Add Torch export tutorial

- Add Torch export tutorial (#2557)
- Temporarily pull 2.1 binaries in the stable branch
- Update wordlist
---------

Co-authored-by: Svetlana Karslioglu <svekars@fb.com>
Co-authored-by: William Wen <williamwen@meta.com>

Author: Svetlana Karslioglu

.jenkins/build.sh

@@ -24,11 +24,10 @@ pip install --progress-bar off -r $DIR/../requirements.txt
 
 #Install PyTorch Nightly for test.
 # Nightly - pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu102/torch_nightly.html
-# RC Link
-# pip uninstall -y torch torchvision torchaudio torchtext
-# pip install --pre --upgrade -f https://download.pytorch.org/whl/test/cu102/torch_test.html torch  torchvision torchaudio torchtext
-# pip uninstall -y torch torchvision torchaudio torchtext
-# pip install --pre --upgrade -f https://download.pytorch.org/whl/test/cu116/torch_test.html torch torchdata torchvision torchaudio torchtext
+# Install 2.1 for testing
+pip uninstall -y torch torchvision torchaudio torchtext torchdata
+pip3 install torch torchvision torchaudio --no-cache-dir --index-url https://download.pytorch.org/whl/test/cu121
+pip3 install torchdata torchtext --index-url https://download.pytorch.org/whl/test/cpu
 
 # Install two language tokenizers for Translation with TorchText tutorial
 python -m spacy download en_core_web_sm

en-wordlist.txt

@@ -436,6 +436,7 @@ summarization
 swappable
 tanh
 th
+tensor's
 thresholding
 timestep
 timesteps

intermediate_source/torch_compile_tutorial.py

@@ -11,15 +11,15 @@
 # ``torch.compile`` makes PyTorch code run faster by
 # JIT-compiling PyTorch code into optimized kernels,
 # all while requiring minimal code changes.
-# 
+#
 # In this tutorial, we cover basic ``torch.compile`` usage,
 # and demonstrate the advantages of ``torch.compile`` over
 # previous PyTorch compiler solutions, such as
-# `TorchScript <https://pytorch.org/docs/stable/jit.html>`__ and 
+# `TorchScript <https://pytorch.org/docs/stable/jit.html>`__ and
 # `FX Tracing <https://pytorch.org/docs/stable/fx.html#torch.fx.symbolic_trace>`__.
 #
 # **Contents**
-# 
+#
 # - Basic Usage
 # - Demonstrating Speedups
 # - Comparison to TorchScript and FX Tracing
@@ -59,7 +59,7 @@
 #
 # ``torch.compile`` is included in the latest PyTorch..
 # Running TorchInductor on GPU requires Triton, which is included with the PyTorch 2.0 nightly
-# binary. If Triton is still missing, try installing ``torchtriton`` via pip 
+# binary. If Triton is still missing, try installing ``torchtriton`` via pip
 # (``pip install torchtriton --extra-index-url "https://download.pytorch.org/whl/nightly/cu117"``
 # for CUDA 11.7).
 #
@@ -104,7 +104,7 @@ def forward(self, x):
 # -----------------------
 #
 # Let's now demonstrate that using ``torch.compile`` can speed
-# up real models. We will compare standard eager mode and 
+# up real models. We will compare standard eager mode and
 # ``torch.compile`` by evaluating and training a ``torchvision`` model on random data.
 #
 # Before we start, we need to define some utility functions.
@@ -253,15 +253,15 @@ def train(mod, data):
 ######################################################################
 # Comparison to TorchScript and FX Tracing
 # -----------------------------------------
-# 
+#
 # We have seen that ``torch.compile`` can speed up PyTorch code.
 # Why else should we use ``torch.compile`` over existing PyTorch
 # compiler solutions, such as TorchScript or FX Tracing? Primarily, the
 # advantage of ``torch.compile`` lies in its ability to handle
 # arbitrary Python code with minimal changes to existing code.
 #
 # One case that ``torch.compile`` can handle that other compiler
-# solutions struggle with is data-dependent control flow (the 
+# solutions struggle with is data-dependent control flow (the
 # ``if x.sum() < 0:`` line below).
 
 def f1(x, y):
@@ -399,7 +399,7 @@ def f3(x):
 # `FX graphs <https://pytorch.org/docs/stable/fx.html#torch.fx.Graph>`__, which can
 # then be further optimized. TorchDynamo extracts FX graphs by analyzing Python bytecode
 # during runtime and detecting calls to PyTorch operations.
-# 
+#
 # Normally, TorchInductor, another component of ``torch.compile``,
 # further compiles the FX graphs into optimized kernels,
 # but TorchDynamo allows for different backends to be used. In order to inspect
@@ -463,10 +463,8 @@ def bar(a, b):
 
 # Reset since we are using a different backend.
 torch._dynamo.reset()
-explanation, out_guards, graphs, ops_per_graph, break_reasons, explanation_verbose = torch._dynamo.explain(
-    bar, torch.randn(10), torch.randn(10)
-)
-print(explanation_verbose)
+explain_output = torch._dynamo.explain(bar)(torch.randn(10), torch.randn(10))
+print(explain_output)
 
 ######################################################################
 # In order to maximize speedup, graph breaks should be limited.
@@ -487,16 +485,18 @@ def bar(a, b):
 print(opt_model(generate_data(16)[0]))
 
 ######################################################################
+# <!----TODO: replace this section with a link to the torch.export tutorial when done --->
+#
 # Finally, if we simply want TorchDynamo to output the FX graph for export,
 # we can use ``torch._dynamo.export``. Note that ``torch._dynamo.export``, like
 # ``fullgraph=True``, raises an error if TorchDynamo breaks the graph.
 
 try:
-    torch._dynamo.export(bar, torch.randn(10), torch.randn(10))
+    torch._dynamo.export(bar)(torch.randn(10), torch.randn(10))
 except:
     tb.print_exc()
 
-model_exp = torch._dynamo.export(init_model(), generate_data(16)[0])
+model_exp = torch._dynamo.export(init_model())(generate_data(16)[0])
 print(model_exp[0](generate_data(16)[0]))
 
 ######################################################################