Add compiled autograd tutorial

intermediate_source/compiled_autograd_tutorial.rst

@@ -0,0 +1,302 @@
+Compiled Autograd: Capturing a larger backward graph for ``torch.compile``
+==========================================================================
+**Author:** `Simon Fan <https://github.com/xmfan>`_
+
+.. grid:: 2
+
+    .. grid-item-card:: :octicon:`mortar-board;1em;` What you will learn
+       :class-card: card-prerequisites
+
+       * How compiled autograd interacts with ``torch.compile``
+       * How to use the compiled autograd API
+       * How to inspect logs using ``TORCH_LOGS``
+
+    .. grid-item-card:: :octicon:`list-unordered;1em;` Prerequisites
+       :class-card: card-prerequisites
+
+       * PyTorch 2.4
+       * Complete the `Introduction to torch.compile <https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html>`_
+       * Read through the TorchDynamo and AOTAutograd sections of `Get Started with PyTorch 2.x <https://pytorch.org/get-started/pytorch-2.0/>`_
+
+Overview
+--------
+Compiled Autograd is a ``torch.compile`` extension introduced in PyTorch 2.4
+that allows the capture of a larger backward graph.
+
+While ``torch.compile`` does capture the backward graph, it does so **partially**. The AOTAutograd component captures the backward graph ahead-of-time, with certain limitations:
+
+* Graph breaks in the forward lead to graph breaks in the backward
+* `Backward hooks <https://pytorch.org/docs/stable/notes/autograd.html#backward-hooks-execution>`_ are not captured
+
+Compiled Autograd addresses these limitations by directly integrating with the autograd engine, allowing
+it to capture the full backward graph at runtime. Models with these two characteristics should try
+Compiled Autograd, and potentially observe better performance.
+
+However, Compiled Autograd introduces its own limitations:
+
+* Added runtime overhead at the start of the backward for cache lookup
+* More prone to recompiles and graph breaks in dynamo due to the larger capture
+
+.. note:: Compiled Autograd is under active development and is not yet compatible with all existing PyTorch features. For the latest status on a particular feature, refer to `Compiled Autograd Landing Page <https://docs.google.com/document/d/11VucFBEewzqgkABIjebZIzMvrXr3BtcY1aGKpX61pJY>`_.
+
+Setup
+-----
+In this tutorial, we will base our examples on this simple neural network model.
+It takes a 10-dimensional input vector, processes it through a single linear layer, and outputs another 10-dimensional vector.
+
+.. code:: python
+
+   import torch
+
+   class Model(torch.nn.Module):
+      def __init__(self):
+         super().__init__()
+         self.linear = torch.nn.Linear(10, 10)
+
+      def forward(self, x):
+         return self.linear(x)
+
+Basic usage
+------------
+Before calling the ``torch.compile`` API, make sure to set ``torch._dynamo.config.compiled_autograd`` to ``True``:
+
+.. code:: python
+
+   model = Model()
+   x = torch.randn(10)
+
+   torch._dynamo.config.compiled_autograd = True
+   @torch.compile
+   def train(model, x):
+      loss = model(x).sum()
+      loss.backward()
+
+   train(model, x) 
+
+In the code above, we create an instance of the ``Model`` class and generate a random 10-dimensional tensor ``x`` by using ``torch.randn(10)``.
+We define the training loop function ``train`` and decorate it with @torch.compile to optimize its execution.
+When ``train(model, x)`` is called:
+
+* Python Interpreter calls Dynamo, since this call was decorated with ``@torch.compile``.
+* Dynamo intercepts the Python bytecode, simulates their execution and records the operations into a graph.
+* ``AOTDispatcher`` disables hooks and calls the autograd engine to compute gradients for ``model.linear.weight`` and ``model.linear.bias``, and records the operations into a graph. Using ``torch.autograd.Function``, AOTDispatcher rewrites the forward and backward implementation of ``train``.
+* Inductor generates a function corresponding to an optimized implementation of the AOTDispatcher forward and backward.
+* Dynamo sets the optimized function to be evaluated next by Python Interpreter.
+* Python Interpreter executes the optimized function, which executes ``loss = model(x).sum()``.
+* Python Interpreter executes ``loss.backward()``, calling into the autograd engine, which routes to the Compiled Autograd engine since we set ``torch._dynamo.config.compiled_autograd = True``.
+* Compiled Autograd computes the gradients for ``model.linear.weight`` and ``model.linear.bias``, and records the operations into a graph, including any hooks it encounters. During this process, it will record the backward previously rewritten by AOTDispatcher. Compiled Autograd then generates a new function which corresponds to a fully-traced implementation of ``loss.backward()``, and executes it with ``torch.compile`` in inference mode.
+* The same steps recursively apply to the Compiled Autograd graph, but this time AOTDispatcher will not need to partition the graph.
