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‎_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb‎

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‎_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb‎

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‎_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb‎

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‎_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb‎

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‎_downloads/822c89760620e7273256cb7f63647167/torch_compile_torch_function_modes.py‎

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"""
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(beta) Utilizing Torch Function modes with torch.compile
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============================================================
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**Author:** `Michael Lazos <https://github.com/mlazos>`_
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"""
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#########################################################
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# This recipe covers how to use a key torch extensibility point,
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# torch function modes, in tandem with ``torch.compile`` to override
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# the behavior of torch operators, also know as **ops**, at trace time, with no runtime overhead.
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#
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# .. note::
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#
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# This recipe requires PyTorch 2.7.0 or later.
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#####################################################################
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# Rewriting a torch op (torch.add -> torch.mul)
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# For this example, we'll use torch function modes to rewrite occurences
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# of addition with multiply instead. This type of override can be common
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# if a certain backend has a custom implementation that should be dispatched
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# for a given op.
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import torch
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# exit cleanly if we are on a device that doesn't support ``torch.compile``
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if torch.cuda.get_device_capability() < (7, 0):
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print("Exiting because torch.compile is not supported on this device.")
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import sys
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sys.exit(0)
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from torch.overrides import BaseTorchFunctionMode
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# Define our mode, Note: ``BaseTorchFunctionMode``
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# implements the actual invocation of func(..)
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class AddToMultiplyMode(BaseTorchFunctionMode):
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def __torch_function__(self, func, types, args=(), kwargs=None):
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if func == torch.Tensor.add:
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func = torch.mul
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return super().__torch_function__(func, types, args, kwargs)
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@torch.compile()
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def test_fn(x, y):
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return x + y * x # Note: infix operators map to torch.Tensor.* methods
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x = torch.rand(2, 2)
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y = torch.rand_like(x)
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with AddToMultiplyMode():
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z = test_fn(x, y)
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assert torch.allclose(z, x * y * x)
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# The mode can also be used within the compiled region as well like this:
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@torch.compile()
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def test_fn(x, y):
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with AddToMultiplyMode():
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return x + y * x # Note: infix operators map to torch.Tensor.* methods
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x = torch.rand(2, 2)
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y = torch.rand_like(x)
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z = test_fn(x, y)
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assert torch.allclose(z, x * y * x)
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######################################################################
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# Conclusion
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# ~~~~~~~~~~
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# In this recipe we demonstrated how to override the behavior of ``torch.*`` operators
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# using torch function modes from within ``torch.compile``. This enables users to utilize
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# the extensibility benefits of torch function modes without the runtime overhead
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# of calling torch function on every op invocation.
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#
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# * See `Extending Torch API with Modes <https://pytorch.org/docs/stable/notes/extending.html#extending-all-torch-api-with-modes>`__ for other examples and background on Torch Function modes.

‎_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb‎

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‎_downloads/d036a4d61563d6157e333f7e5b20b091/torch_compile_torch_function_modes.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# For tips on running notebooks in Google Colab, see\n",
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"# https://pytorch.org/tutorials/beginner/colab\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"(beta) Utilizing Torch Function modes with torch.compile\n",
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"========================================================\n",
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"\n",
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"**Author:** [Michael Lazos](https://github.com/mlazos)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This recipe covers how to use a key torch extensibility point,\n",
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"\n",
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": torch function modes, in tandem with `torch.compile` to override the\n",
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" behavior of torch operators, also know as **ops**, at trace time,\n",
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" with no runtime overhead.\n",
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"\n",
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"<div style=\"background-color: #54c7ec; color: #fff; font-weight: 700; padding-left: 10px; padding-top: 5px; padding-bottom: 5px\"><strong>NOTE:</strong></div>\n",
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"\n",
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"<div style=\"background-color: #f3f4f7; padding-left: 10px; padding-top: 10px; padding-bottom: 10px; padding-right: 10px\">\n",
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"\n",
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"<p>This recipe requires PyTorch 2.7.0 or later.</p>\n",
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"\n",
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"</div>\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Rewriting a torch op (torch.add -\\> torch.mul)\n",
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"==============================================\n",
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"\n",
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"For this example, we\\'ll use torch function modes to rewrite occurences\n",
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"of addition with multiply instead. This type of override can be common\n",
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"if a certain backend has a custom implementation that should be\n",
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"dispatched for a given op.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"\n",
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"# exit cleanly if we are on a device that doesn't support ``torch.compile``\n",
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"if torch.cuda.get_device_capability() < (7, 0):\n",
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" print(\"Exiting because torch.compile is not supported on this device.\")\n",
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" import sys\n",
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" sys.exit(0)\n",
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"\n",
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"from torch.overrides import BaseTorchFunctionMode\n",
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"\n",
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"# Define our mode, Note: ``BaseTorchFunctionMode``\n",
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"# implements the actual invocation of func(..)\n",
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"class AddToMultiplyMode(BaseTorchFunctionMode):\n",
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" def __torch_function__(self, func, types, args=(), kwargs=None):\n",
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" if func == torch.Tensor.add:\n",
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" func = torch.mul\n",
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"\n",
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" return super().__torch_function__(func, types, args, kwargs)\n",
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"\n",
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"@torch.compile()\n",
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"def test_fn(x, y):\n",
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" return x + y * x # Note: infix operators map to torch.Tensor.* methods\n",
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"\n",
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"x = torch.rand(2, 2)\n",
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"y = torch.rand_like(x)\n",
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"\n",
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"with AddToMultiplyMode():\n",
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" z = test_fn(x, y)\n",
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"\n",
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"assert torch.allclose(z, x * y * x)\n",
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"\n",
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"# The mode can also be used within the compiled region as well like this:\n",
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"\n",
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"@torch.compile()\n",
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"def test_fn(x, y):\n",
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" with AddToMultiplyMode():\n",
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" return x + y * x # Note: infix operators map to torch.Tensor.* methods\n",
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"\n",
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"x = torch.rand(2, 2)\n",
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"y = torch.rand_like(x)\n",
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"z = test_fn(x, y)\n",
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"\n",
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"assert torch.allclose(z, x * y * x)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Conclusion\n",
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"==========\n",
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"\n",
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"In this recipe we demonstrated how to override the behavior of `torch.*`\n",
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"operators using torch function modes from within `torch.compile`. This\n",
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"enables users to utilize the extensibility benefits of torch function\n",
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"modes without the runtime overhead of calling torch function on every op\n",
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"invocation.\n",
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"\n",
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"- See [Extending Torch API with\n",
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" Modes](https://pytorch.org/docs/stable/notes/extending.html#extending-all-torch-api-with-modes)\n",
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" for other examples and background on Torch Function modes.\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

‎_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb‎

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‎_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb‎

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‎_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb‎

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