Removing formatting issues.

Author: AlannaBurke

.ci/docker/requirements.txt

@@ -14,7 +14,7 @@ tqdm==4.66.1
 numpy==1.24.4
 matplotlib
 librosa
-torch==2.7
+torch==2.6
 torchvision
 torchdata
 networkx
@@ -67,7 +67,7 @@ iopath
 pygame==2.6.0
 pycocotools
 semilearn==0.3.2
-torchao==0.10.0
+torchao==0.5.0
 segment_anything==1.0
 torchrec==1.1.0; platform_system == "Linux"
-fbgemm-gpu==1.2.0; platform_system == "Linux"
+fbgemm-gpu==1.1.0; platform_system == "Linux"

.jenkins/build.sh

@@ -22,10 +22,13 @@ 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).
+# 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 pip uninstall -y torch torchvision torchaudio torchtext torchdata torchrl tensordict
 # sudo pip3 install fbgemm-gpu==1.1.0 torchrec==1.0.0 --no-cache-dir --index-url https://download.pytorch.org/whl/test/cu124
-# pip3 install torch==2.7.0 torchvision torchaudio --no-cache-dir --index-url https://download.pytorch.org/whl/test/cu126
+sudo pip uninstall -y torch torchvision torchaudio torchtext torchdata torchrl tensordict
+pip3 install torch==2.7.0 torchvision torchaudio --no-cache-dir --index-url https://download.pytorch.org/whl/test/cu126
+#sudo pip uninstall -y fbgemm-gpu
 # Install two language tokenizers for Translation with TorchText tutorial
 python -m spacy download en_core_web_sm
 python -m spacy download de_core_news_sm

.jenkins/metadata.json

@@ -1,7 +1,4 @@
 {
-  "recipes_source/torch_logs.py": {
-    "duration": 0
-  },
   "intermediate_source/ax_multiobjective_nas_tutorial.py": {
     "extra_files": ["intermediate_source/mnist_train_nas.py"],
     "duration": 2000

.jenkins/validate_tutorials_built.py

@@ -50,8 +50,15 @@
     "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/torchrec_intro_tutorial", # reenable after 3302 is fixe
-    "intermediate_source/memory_format_tutorial"
+    "advanced_source/semi_structured_sparse", # reenable after 3303 is fixed.
+    "intermediate_source/mario_rl_tutorial", # reenable after 3302 is fixed
+    "intermediate_source/reinforcement_ppo", # reenable after 3302 is fixed
+    "intermediate_source/pinmem_nonblock", # reenable after 3302 is fixed
+    "intermediate_source/dqn_with_rnn_tutorial", # reenable after 3302 is fixed
+    "advanced_source/pendulum", # reenable after 3302 is fixed
+    "advanced_source/coding_ddpg", # reenable after 3302 is fixed
+    "intermediate_source/torchrec_intro_tutorial", # reenable after 3302 is fixed
+    "recipes_source/recipes/reasoning_about_shapes" # reenable after 3326 is fixed
 ]
 
 def tutorial_source_dirs() -> List[Path]:

advanced_source/sharding.rst

@@ -22,10 +22,6 @@ We highly recommend CUDA when using torchRec. If using CUDA: - cuda >=
     !sudo chmod +x Miniconda3-py37_4.9.2-Linux-x86_64.sh
     !sudo bash ./Miniconda3-py37_4.9.2-Linux-x86_64.sh -b -f -p /usr/local
 
-.. code:: python
-
-    # install pytorch with cudatoolkit 11.3
-    !sudo conda install pytorch cudatoolkit=11.3 -c pytorch-nightly -y
 
 Installing torchRec will also install
 `FBGEMM <https://github.com/pytorch/fbgemm>`__, a collection of CUDA

advanced_source/torch_script_custom_ops.rst

@@ -190,6 +190,8 @@ Environment setup
 We need an installation of PyTorch and OpenCV.  The easiest and most platform
 independent way to get both is to via Conda::
 
+.. these need to be updated
+
   conda install -c pytorch pytorch
   conda install opencv
 

beginner_source/colab.rst

@@ -11,7 +11,7 @@ PyTorch Version in Google Colab
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Wen you are running a tutorial that requires a version of PyTorch that has
-just been released, that version might not be yet available in Google Colab.
+jst been released, that version might not be yet available in Google Colab.
 To check that you have the required ``torch`` and compatible domain libraries
 installed, run ``!pip list``.
 