+
+Inspecting the compiled autograd logs
+-------------------------------------
+Run the script with the ``TORCH_LOGS`` environment variables:
+
+* To only print the compiled autograd graph, use ``TORCH_LOGS="compiled_autograd" python example.py``
+* To print the graph with more tensor metadata and recompile reasons, at the cost of performance, use ``TORCH_LOGS="compiled_autograd_verbose" python example.py``
+
+Rerun the snippet above, the compiled autograd graph should now be logged to ``stderr``. Certain graph nodes will have names that are prefixed by ``aot0_``,
+these correspond to the nodes previously compiled ahead of time in AOTAutograd backward graph 0, for example, ``aot0_view_2`` corresponds to ``view_2`` of the AOT backward graph with id=0.
+
+
+.. code:: python
+
+   stderr_output = """
+   DEBUG:torch._dynamo.compiled_autograd.__compiled_autograd_verbose:Cache miss due to new autograd node: torch::autograd::GraphRoot (NodeCall 0) with key size 39, previous key sizes=[]
+   DEBUG:torch._dynamo.compiled_autograd.__compiled_autograd_verbose:TRACED GRAPH
+   ===== Compiled autograd graph =====
+   <eval_with_key>.4 class CompiledAutograd(torch.nn.Module):
+      def forward(self, inputs, sizes, scalars, hooks):
+         # No stacktrace found for following nodes
+         aot0_tangents_1: "f32[][]cpu" = inputs[0]
+         aot0_primals_3: "f32[10][1]cpu" = inputs[1]
+         getitem_2: "f32[10][1]cpu" = inputs[2]
+         getitem_3: "f32[10, 10][10, 1]cpu" = inputs[3];  inputs = None
+
+            # File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:483 in set_node_origin, code: CompiledFunctionBackward0 (NodeCall 1)
+         aot0_expand: "f32[10][0]cpu" = torch.ops.aten.expand.default(aot0_tangents_1, [10]);  aot0_tangents_1 = None
+         aot0_view_2: "f32[1, 10][0, 0]cpu" = torch.ops.aten.view.default(aot0_expand, [1, 10]);  aot0_expand = None
+         aot0_permute_2: "f32[10, 1][0, 0]cpu" = torch.ops.aten.permute.default(aot0_view_2, [1, 0])
+         aot0_select: "f32[1][0]cpu" = torch.ops.aten.select.int(aot0_permute_2, 0, 0)
+         aot0_view: "f32[1, 10][10, 1]cpu" = torch.ops.aten.view.default(aot0_primals_3, [1, 10]);  aot0_primals_3 = None
+         aot0_mul_3: "f32[1, 10][10, 1]cpu" = torch.ops.aten.mul.Tensor(aot0_select, aot0_view);  aot0_select = None
+         aot0_select_1: "f32[1][0]cpu" = torch.ops.aten.select.int(aot0_permute_2, 0, 1)
+         aot0_mul_4: "f32[1, 10][10, 1]cpu" = torch.ops.aten.mul.Tensor(aot0_select_1, aot0_view);  aot0_select_1 = None
+         aot0_select_2: "f32[1][0]cpu" = torch.ops.aten.select.int(aot0_permute_2, 0, 2)
+         aot0_mul_5: "f32[1, 10][10, 1]cpu" = torch.ops.aten.mul.Tensor(aot0_select_2, aot0_view);  aot0_select_2 = None
+         aot0_select_3: "f32[1][0]cpu" = torch.ops.aten.select.int(aot0_permute_2, 0, 3)
+         aot0_mul_6: "f32[1, 10][10, 1]cpu" = torch.ops.aten.mul.Tensor(aot0_select_3, aot0_view);  aot0_select_3 = None
+         aot0_select_4: "f32[1][0]cpu" = torch.ops.aten.select.int(aot0_permute_2, 0, 4)
+         aot0_mul_7: "f32[1, 10][10, 1]cpu" = torch.ops.aten.mul.Tensor(aot0_select_4, aot0_view);  aot0_select_4 = None
+         aot0_select_5: "f32[1][0]cpu" = torch.ops.aten.select.int(aot0_permute_2, 0, 5)
+         aot0_mul_8: "f32[1, 10][10, 1]cpu" = torch.ops.aten.mul.Tensor(aot0_select_5, aot0_view);  aot0_select_5 = None
+         aot0_select_6: "f32[1][0]cpu" = torch.ops.aten.select.int(aot0_permute_2, 0, 6)
+         aot0_mul_9: "f32[1, 10][10, 1]cpu" = torch.ops.aten.mul.Tensor(aot0_select_6, aot0_view);  aot0_select_6 = None
+         aot0_select_7: "f32[1][0]cpu" = torch.ops.aten.select.int(aot0_permute_2, 0, 7)
+         aot0_mul_10: "f32[1, 10][10, 1]cpu" = torch.ops.aten.mul.Tensor(aot0_select_7, aot0_view);  aot0_select_7 = None
+         aot0_select_8: "f32[1][0]cpu" = torch.ops.aten.select.int(aot0_permute_2, 0, 8)
+         aot0_mul_11: "f32[1, 10][10, 1]cpu" = torch.ops.aten.mul.Tensor(aot0_select_8, aot0_view);  aot0_select_8 = None
+         aot0_select_9: "f32[1][0]cpu" = torch.ops.aten.select.int(aot0_permute_2, 0, 9);  aot0_permute_2 = None
