Merge branch 'main' into quant-fixes

Author: svekars

_static/img/compiled_autograd/call_hook_node.png

@@ -97,62 +97,10 @@ Run the script with the ``TORCH_LOGS`` environment variables:
 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.
 
+In the image below, the red box encapsulates the AOT backward graph that is captured by ``torch.compile`` without Compiled Autograd.
 
-.. 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 []
-   """
+.. image:: ../_static/img/compiled_autograd/entire_verbose_log.png
 
 .. 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.
 
@@ -181,7 +129,7 @@ Or you can use the context manager, which will apply to all autograd calls withi
 
 Compiled Autograd addresses certain limitations of AOTAutograd
 --------------------------------------------------------------
-1. Graph breaks in the forward pass lead to graph breaks in the backward pass:
+1. Graph breaks in the forward pass no longer necessarily lead to graph breaks in the backward pass:
 
 .. code:: python
 
@@ -216,7 +164,10 @@ Compiled Autograd addresses certain limitations of AOTAutograd
 In the first ``torch.compile`` case, we see that 3 backward graphs were produced due to the 2 graph breaks in the compiled function ``fn``. 
 Whereas in the second ``torch.compile`` with compiled autograd case, we see that a full backward graph was traced despite the graph breaks.
 
-2. Backward hooks are not captured
+.. note:: It is still possible for the Dynamo to graph break when tracing backward hooks captured by Compiled Autograd.
+
+
+2. Backward hooks can now be captured
 
 .. code:: python
 
@@ -233,19 +184,7 @@ Whereas in the second ``torch.compile`` with compiled autograd case, we see that
 
 There should be a ``call_hook`` node in the graph, which dynamo will later inline into the following:
 
-.. 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
-         ...
-   """
+.. image:: ../_static/img/compiled_autograd/call_hook_node.png
 
 Common recompilation reasons for Compiled Autograd
 --------------------------------------------------
@@ -261,18 +200,7 @@ Common recompilation reasons for Compiled Autograd
 
 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=[]
-   ...
-   """
+.. image:: ../_static/img/compiled_autograd/recompile_due_to_node.png
 
 2. Due to tensors changing shapes:
 
@@ -286,16 +214,7 @@ In the example above, we call a different operator on each iteration, leading to
 
 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
-   ...
-   """
+.. image:: ../_static/img/compiled_autograd/recompile_due_to_dynamic.png
 
 Conclusion
 ----------