@@ -27,7 +27,7 @@ Using Tutorial Data from Google Drive in Colab
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 We've added a new feature to tutorials that allows users to open the
-notebook associated with a tutorial in Google Colab. You may need to
+ntebook associated with a tutorial in Google Colab. You may need to
 copy data to your Google drive account to get the more complex tutorials
 to work.
 

beginner_source/introyt/captumyt.py

@@ -106,14 +106,7 @@
 -  Matplotlib version 3.3.4, since Captum currently uses a Matplotlib
    function whose arguments have been renamed in later versions
 
-To install Captum in an Anaconda or pip virtual environment, use the
-appropriate command for your environment below:
-
-With ``conda``:
-
-.. code-block:: sh
-
-    conda install pytorch torchvision captum flask-compress matplotlib=3.3.4 -c pytorch
+To install Captum, use the appropriate command for your environment below:
 
 With ``pip``:
 
@@ -127,51 +120,56 @@
 
 A First Example
 ---------------
- 
+
 To start, let’s take a simple, visual example. We’ll start with a ResNet
 model pretrained on the ImageNet dataset. We’ll get a test input, and
 use different **Feature Attribution** algorithms to examine how the
 input images affect the output, and see a helpful visualization of this
 input attribution map for some test images.
- 
-First, some imports: 
 
-"""
+First, some imports:
 
-import torch
-import torch.nn.functional as F
-import torchvision.transforms as transforms
-import torchvision.models as models
+"""
 
-import captum
-from captum.attr import IntegratedGradients, Occlusion, LayerGradCam, LayerAttribution
-from captum.attr import visualization as viz
+import json
 
 import os, sys
-import json
 
-import numpy as np
-from PIL import Image
+import captum
 import matplotlib.pyplot as plt
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torchvision.models as models
+import torchvision.transforms as transforms
+from captum.attr import (
+    IntegratedGradients,
+    LayerAttribution,
+    LayerGradCam,
+    Occlusion,
+    visualization as viz,
+)
 from matplotlib.colors import LinearSegmentedColormap
+from PIL import Image
 
 
 #########################################################################
 # Now we’ll use the TorchVision model library to download a pretrained
 # ResNet. Since we’re not training, we’ll place it in evaluation mode for
 # now.
-# 
+#
 
-model = models.resnet18(weights='IMAGENET1K_V1')
+model = models.resnet18(weights="IMAGENET1K_V1")
 model = model.eval()
 
 
 #######################################################################
 # The place where you got this interactive notebook should also have an
 # ``img`` folder with a file ``cat.jpg`` in it.
-# 
+#
 
-test_img = Image.open('img/cat.jpg')
+test_img = Image.open("img/cat.jpg")
 test_img_data = np.asarray(test_img)
 plt.imshow(test_img_data)
 plt.show()

beginner_source/introyt/tensorboardyt_tutorial.py

@@ -24,13 +24,6 @@
 To run this tutorial, you’ll need to install PyTorch, TorchVision,
 Matplotlib, and TensorBoard.
 
