We define a functional version of a C++ torch::autograd::Function. The
functional version reconstructs the ctx object and then calls
backward with it.
Some more details:
- we define how to pack/unpack ctx.saved_data into an IValue. It's a
Dict[str, IValue], so it wasn't difficult.
- every call to CppNode::apply_with_saved binds a new function to
Python. This is because we're unable to reuse the a previously bound
function for reasons (the schema may change depending on what the user
actually puts into their Dict[str, IValue]).
Test Plan:
- existing tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143387
Approved by: https://github.com/jansel, https://github.com/xmfan
ghstack dependencies: #143296, #143304
- Adds support for custom ops backed by c++ custom autograd functions, e.g. fbgemm
- Include files more granularly to avoid namespace pollution and circular imports
limitations:
- requires user to audit their code and opt-in their custom autograd::Function via autograd::Function::is_traceable and maybe additional compiled_args + apply_with_saved implementation. this was the only way I can think of for soundness
- will throw if we can't hash the saved_data i.e. for any non implemented type other than list and dict in at::IValue::hash b0cfa96e82/aten/src/ATen/core/ivalue.cpp (L364)
- can technically silently fail if both the typeid hash and the typeid string name of the custom autograd::Function collide at the same time, and an identical autograd graph containing a different custom autograd::Function, yet that has an identical implementation, is called. this case seems extremely unlikely, and the only alternative to hash collision i can think of is compiling with reflection
- tensors not saved via save_variables are not lifted, and are specialized on TensorImpl*'s hash (treated as a memory address). if needed, we can lift them.
Differential Revision: [D54818488](https://our.internmc.facebook.com/intern/diff/D54818488)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120681
Approved by: https://github.com/jansel
- Adds support for custom ops backed by c++ custom autograd functions, e.g. fbgemm
- Include files more granularly to avoid namespace pollution and circular imports
limitations:
- requires user to audit their code and opt-in their custom autograd::Function via autograd::Function::is_traceable and maybe additional compiled_args + apply_with_saved implementation. this was the only way I can think of for soundness
- will throw if we can't hash the saved_data i.e. for any non implemented type other than list and dict in at::IValue::hash b0cfa96e82/aten/src/ATen/core/ivalue.cpp (L364)
- can technically silently fail if both the typeid hash and the typeid string name of the custom autograd::Function collide at the same time, and an identical autograd graph containing a different custom autograd::Function, yet that has an identical implementation, is called. this case seems extremely unlikely, and the only alternative to hash collision i can think of is compiling with reflection
- tensors not saved via save_variables are not lifted, and are specialized on TensorImpl*'s hash (treated as a memory address). if needed, we can lift them.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120681
Approved by: https://github.com/jansel
- Adds support for custom ops backed by c++ custom autograd functions, e.g. fbgemm
- Include files more granularly to avoid namespace pollution and circular imports
limitations:
- requires user to audit their code and opt-in their custom autograd::Function via autograd::Function::is_traceable and maybe additional compiled_args + apply_with_saved implementation. this was the only way I can think of for soundness
- will throw if we can't hash the saved_data i.e. for any non implemented type other than list and dict in at::IValue::hash b0cfa96e82/aten/src/ATen/core/ivalue.cpp (L364)
- can technically silently fail if both the typeid hash and the typeid string name of the custom autograd::Function collide at the same time, and an identical autograd graph containing a different custom autograd::Function, yet that has an identical implementation, is called. this case seems extremely unlikely, and the only alternative to hash collision i can think of is compiling with reflection
- tensors not saved via save_variables are not lifted, and are specialized on TensorImpl*'s hash (treated as a memory address). if needed, we can lift them.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120681
Approved by: https://github.com/jansel
VariableInfo is used by both `custom_function.h` (in a templated class) and `compiled_autograd.h` (in a class with some templated methods). Another way could have been to make a `compiled_autograd.cpp` and forward declare VariableInfo, but this VariableInfo was also being used in other nodes like PyNode so it felt cleaner to do it this way.
Differential Revision: [D54287007](https://our.internmc.facebook.com/intern/diff/D54287007)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120732
Approved by: https://github.com/jansel
Fixes https://github.com/pytorch/pytorch/issues/96887
We error out in BOTH the case when graph is created and when it is not created.
Still bc-breaking, but not as severe because we are limiting to the case where someone uses setup_context.
This makes setup_context and non-setup_context versions diverge in their behavior
- With the non-setup_context version, saved variables are assumed to have the grad_fn of the inputs.
- But now with the setup_context version, we produce an error for this case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97212
Approved by: https://github.com/zou3519
