Merge branch 'main' into angelayi/aoti_fix

Author: svekars

.ci/docker/requirements.txt

@@ -28,8 +28,8 @@ tensorboard
 jinja2==3.1.3
 pytorch-lightning
 torchx
-torchrl==0.5.0
-tensordict==0.5.0
+torchrl==0.6.0
+tensordict==0.6.0
 ax-platform>=0.4.0
 nbformat>=5.9.2
 datasets

.jenkins/build.sh

@@ -22,8 +22,10 @@ sudo apt-get install -y pandoc
 #Install PyTorch Nightly for test.
 # Nightly - pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu102/torch_nightly.html
 # Install 2.5 to merge all 2.4 PRs - uncomment to install nightly binaries (update the version as needed).
-# pip uninstall -y torch torchvision torchaudio torchtext torchdata
-# pip3 install torch==2.5.0 torchvision torchaudio --no-cache-dir --index-url https://download.pytorch.org/whl/test/cu124
+sudo pip uninstall -y torch torchvision torchaudio torchtext torchdata
+sudo pip3 install torch==2.6.0 torchvision --no-cache-dir --index-url https://download.pytorch.org/whl/test/cu124
+sudo pip uninstall -y fbgemm-gpu torchrec
+sudo pip3 install fbgemm-gpu==1.1.0 torchrec==1.0.0 --no-cache-dir --index-url https://download.pytorch.org/whl/test/cu124
 
 # Install two language tokenizers for Translation with TorchText tutorial
 python -m spacy download en_core_web_sm

.jenkins/validate_tutorials_built.py

@@ -51,7 +51,10 @@
     "intermediate_source/flask_rest_api_tutorial",
     "intermediate_source/text_to_speech_with_torchaudio",
     "intermediate_source/tensorboard_profiler_tutorial", # reenable after 2.0 release.
-    "intermediate_source/torch_export_tutorial" # reenable after 2940 is fixed.
+    "intermediate_source/torch_export_tutorial", # reenable after 2940 is fixed.
+    "advanced_source/pendulum",
+    "beginner_source/onnx/export_simple_model_to_onnx_tutorial",
+    "beginner_source/onnx/onnx_registry_tutorial"
 ]
 
 def tutorial_source_dirs() -> List[Path]:

.lycheeignore

@@ -12,3 +12,9 @@ https://pytorch.org/tutorials/beginner/colab/n
 
 # Ignore local host link from intermediate_source/tensorboard_tutorial.rst
 http://localhost:6006
+
+# Ignore local host link from recipes_source/deployment_with_flask.rst
+http://localhost:5000/predict 
+
+# Ignore local host link from advanced_source/cpp_frontend.rst 
+https://www.uber.com/blog/deep-neuroevolution/

CONTRIBUTING.md

@@ -218,9 +218,8 @@ described in the preceding sections:
 - [NLP From Scratch: Generating Names with a Character-Level RNN
 Tutorial](https://pytorch.org/tutorials/intermediate/char_rnn_generation_tutorial.html)
 
-If you are creating a recipe, we recommend that you use [this
-template](https://github.com/pytorch/tutorials/blob/tutorials_refresh/recipes_source/recipes/example_recipe.py)
-as a guide.
+If you are creating a recipe, [this is a good
+example.](https://github.com/pytorch/tutorials/blob/main/recipes_source/recipes/what_is_state_dict.py)
 
 
 # Submission Process #

advanced_source/cpp_autograd.rst

@@ -255,9 +255,9 @@ Out:
   [ CPUFloatType{3,4} ]
 
 Please see the documentation for ``torch::autograd::backward``
-(`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1afa9b5d4329085df4b6b3d4b4be48914b.html>`_)
+(`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1a1403bf65b1f4f8c8506a9e6e5312d030.html>`_)
 and ``torch::autograd::grad``
-(`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1a1e03c42b14b40c306f9eb947ef842d9c.html>`_)
+(`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1ab9fa15dc09a8891c26525fb61d33401a.html>`_)
 for more information on how to use them.
 
