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Merge branch 'main' into add-t5-tutorial
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‎.github/ISSUE_TEMPLATE/bug-report.yml

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name: 🐛 Bug Report
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description: Create a tutorial bug report
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title: "[BUG] - <title>"
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labels: [
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"bug"
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]
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body:
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- type: markdown
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attributes:
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value: >
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#### Before submitting a bug, please make sure the issue hasn't been already addressed by searching through [the existing and past issues](https://github.com/pytorch/tutorials/issues?q=is%3Aissue+sort%3Acreated-desc+).
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- type: textarea
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attributes:
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label: Add Link
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description: |
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**Add the link to the tutorial***
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placeholder: |
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Link to the tutorial on the website:
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validations:
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required: true
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- type: textarea
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attributes:
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label: Describe the bug
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description: |
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**Add the bug description**
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placeholder: |
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Provide a detailed description of the issue with code samples if relevant
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```python
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# Sample code to reproduce the problem if relevant
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```
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**Expected Result:** (Describe what you were expecting to see)
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**Actual Result:** (Describe the result)
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```
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The error message you got, with the full traceback.
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```
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validations:
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required: true
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- type: textarea
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attributes:
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label: Describe your environment
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description: |
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**Describe the environment you encountered the bug in:**
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placeholder: |
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* Platform (i.e macOS, Linux, Google Colab):
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* CUDA (yes/no, version?):
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* PyTorch version (run `python -c "import torch; print(torch.__version__)"`):
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validations:
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required: true
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- type: markdown
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attributes:
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value: >
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Thanks for contributing 🎉!

‎.github/ISSUE_TEMPLATE/feature-request.yml

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name: 🚀 Feature request
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description: Submit a proposal for a new PyTorch tutorial or improvement of an existing tutorial
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title: "💡 [REQUEST] - <title>"
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labels: [
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"feature"
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]
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body:
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- type: textarea
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attributes:
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label: 🚀 Descirbe the improvement or the new tutorial
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description: |
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**Describe the improvement**
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placeholder: |
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Explain why this improvement or new tutorial is important. For example, *"This tutorial will help users to better understand feature X of PyTorch."* If there is a tutorial that you propose to replace, add here. If this is related to another GitHub issue, add a link here.
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validations:
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required: true
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- type: textarea
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attributes:
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label: Existing tutorials on this topic
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description: |
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**Add a list of existing tutorials on the same topic.**
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placeholder: |
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List tutorials that already explain this functionality if exist. On pytorch.org or elsewhere.
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* Link
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* Link
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- type: textarea
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attributes:
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label: Additional context
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description: |
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**Add additional context**
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placeholder: |
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Add any other context or screenshots about the feature request.
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- type: markdown
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attributes:
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value: >
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Thanks for contributing 🎉!

‎.jenkins/get_sphinx_filenames.py

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from pathlib import Path
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from typing import List
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from get_files_to_run import get_all_files
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from validate_tutorials_built import NOT_RUN
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def get_files_for_sphinx() -> List[str]:
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all_py_files = get_all_files()
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return [x for x in all_py_files if all(y not in x for y in NOT_RUN)]
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SPHINX_SHOULD_RUN = "|".join(get_files_for_sphinx())

