Automated tutorials push

Author: pytorchbot

_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b622bb9f",
+   "id": "4779a3f6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c95d87b3",
+   "id": "c5f807f2",
    "metadata": {},
    "source": [
     "\n",

_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb

@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "decaa202",
+   "id": "26c612f6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bcd3c6bb",
+   "id": "9fce16f1",
    "metadata": {},
    "source": [
     "\n",

_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7c5b6aea",
+   "id": "c3439dbe",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e063424",
+   "id": "cdc0c835",
    "metadata": {},
    "source": [
     "\n",

_downloads/6b019e0b5f84b568fcca1120bd28e230/torch_compile_tutorial.py

@@ -101,8 +101,11 @@ def forward(self, x):
         return torch.nn.functional.relu(self.lin(x))
 
 mod = MyModule()
-opt_mod = torch.compile(mod)
-print(opt_mod(t))
+mod.compile()
+print(mod(t))
+## or:
+# opt_mod = torch.compile(mod)
+# print(opt_mod(t))
 
 ######################################################################
 # torch.compile and Nested Calls
@@ -135,8 +138,8 @@ def forward(self, x):
         return torch.nn.functional.relu(self.outer_lin(x))
 
 outer_mod = OuterModule()
-opt_outer_mod = torch.compile(outer_mod)
-print(opt_outer_mod(t))
+outer_mod.compile()
+print(outer_mod(t))
 
 ######################################################################
 # We can also disable some functions from being compiled by using
@@ -197,6 +200,12 @@ 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

_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9a5bf02f",
+   "id": "14152759",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cb12d848",
+   "id": "55289e43",
    "metadata": {},
    "source": [
     "\n",

_downloads/96ad88eb476f41a5403dcdade086afb8/torch_compile_tutorial.ipynb

@@ -173,8 +173,11 @@
     "        return torch.nn.functional.relu(self.lin(x))\n",
     "\n",
     "mod = MyModule()\n",
-    "opt_mod = torch.compile(mod)\n",
-    "print(opt_mod(t))"
+    "mod.compile()\n",
+    "print(mod(t))\n",
+    "## or:\n",
+    "# opt_mod = torch.compile(mod)\n",
+    "# print(opt_mod(t))"
    ]
   },
   {
@@ -235,8 +238,8 @@
     "        return torch.nn.functional.relu(self.outer_lin(x))\n",
     "\n",
     "outer_mod = OuterModule()\n",
-    "opt_outer_mod = torch.compile(outer_mod)\n",
-    "print(opt_outer_mod(t))"
+    "outer_mod.compile()\n",
+    "print(outer_mod(t))"
    ]
   },
   {
@@ -317,7 +320,14 @@
     "nested functions and modules, start by compiling the leaf functions or\n",
     "modules first. For more information see [TorchDynamo APIs for\n",
     "fine-grained\n",
-    "tracing](https://pytorch.org/docs/stable/torch.compiler_fine_grain_apis.html).\n"
+    "tracing](https://pytorch.org/docs/stable/torch.compiler_fine_grain_apis.html).\n",
+    "\n",
+    "5.  **Prefer \\`\\`mod.compile()\\`\\` over \\`\\`torch.compile(mod)\\`\\`:**\n",
+    "    Avoids `_orig_` prefix issues in `state_dict`.\n",
+    "\n",
+    "6\\. **Use \\`\\`fullgraph=True\\`\\` to catch graph breaks:** Helps ensure\n",
+    "end-to-end compilation, maximizing speedup and compatibility with\n",
+    "`torch.export`.\n"
    ]
   },
   {

_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb

@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1ae6a4ec",
+   "id": "78ac4491",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "daa092f9",
+   "id": "9aa1599c",
    "metadata": {},
    "source": [
     "\n",

_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bf84aab6",
+   "id": "744039b5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a310319",
+   "id": "5974d4a8",
    "metadata": {},
    "source": [
     "\n",

_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "33368c0f",
+   "id": "7d278b80",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "413ad8be",
+   "id": "93f682a5",
    "metadata": {},
    "source": [
     "\n",

_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d2c5b942",
+   "id": "58c85a0a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b2a87aa5",
+   "id": "e275cb61",
    "metadata": {},
    "source": [
     "\n",

_images/sphx_glr_char_rnn_classification_tutorial_001.png

@@ -517,9 +517,9 @@ models run single threaded.
  .. code-block:: none
 
     loss: 5.167
-    elapsed time (seconds): 201.7
+    elapsed time (seconds): 197.8
     loss: 5.168
-    elapsed time (seconds): 118.0
+    elapsed time (seconds): 118.4
 