+         aot0_mul_12: "f32[1, 10][10, 1]cpu" = torch.ops.aten.mul.Tensor(aot0_select_9, aot0_view);  aot0_select_9 = aot0_view = None
+         aot0_cat: "f32[10, 10][10, 1]cpu" = torch.ops.aten.cat.default([aot0_mul_3, aot0_mul_4, aot0_mul_5, aot0_mul_6, aot0_mul_7, aot0_mul_8, aot0_mul_9, aot0_mul_10, aot0_mul_11, aot0_mul_12]);  aot0_mul_3 = aot0_mul_4 = aot0_mul_5 = aot0_mul_6 = aot0_mul_7 = aot0_mul_8 = aot0_mul_9 = aot0_mul_10 = aot0_mul_11 = aot0_mul_12 = None
+         aot0_permute_3: "f32[10, 10][1, 10]cpu" = torch.ops.aten.permute.default(aot0_cat, [1, 0]);  aot0_cat = None
+         aot0_sum_3: "f32[1, 10][10, 1]cpu" = torch.ops.aten.sum.dim_IntList(aot0_view_2, [0], True);  aot0_view_2 = None
+         aot0_view_3: "f32[10][1]cpu" = torch.ops.aten.view.default(aot0_sum_3, [10]);  aot0_sum_3 = None
+
+            # File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:483 in set_node_origin, code: torch::autograd::AccumulateGrad (NodeCall 2)
+         accumulate_grad_ = torch.ops.inductor.accumulate_grad_.default(getitem_2, aot0_view_3);  getitem_2 = aot0_view_3 = accumulate_grad_ = None
+
+            # File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:483 in set_node_origin, code: CompiledFunctionBackward0 (NodeCall 1)
+         aot0_permute_4: "f32[10, 10][10, 1]cpu" = torch.ops.aten.permute.default(aot0_permute_3, [1, 0]);  aot0_permute_3 = None
+
+            # File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:483 in set_node_origin, code: torch::autograd::AccumulateGrad (NodeCall 3)
+         accumulate_grad__1 = torch.ops.inductor.accumulate_grad_.default(getitem_3, aot0_permute_4);  getitem_3 = aot0_permute_4 = accumulate_grad__1 = None
+         _exec_final_callbacks_stub = torch__dynamo_external_utils__exec_final_callbacks_stub();  _exec_final_callbacks_stub = None
+         return []
+   """
+
+.. note:: This is the graph on which we will call ``torch.compile``, **NOT** the optimized graph. Compiled Autograd essentially generates some unoptimized Python code to represent the entire C++ autograd execution.
+
+Compiling the forward and backward pass using different flags
+-------------------------------------------------------------
+You can use different compiler configs for the two compilations, for example, the backward may be a fullgraph even if there are graph breaks in the forward.
+
+.. code:: python
+
+def train(model, x):
+    model = torch.compile(model)
+    loss = model(x).sum()
+    torch._dynamo.config.compiled_autograd = True
+    torch.compile(lambda: loss.backward(), fullgraph=True)()
+
+Or you can use the context manager, which will apply to all autograd calls within its scope.
+
+.. code:: python
+
+   def train(model, x):
+      model = torch.compile(model)
+      loss = model(x).sum()
+      with torch._dynamo.compiled_autograd.enable(torch.compile(fullgraph=True)):
+         loss.backward()
+
+
+Compiled Autograd addresses certain limitations of AOTAutograd
+--------------------------------------------------------------
+1. Graph breaks in the forward pass lead to graph breaks in the backward pass:
+
+.. code:: python
+
+   @torch.compile(backend="aot_eager")
+   def fn(x):
+      # 1st graph
+      temp = x + 10
+      torch._dynamo.graph_break()
+      # 2nd graph
+      temp = temp + 10
+      torch._dynamo.graph_break()
+      # 3rd graph
+      return temp.sum()
+
+   x = torch.randn(10, 10, requires_grad=True)
+   torch._dynamo.utils.counters.clear()
+   loss = fn(x)
+
+   # 1. base torch.compile 
+   loss.backward(retain_graph=True)
+   assert(torch._dynamo.utils.counters["stats"]["unique_graphs"] == 3)
+   torch._dynamo.utils.counters.clear()
+
+   # 2. torch.compile with compiled autograd
+   with torch._dynamo.compiled_autograd.enable(torch.compile(backend="aot_eager")):
+      loss.backward()
+
+   # single graph for the backward
+   assert(torch._dynamo.utils.counters["stats"]["unique_graphs"] == 1)
+
+
+In the ``1. base torch.compile`` case, we see that 3 backward graphs were produced due to the 2 graph breaks in the compiled function ``fn``. 
