.html>`_,
@@ -43,15 +43,15 @@
     root="data",
     train=True,
     download=True,
-    transform=ToTensor(),
+    transform=v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)]),
 )
 
 # Download test data from open datasets.
 test_data = datasets.FashionMNIST(
     root="data",
     train=False,
     download=True,
-    transform=ToTensor(),
+    transform=v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)]),
 )
 
 ######################################################################

beginner_source/basics/tensorqs_tutorial.py

@@ -63,7 +63,7 @@
 #
 # ``shape`` is a tuple of tensor dimensions. In the functions below, it determines the dimensionality of the output tensor.
 
-shape = (2,3,)
+shape = (2,3)
 rand_tensor = torch.rand(shape)
 ones_tensor = torch.ones(shape)
 zeros_tensor = torch.zeros(shape)

beginner_source/basics/transforms_tutorial.py

@@ -23,42 +23,47 @@
 
 The FashionMNIST features are in PIL Image format, and the labels are integers.
 For training, we need the features as normalized tensors, and the labels as one-hot encoded tensors.
-To make these transformations, we use ``ToTensor`` and ``Lambda``.
+To make these transformations, we use the ``torchvision.transforms.v2`` API along with ``torch.nn.functional.one_hot``.
 """
 
 import torch
+import torch.nn.functional as F
 from torchvision import datasets
-from torchvision.transforms import ToTensor, Lambda
+from torchvision.transforms import v2
 
 ds = datasets.FashionMNIST(
     root="data",
     train=True,
     download=True,
-    transform=ToTensor(),
-    target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1))
+    transform=v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)]),
+    target_transform=v2.Lambda(
+        lambda y: F.one_hot(torch.tensor(y), num_classes=10).float()
+    ),
 )
 
 #################################################
-# ToTensor()
+# ToImage() and ToDtype()
 # -------------------------------
 #
-# `ToTensor <https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.ToTensor>`_
-# converts a PIL image or NumPy ``ndarray`` into a ``FloatTensor``. and scales
-# the image's pixel intensity values in the range [0., 1.]
+# The ``torchvision.transforms.v2`` API replaces the legacy ``ToTensor`` transform with a two-step pipeline.
+# `v2.ToImage <https://pytorch.org/vision/stable/generated/torchvision.transforms.v2.ToImage.html>`_
+# converts a PIL image or NumPy ``ndarray`` into a ``torchvision.tv_tensors.Image`` tensor, and
+# `v2.ToDtype <https://pytorch.org/vision/stable/generated/torchvision.transforms.v2.ToDtype.html>`_
+# with ``scale=True`` casts it to ``float32`` and scales the pixel intensity values to the range [0., 1.].
 #
 
 ##############################################
 # Lambda Transforms
 # -------------------------------
 #
-# Lambda transforms apply any user-defined lambda function. Here, we define a function
-# to turn the integer into a one-hot encoded tensor.
-# It first creates a zero tensor of size 10 (the number of labels in our dataset) and calls
-# `scatter_ <https://pytorch.org/docs/stable/generated/torch.Tensor.scatter_.html>`_ which assigns a
-# ``value=1`` on the index as given by the label ``y``.
+# Lambda transforms apply any user-defined lambda function. Here, we use
+# `torch.nn.functional.one_hot <https://pytorch.org/docs/stable/generated/torch.nn.functional.one_hot.html>`_
+# to turn the integer label into a one-hot encoded tensor of size 10 (the number of labels in our dataset),
+# then cast it to ``float`` to match the expected dtype.
 
-target_transform = Lambda(lambda y: torch.zeros(
-    10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1))
+target_transform = v2.Lambda(
+    lambda y: F.one_hot(torch.tensor(y), num_classes=10).float()
+)
 
 ######################################################################
 # --------------
@@ -67,4 +72,5 @@
 #################################################################
 # Further Reading
 # ~~~~~~~~~~~~~~~~~
-# - `torchvision.transforms API <https://pytorch.org/vision/stable/transforms.html>`_
+# - `Getting started with transforms v2 <https://pytorch.org/vision/stable/auto_examples/transforms/plot_transforms_getting_started.html>`_
+# - `torchvision.transforms.v2 API <https://pytorch.org/vision/stable/transforms.html#v2-api-reference-recommended>`_

beginner_source/blitz/cifar10_tutorial.py

@@ -57,7 +57,7 @@
 """
 import torch
 import torchvision
-import torchvision.transforms as transforms
+from torchvision.transforms import v2
 
 ########################################################################
 # The output of torchvision datasets are PILImage images of range [0, 1].
@@ -69,9 +69,10 @@
 #     BrokenPipeError or RuntimeError related to multiprocessing, try setting
 #     the num_worker of torch.utils.data.DataLoader() to 0.
 
-transform = transforms.Compose(
-    [transforms.ToTensor(),
-     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+transform = v2.Compose([
+    v2.ToImage(),
+    v2.ToDtype(torch.float32, scale=True),
+    v2.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
 
 batch_size = 4
 
@@ -191,7 +192,7 @@ def forward(self, x):
 ########################################################################
 # Let's quickly save our trained model:
 
-PATH = './cifar_net.pth'
+PATH = './cifar_net.pt'
 torch.save(net.state_dict(), PATH)
 
 ########################################################################
@@ -302,7 +303,7 @@ def forward(self, x):
 # Let's first define our device as the first visible cuda device if we have
 # CUDA available:
 
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+device = torch.device(torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else 'cpu')
 
 # Assuming that we are on a CUDA machine, this should print a CUDA device:
 
@@ -355,9 +356,9 @@ def forward(self, x):
 # -  `Discuss PyTorch on the Forums`_
 # -  `Chat with other users on Slack`_
 #
-# .. _Train a state-of-the-art ResNet network on imagenet: https://github.com/pytorch/examples/tree/master/imagenet
-# .. _Train a face generator using Generative Adversarial Networks: https://github.com/pytorch/examples/tree/master/dcgan
-# .. _Train a word-level language model using Recurrent LSTM networks: https://github.com/pytorch/examples/tree/master/word_language_model
+# .. _Train a state-of-the-art ResNet network on imagenet: https://github.com/pytorch/examples/tree/main/imagenet
+# .. _Train a face generator using Generative Adversarial Networks: https://github.com/pytorch/examples/tree/main/dcgan
+# .. _Train a word-level language model using Recurrent LSTM networks: https://github.com/pytorch/examples/tree/main/word_language_model
 # .. _More examples: https://github.com/pytorch/examples
 # .. _More tutorials: https://github.com/pytorch/tutorials
 # .. _Discuss PyTorch on the Forums: https://discuss.pytorch.org/