-With ``conda``:
-
-.. code-block:: sh
-
-    conda install pytorch torchvision -c pytorch
-    conda install matplotlib tensorboard
-
 With ``pip``:
 
 .. code-block:: sh

conf.py

@@ -99,16 +99,10 @@
 
 def reset_seeds(gallery_conf, fname):
     torch.cuda.empty_cache()
-    torch.backends.cudnn.deterministic = True
-    torch.backends.cudnn.benchmark = False
-    torch._dynamo.reset()
-    torch._inductor.config.force_disable_caches = True
     torch.manual_seed(42)
     torch.set_default_device(None)
     random.seed(10)
     numpy.random.seed(10)
-    torch.set_grad_enabled(True)
-
     gc.collect()
 
 sphinx_gallery_conf = {

en-wordlist.txt

@@ -698,14 +698,3 @@ TorchServe
 Inductor’s
 onwards
 recompilations
-BiasCorrection
-ELU
-GELU
-NNCF
-OpenVINO
-OpenVINOQuantizer
-PReLU
-Quantizer
-SmoothQuant
-quantizer
-quantizers

intermediate_source/dist_tuto.rst

@@ -523,6 +523,7 @@ for an available MPI implementation. The following steps install the MPI
 backend, by installing PyTorch `from
 source <https://github.com/pytorch/pytorch#from-source>`__.
 
+.. needs an update
 1. Create and activate your Anaconda environment, install all the
    pre-requisites following `the
    guide <https://github.com/pytorch/pytorch#from-source>`__, but do

intermediate_source/memory_format_tutorial.py

@@ -376,22 +376,6 @@ def attribute(m):
     for (k, v) in attrs.items():
         setattr(m, k, v)
 
-import gc
-import sys
-
-torch._dynamo.reset()
-if torch.cuda.is_available():
-    torch.cuda.empty_cache()
-
-gc.collect()
-
-# Clear any references to the wrapper functions
-del old_attrs
-del contains_cl
-del print_inputs
-del check_wrapper
-del attribute
-
 ######################################################################
 # Work to do
 # ----------

intermediate_source/torch_compile_tutorial.py

@@ -101,11 +101,8 @@ def forward(self, x):
         return torch.nn.functional.relu(self.lin(x))
 
 mod = MyModule()
-mod.compile()
-print(mod(t))
-## or:
-# opt_mod = torch.compile(mod)
-# print(opt_mod(t))
+opt_mod = torch.compile(mod)
+print(opt_mod(t))
 
 ######################################################################
 # torch.compile and Nested Calls
@@ -138,8 +135,8 @@ def forward(self, x):
         return torch.nn.functional.relu(self.outer_lin(x))
 
 outer_mod = OuterModule()
-outer_mod.compile()
-print(outer_mod(t))
+opt_outer_mod = torch.compile(outer_mod)
+print(opt_outer_mod(t))
 
 ######################################################################
 # We can also disable some functions from being compiled by using
@@ -200,12 +197,6 @@ def outer_function():
 # 4. **Compile Leaf Functions First:** In complex models with multiple nested
 # functions and modules, start by compiling the leaf functions or modules first.
 # For more information see `TorchDynamo APIs for fine-grained tracing <https://pytorch.org/docs/stable/torch.compiler_fine_grain_apis.html>`__.
-#
-# 5. **Prefer ``mod.compile()`` over ``torch.compile(mod)``:** Avoids ``_orig_`` prefix issues in ``state_dict``.
-#
-# 6. **Use ``fullgraph=True`` to catch graph breaks:** Helps ensure end-to-end compilation, maximizing speedup
-# and compatibility with ``torch.export``.
-
 
 ######################################################################
 # Demonstrating Speedups

prototype_source/inductor_windows.rst

@@ -22,9 +22,10 @@ Install a Compiler
 
 C++ compiler is required for TorchInductor optimization, let's take Microsoft Visual C++ (MSVC) as an example.
 
-#. Download and install `MSVC <https://visualstudio.microsoft.com/downloads/>`_.
+1. Download and install `MSVC <https://visualstudio.microsoft.com/downloads/>`_.
 
-#. During Installation, select **Workloads** and then **Desktop & Mobile**. Select a checkmark on **Desktop Development with C++** and install.
+1. During Installation, select **Workloads** and then **Desktop & Mobile**.
+1. Select a checkmark on **Desktop Development with C++** and install.
 
 .. image:: ../_static/img/install_msvc.png