‎.jenkins/validate_tutorials_built.py

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# the file name to explain why, like intro.html), or fix the tutorial and remove it from this list).
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NOT_RUN = [
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"basics/intro", # no code
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"translation_transformer",
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"profiler",
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"saving_loading_models",
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"introyt/captumyt",
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"examples_nn/polynomial_module",
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"examples_nn/dynamic_net",
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"examples_nn/polynomial_optim",
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"former_torchies/autograd_tutorial_old",
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"former_torchies/tensor_tutorial_old",
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"examples_autograd/polynomial_autograd",
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"examples_autograd/polynomial_custom_function",
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"parametrizations",
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"mnist_train_nas", # used by ax_multiobjective_nas_tutorial.py
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"fx_conv_bn_fuser",
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"super_resolution_with_onnxruntime",
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"ddp_pipeline", # requires 4 gpus
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"fx_graph_mode_ptq_dynamic",
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"vmap_recipe",
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"torchscript_freezing",
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"nestedtensor",
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"recipes/saving_and_loading_models_for_inference",
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"recipes/saving_multiple_models_in_one_file",
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"recipes/loading_data_recipe",
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"recipes/tensorboard_with_pytorch",
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"recipes/what_is_state_dict",
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"recipes/profiler_recipe",
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"recipes/save_load_across_devices",
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"recipes/warmstarting_model_using_parameters_from_a_different_model",
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"torch_compile_tutorial_",
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"recipes/dynamic_quantization",
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"recipes/saving_and_loading_a_general_checkpoint",
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"recipes/benchmark",
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"recipes/tuning_guide",
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"recipes/zeroing_out_gradients",
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"recipes/defining_a_neural_network",
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"recipes/timer_quick_start",
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"recipes/amp_recipe",
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"recipes/Captum_Recipe",
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"flask_rest_api_tutorial",
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"text_to_speech_with_torchaudio",
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"beginner_source/basics/intro", # no code
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"beginner_source/translation_transformer",
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"beginner_source/profiler",
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"beginner_source/saving_loading_models",
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"beginner_source/introyt/captumyt",
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"beginner_source/examples_nn/polynomial_module",
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"beginner_source/examples_nn/dynamic_net",
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"beginner_source/examples_nn/polynomial_optim",
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"beginner_source/former_torchies/autograd_tutorial_old",
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"beginner_source/former_torchies/tensor_tutorial_old",
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"beginner_source/examples_autograd/polynomial_autograd",
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"beginner_source/examples_autograd/polynomial_custom_function",
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"intermediate_source/parametrizations",
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"intermediate_source/mnist_train_nas", # used by ax_multiobjective_nas_tutorial.py
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"intermediate_source/fx_conv_bn_fuser",
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"advanced_source/super_resolution_with_onnxruntime",
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"advanced_source/ddp_pipeline", # requires 4 gpus
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"prototype_source/fx_graph_mode_ptq_dynamic",
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"prototype_source/vmap_recipe",
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"prototype_source/torchscript_freezing",
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"prototype_source/nestedtensor",
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"recipes_source/recipes/saving_and_loading_models_for_inference",
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"recipes_source/recipes/saving_multiple_models_in_one_file",
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"recipes_source/recipes/loading_data_recipe",
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"recipes_source/recipes/tensorboard_with_pytorch",
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"recipes_source/recipes/what_is_state_dict",
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"recipes_source/recipes/profiler_recipe",
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"recipes_source/recipes/save_load_across_devices",
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"recipes_source/recipes/warmstarting_model_using_parameters_from_a_different_model",
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"recipes_source/recipes/dynamic_quantization",
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"recipes_source/recipes/saving_and_loading_a_general_checkpoint",
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"recipes_source/recipes/benchmark",
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"recipes_source/recipes/tuning_guide",
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"recipes_source/recipes/zeroing_out_gradients",
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"recipes_source/recipes/defining_a_neural_network",
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"recipes_source/recipes/timer_quick_start",
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"recipes_source/recipes/amp_recipe",
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"recipes_source/recipes/Captum_Recipe",
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"intermediate_source/flask_rest_api_tutorial",
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"intermediate_source/text_to_speech_with_torchaudio",
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"intermediate_source/ax_multiobjective_nas_tutorial",
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"intermediate_source/tensorboard_profiler_tutorial" # reenable after 2.0 release.
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]
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def tutorial_source_dirs() -> List[Path]:
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return [
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p.relative_to(REPO_ROOT).with_name(p.stem[:-7])
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glob_path = f"{tutorial_source_dir}/**/*.html"
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html_file_paths += docs_dir.glob(glob_path)
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should_not_run = [f'{x.replace("_source", "")}.html' for x in NOT_RUN]
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did_not_run = []
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for html_file_path in html_file_paths:
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with open(html_file_path, "r", encoding="utf-8") as html_file:
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if (
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"Total running time of the script: ( 0 minutes 0.000 seconds)"
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in elem.text
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and not any(
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html_file_path.match(file) for file in NOT_RUN
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)
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and not any(html_file_path.match(file) for file in should_not_run)
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):
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did_not_run.append(html_file_path.as_posix())
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‎_static/img/invpendulum.gif

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‎advanced_source/static_quantization_tutorial.rst