+Whereas in ``2. torch.compile with compiled autograd``, we see that a full backward graph was traced despite the graph breaks.
+
+2. Backward hooks are not captured
+
+.. code:: python
+
+   @torch.compile(backend="aot_eager")
+   def fn(x):
+      return x.sum()
+
+   x = torch.randn(10, 10, requires_grad=True)
+   x.register_hook(lambda grad: grad+10)
+   loss = fn(x)
+
+   with torch._dynamo.compiled_autograd.enable(torch.compile(backend="aot_eager")):
+      loss.backward()
+
+There should be a ``call_hook`` node in the graph, which dynamo will later inline into
+
+.. code:: python
+
+   stderr_output = """
+   DEBUG:torch._dynamo.compiled_autograd.__compiled_autograd_verbose:Cache miss due to new autograd node: torch::autograd::GraphRoot (NodeCall 0) with key size 39, previous key sizes=[]
+   DEBUG:torch._dynamo.compiled_autograd.__compiled_autograd_verbose:TRACED GRAPH
+   ===== Compiled autograd graph =====
+   <eval_with_key>.2 class CompiledAutograd(torch.nn.Module):
+      def forward(self, inputs, sizes, scalars, hooks):
+         ...
+         getitem_2 = hooks[0];  hooks = None
+         call_hook: "f32[10, 10][0, 0]cpu" = torch__dynamo_external_utils_call_hook(getitem_2, aot0_expand, hook_type = 'tensor_pre_hook');  getitem_2 = aot0_expand = None
+         ...
+   """
+
+Common recompilation reasons for Compiled Autograd
+--------------------------------------------------
+1. Due to changes in the autograd structure of the loss value
+
+.. code:: python
+
+   torch._dynamo.config.compiled_autograd = True
+   x = torch.randn(10, requires_grad=True)
+   for op in [torch.add, torch.sub, torch.mul, torch.div]:
+      loss = op(x, x).sum()
+      torch.compile(lambda: loss.backward(), backend="eager")()
+
+In the example above, we call a different operator on each iteration, leading to ``loss`` tracking a different autograd history each time. You should see some recompile messages: **Cache miss due to new autograd node**.
+
+.. code:: python
+
+   stderr_output = """
+   Cache miss due to new autograd node: torch::autograd::GraphRoot (NodeCall 0) with key size 39, previous key sizes=[] 
+   ...
+   Cache miss due to new autograd node: SubBackward0 (NodeCall 2) with key size 56, previous key sizes=[]
+   ...
+   Cache miss due to new autograd node: MulBackward0 (NodeCall 2) with key size 71, previous key sizes=[]
+   ...
+   Cache miss due to new autograd node: DivBackward0 (NodeCall 2) with key size 70, previous key sizes=[]
+   ...
+   """
+
+2. Due to tensors changing shapes
+
+.. code:: python
+
+   torch._dynamo.config.compiled_autograd = True
+   for i in [10, 100, 10]:
+      x = torch.randn(i, i, requires_grad=True)
+      loss = x.sum()
+      torch.compile(lambda: loss.backward(), backend="eager")()
+
+In the example above, ``x`` changes shapes, and compiled autograd will mark ``x`` as a dynamic shape tensor after the first change. You should see recompiles messages: **Cache miss due to changed shapes**.
+
+.. code:: python
+
+   stderr_output = """
+   ...
+   Cache miss due to changed shapes: marking size idx 0 of torch::autograd::GraphRoot (NodeCall 0) as dynamic
+   Cache miss due to changed shapes: marking size idx 1 of torch::autograd::AccumulateGrad (NodeCall 2) as dynamic
+   Cache miss due to changed shapes: marking size idx 2 of torch::autograd::AccumulateGrad (NodeCall 2) as dynamic
+   Cache miss due to changed shapes: marking size idx 3 of torch::autograd::AccumulateGrad (NodeCall 2) as dynamic
+   ...
+   """
+
+Conclusion
+----------
+In this tutorial, we went over the high-level ecosystem of ``torch.compile`` with compiled autograd, the basics of compiled autograd and a few common recompilation reasons. Stay tuned for deep dives on `dev-discuss <https://dev-discuss.pytorch.org/>`_.