 Using custom autograd function in C++
@@ -394,9 +394,9 @@ C++ using the following table:
 +--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 | Python                         | C++                                                                                                                                                                    |
 +================================+========================================================================================================================================================================+
-| ``torch.autograd.backward``    | ``torch::autograd::backward`` (`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1afa9b5d4329085df4b6b3d4b4be48914b.html>`_)                  |
+| ``torch.autograd.backward``    | ``torch::autograd::backward`` (`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1a1403bf65b1f4f8c8506a9e6e5312d030.html>`_)                  |
 +--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| ``torch.autograd.grad``        | ``torch::autograd::grad`` (`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1a1e03c42b14b40c306f9eb947ef842d9c.html>`_)                      |
+| ``torch.autograd.grad``        | ``torch::autograd::grad`` (`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1ab9fa15dc09a8891c26525fb61d33401a.html>`_)                      |
 +--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 | ``torch.Tensor.detach``        | ``torch::Tensor::detach`` (`link <https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor6detachEv>`_)                                              |
 +--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

advanced_source/cpp_custom_ops.rst

@@ -19,6 +19,10 @@ Custom C++ and CUDA Operators
        * PyTorch 2.4 or later
        * Basic understanding of C++ and CUDA programming
 
+.. note::
+
+  This tutorial will also work on AMD ROCm with no additional modifications.
+
 PyTorch offers a large library of operators that work on Tensors (e.g. torch.add, torch.sum, etc).
 However, you may wish to bring a new custom operator to PyTorch. This tutorial demonstrates the
 blessed path to authoring a custom operator written in C++/CUDA.

advanced_source/cpp_frontend.rst

@@ -57,7 +57,7 @@ the right tool for the job. Examples for such environments include:
   Multiprocessing is an alternative, but not as scalable and has significant
   shortcomings. C++ has no such constraints and threads are easy to use and
   create. Models requiring heavy parallelization, like those used in `Deep
-  Neuroevolution <https://eng.uber.com/deep-neuroevolution/>`_, can benefit from
+  Neuroevolution <https://www.uber.com/blog/deep-neuroevolution/>`_, can benefit from
   this.
 - **Existing C++ Codebases**: You may be the owner of an existing C++
   application doing anything from serving web pages in a backend server to
@@ -662,7 +662,7 @@ Defining the DCGAN Modules
 We now have the necessary background and introduction to define the modules for
 the machine learning task we want to solve in this post. To recap: our task is
 to generate images of digits from the `MNIST dataset
-<http://yann.lecun.com/exdb/mnist/>`_. We want to use a `generative adversarial
+<https://huggingface.co/datasets/ylecun/mnist>`_. We want to use a `generative adversarial
 network (GAN)
 <https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf>`_ to solve
 this task. In particular, we'll use a `DCGAN architecture

advanced_source/custom_ops_landing_page.rst

@@ -23,6 +23,7 @@ You may wish to author a custom operator from Python (as opposed to C++) if:
   respect to ``torch.compile`` and ``torch.export``.
 - you have some Python bindings to C++/CUDA kernels and want those to compose with PyTorch
   subsystems (like ``torch.compile`` or ``torch.autograd``)
+- you are using Python (and not a C++-only environment like AOTInductor).
 
 Integrating custom C++ and/or CUDA code with PyTorch
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

advanced_source/pendulum.py

@@ -33,9 +33,9 @@
 
   In the process, we will touch three crucial components of TorchRL:
 
-* `environments <https://pytorch.org/rl/reference/envs.html>`__
-* `transforms <https://pytorch.org/rl/reference/envs.html#transforms>`__
-* `models (policy and value function) <https://pytorch.org/rl/reference/modules.html>`__
+* `environments <https://pytorch.org/rl/stable/reference/envs.html>`__
+* `transforms <https://pytorch.org/rl/stable/reference/envs.html#transforms>`__
+* `models (policy and value function) <https://pytorch.org/rl/stable/reference/modules.html>`__
 
 """
 
@@ -384,7 +384,7 @@ def _reset(self, tensordict):
 # convenient shortcuts to the content of the output and input spec containers.
 #
 # TorchRL offers multiple :class:`~torchrl.data.TensorSpec`
-# `subclasses <https://pytorch.org/rl/reference/data.html#tensorspec>`_ to
+# `subclasses <https://pytorch.org/rl/stable/reference/data.html#tensorspec>`_ to
 # encode the environment's input and output characteristics.
 #
 # Specs shape