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per_channel_quantized_model = load_model(saved_model_dir + float_model_file)
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per_channel_quantized_model.eval()
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per_channel_quantized_model.fuse_model()
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per_channel_quantized_model.qconfig = torch.ao.quantization.get_default_qconfig('fbgemm')
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# The old 'fbgemm' is still available but 'x86' is the recommended default.
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per_channel_quantized_model.qconfig = torch.ao.quantization.get_default_qconfig('x86')
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print(per_channel_quantized_model.qconfig)
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torch.ao.quantization.prepare(per_channel_quantized_model, inplace=True)
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qat_model = load_model(saved_model_dir + float_model_file)
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qat_model.fuse_model()
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optimizer = torch.optim.SGD(qat_model.parameters(), lr = 0.0001)
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qat_model.qconfig = torch.ao.quantization.get_default_qat_qconfig('fbgemm')
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optimizer = torch.optim.SGD(qat_model.parameters(), lr = 0.0001)
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# The old 'fbgemm' is still available but 'x86' is the recommended default.
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qat_model.qconfig = torch.ao.quantization.get_default_qat_qconfig('x86')
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Finally, ``prepare_qat`` performs the "fake quantization", preparing the model for quantization-aware training
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‎beginner_source/basics/tensorqs_tutorial.py

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######################################################################
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# **Joining tensors** You can use ``torch.cat`` to concatenate a sequence of tensors along a given dimension.
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# See also `torch.stack <https://pytorch.org/docs/stable/generated/torch.stack.html>`__,
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# another tensor joining op that is subtly different from ``torch.cat``.
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# another tensor joining option that is subtly different from ``torch.cat``.
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t1 = torch.cat([tensor, tensor, tensor], dim=1)
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print(t1)
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‎beginner_source/introyt/autogradyt_tutorial.py

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print(model.layer2.weight.grad[0][0:10])
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optimizer.zero_grad()
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optimizer.zero_grad(set_to_none=False)
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print(model.layer2.weight.grad[0][0:10])
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‎beginner_source/new-release-colab.rst

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.. _new-release_colab::
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Notes for Running in Colab
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. note::
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This tutorial requires PyTorch 2.0.0 or later. If you are running this
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in Google Colab, verify that you have the required ``torch`` and
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compatible domain libraties installed by running ``!pip list``.
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If the installed version of PyTorch is lower than required,
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unistall it and reinstall again by running the following commands:
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.. code-block:: python
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!pip3 uninstall --yes torch torchaudio torchvision torchtext torchdata
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!pip3 install torch torchaudio torchvision torchtext torchdata

‎beginner_source/text_sentiment_ngrams_tutorial.py

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def __init__(self, vocab_size, embed_dim, num_class):
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super(TextClassificationModel, self).__init__()
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self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)
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self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=False)
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self.fc = nn.Linear(embed_dim, num_class)
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self.init_weights()
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‎beginner_source/vt_tutorial.py

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# Now run the code below:
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#
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# Use 'fbgemm' for server inference and 'qnnpack' for mobile inference
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backend = "fbgemm" # replaced with qnnpack causing much worse inference speed for quantized model on this notebook
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# Use 'x86' for server inference (the old 'fbgemm' is still available but 'x86' is the recommended default) and 'qnnpack' for mobile inference.
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backend = "x86" # replaced with qnnpack causing much worse inference speed for quantized model on this notebook
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model.qconfig = torch.quantization.get_default_qconfig(backend)
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torch.backends.quantized.engine = backend
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‎conf.py

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from custom_directives import IncludeDirective, GalleryItemDirective, CustomGalleryItemDirective, CustomCalloutItemDirective, CustomCardItemDirective
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import distutils.file_util
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import re
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from validate_tutorials_built import NOT_RUN
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from get_sphinx_filenames import SPHINX_SHOULD_RUN
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import plotly.io as pio
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pio.renderers.default = 'sphinx_gallery'
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intersphinx_mapping = {
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"torch": ("https://pytorch.org/docs/stable/", None),
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"tensordict": ("https://pytorch-labs.github.io/tensordict/", None),
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"torchrl": ("https://pytorch.org/rl/", None),
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"torchaudio": ("https://pytorch.org/audio/stable/", None),
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"torchtext": ("https://pytorch.org/text/stable/", None),
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"torchvision": ("https://pytorch.org/vision/stable/", None),
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'examples_dirs': ['beginner_source', 'intermediate_source',
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'advanced_source', 'recipes_source', 'prototype_source'],
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'gallery_dirs': ['beginner', 'intermediate', 'advanced', 'recipes', 'prototype'],
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'filename_pattern': '.py',
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'ignore_pattern': re.compile(f"({'|'.join(NOT_RUN)}).py$"),
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'filename_pattern': re.compile(SPHINX_SHOULD_RUN),
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'promote_jupyter_magic': True,
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'backreferences_dir': None
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}

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