 
 
@@ -541,7 +541,7 @@ Thanks for reading! As always, we welcome any feedback, so please create an issu
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  28.935 seconds)
+   **Total running time of the script:** ( 5 minutes  25.477 seconds)
 
 
 .. _sphx_glr_download_advanced_dynamic_quantization_tutorial.py:

_images/sphx_glr_char_rnn_classification_tutorial_002.png

@@ -410,20 +410,22 @@ network to evaluation mode using ``.eval()``.
     Downloading: "https://download.pytorch.org/models/vgg19-dcbb9e9d.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth
 
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@@ -744,22 +746,22 @@ Finally, we can run the algorithm.
 
     Optimizing..
     run [50]:
-    Style Loss : 4.186186 Content Loss: 4.187174
+    Style Loss : 4.286120 Content Loss: 4.196964
 
     run [100]:
-    Style Loss : 1.130905 Content Loss: 3.028577
+    Style Loss : 1.156173 Content Loss: 3.044555
 
     run [150]:
-    Style Loss : 0.713207 Content Loss: 2.648742
+    Style Loss : 0.709581 Content Loss: 2.651251
 
     run [200]:
-    Style Loss : 0.486411 Content Loss: 2.495289
+    Style Loss : 0.473631 Content Loss: 2.488267
 
     run [250]:
-    Style Loss : 0.346993 Content Loss: 2.406199
+    Style Loss : 0.346411 Content Loss: 2.402201
 
     run [300]:
-    Style Loss : 0.267672 Content Loss: 2.350107
+    Style Loss : 0.264236 Content Loss: 2.346521
 
 
 
@@ -768,7 +770,7 @@ Finally, we can run the algorithm.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  11.353 seconds)
+   **Total running time of the script:** ( 0 minutes  12.000 seconds)
 
 
 .. _sphx_glr_download_advanced_neural_style_tutorial.py:

_images/sphx_glr_neural_style_tutorial_004.png

@@ -303,7 +303,7 @@ The backward pass computes the gradient ``wrt`` the input and the gradient ``wrt
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.366 seconds)
+   **Total running time of the script:** ( 0 minutes  0.371 seconds)
 
 
 .. _sphx_glr_download_advanced_numpy_extensions_tutorial.py:

_images/sphx_glr_reinforcement_q_learning_001.png

@@ -487,7 +487,7 @@ For more detailed information, see:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  2.402 seconds)
+   **Total running time of the script:** ( 0 minutes  2.370 seconds)
 
 
 .. _sphx_glr_download_advanced_python_custom_ops.py:

_images/sphx_glr_spatial_transformer_tutorial_001.png

@@ -70,7 +70,7 @@ model from C++.
 
     2.7.0+cu126
 
-    <torch._C.Generator object at 0x7f5ee6596470>
+    <torch._C.Generator object at 0x7f3bbbd9a470>
 
 
 
@@ -808,7 +808,7 @@ https://colab.research.google.com/drive/1HiICg6jRkBnr5hvK2-VnMi88Vi9pUzEJ
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.150 seconds)
+   **Total running time of the script:** ( 0 minutes  0.147 seconds)
 
 
 .. _sphx_glr_download_beginner_Intro_to_TorchScript_tutorial.py:

_images/sphx_glr_torchvision_tutorial_002.png

@@ -113,8 +113,8 @@ documentation <https://pytorch.org/docs/stable/autograd.html#function>`__.
 
  .. code-block:: none
 
-    Gradient function for z = <AddBackward0 object at 0x7f5842ad19c0>
-    Gradient function for loss = <BinaryCrossEntropyWithLogitsBackward0 object at 0x7f5842ad0610>
+    Gradient function for z = <AddBackward0 object at 0x7f4c13fa22c0>
+    Gradient function for loss = <BinaryCrossEntropyWithLogitsBackward0 object at 0x7f4c13fa2f80>
 
 
 
@@ -395,7 +395,7 @@ Further Reading
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.009 seconds)
+   **Total running time of the script:** ( 0 minutes  0.008 seconds)
 
 
 .. _sphx_glr_download_beginner_basics_autogradqs_tutorial.py:

_sources/advanced/dynamic_quantization_tutorial.rst.txt

@@ -475,7 +475,7 @@ Further Reading
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.493 seconds)
+   **Total running time of the script:** ( 0 minutes  0.514 seconds)
 
 
 .. _sphx_glr_download_beginner_basics_buildmodel_tutorial.py: