This PR changes compiled autograd's handling of gradient accumulation, by proxying it as a `call_accumulate_grad`, which does the .grad mutation in python bytecode for dynamo to see. For eager, the only change is the leaf invariant check was moved up.
Before:
- Compiled Autograd Engine: proxies call to inductor accumulate_grad op
- Dynamo: polyfills the inductor accumulate_grad op (not respecting all of the accumulateGrad implementation e.g. sparse, gradient layout contract)
```python
new_grad_strided: "f32[s21]" = torch.empty_like(getitem_1); getitem_1 = None
copy_: "f32[s21]" = new_grad_strided.copy_(aot3_tangents_1); copy_ = None
```
- AOTAutograd: functionalizes the copy_
After:
- Compiled Autograd Engine: proxies call to `call_accumulate_grad`, which calls `torch._dynamo.compiled_autograd.ops.AccumulateGrad`/`AccumulateGrad_apply_functional_no_hooks_ivalue`, similar to other functional autograd implementations, but also sets .grad from python. Hooks are still handled separately from this call.
- Dynamo: `torch._dynamo.compiled_autograd.ops.AccumulateGrad` was allow_in_graph'd
- AOTAutograd: traces into the op, with FunctionalTensors.
While functionalizing the tensors, we insert an autograd Error node to ensure that we don't use the autograd meta from tracing. This clashes with the "leaf variable has been moved into the graph interior" error check, I could not find a way to identify a FunctionalTensor subclass from C++, so I bypass that for Error nodes in the compiled case.
In the CI PR, this fixes 19 tests relating to sparse tensors, and more are hidden by an earlier failure in dynamo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155521
Approved by: https://github.com/jansel
Adds create_graph support if you don't compile or compile only with torch.compile(backend="eager").
Using a backend that uses AOTDispatch produces a post-dispatch AOT backward, where its double backward will be silently incorrect if the forward trace involved any ops that are not composite implicit.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153222
Approved by: https://github.com/jansel
ghstack dependencies: #153193
we use dummy tensors in our initial trace, so we should never inline. the subclass dispatch might not support the dummy tensor, e.g. DTensor accumulate grad will check that both param and grad are DTensors
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149014
Approved by: https://github.com/jansel
ghstack dependencies: #149064
i'm changing CA initial trace to always trace as dynamic, fixes these errors:
```python
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
FAILED [0.2139s] test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_autograd_python_custom_function_inplace - RuntimeError: !has_symbolic_sizes_strides_ INTERNAL ASSERT FAILED at "/home/xmfan/core/a/pytorch/aten/src/ATen/TensorGeometry.h":63, please report a bug to PyTorch.
To execute this test, run the following from the base repo dir:
python test/test_autograd.py TestAutogradWithCompiledAutograd.test_autograd_python_custom_function_inplace
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
FAILED [0.0057s] test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_copy_slices_graph_task_updates - RuntimeError: !has_symbolic_sizes_strides_ INTERNAL ASSERT FAILED at "/home/xmfan/core/a/pytorch/aten/src/ATen/TensorGeometry.h":63, please report a bug to PyTorch.
To execute this test, run the following from the base repo dir:
python test/test_autograd.py TestAutogradWithCompiledAutograd.test_copy_slices_graph_task_updates
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
FAILED [0.9662s] test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_inplace_on_view_weak_grad_fn - RuntimeError: !has_symbolic_sizes_strides_ INTERNAL ASSERT FAILED at "/home/xmfan/core/a/pytorch/aten/src/ATen/TensorGeometry.h":63, please report a bug to PyTorch.
To execute this test, run the following from the base repo dir:
python test/test_autograd.py TestAutogradWithCompiledAutograd.test_inplace_on_view_weak_grad_fn
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
FAILED [0.0077s] test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_leaf_assignment - RuntimeError: !has_symbolic_sizes_strides_ INTERNAL ASSERT FAILED at "/home/xmfan/core/a/pytorch/aten/src/ATen/TensorGeometry.h":63, please report a bug to PyTorch.
To execute this test, run the following from the base repo dir:
python test/test_autograd.py TestAutogradWithCompiledAutograd.test_leaf_assignment
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
FAILED [5.0485s] test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_setitem_mask - RuntimeError: !has_symbolic_sizes_strides_ INTERNAL ASSERT FAILED at "/home/xmfan/core/a/pytorch/aten/src/ATen/TensorGeometry.h":63, please report a bug to PyTorch.
To execute this test, run the following from the base repo dir:
python test/test_autograd.py TestAutogradWithCompiledAutograd.test_setitem_mask
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
FAILED [0.0102s] test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_tensor_hooks_inplace_over_view - RuntimeError: !has_symbolic_sizes_strides_ INTERNAL ASSERT FAILED at "/home/xmfan/core/a/pytorch/aten/src/ATen/TensorGeometry.h":63, please report a bug to PyTorch.
To execute this test, run the following from the base repo dir:
python test/test_autograd.py TestAutogradWithCompiledAutograd.test_tensor_hooks_inplace_over_view
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148799
Approved by: https://github.com/jansel, https://github.com/zou3519
Enables clang-tidy rule [`misc-use-internal-linkage`](https://clang.llvm.org/extra/clang-tidy/checks/misc/use-internal-linkage.html). This new check was introduced in Clang-Tidy 18 and is available due to recent update of Clang-Tidy 19.
The check marks functions and variables used only in the translation unit as static. Therefore undesired symbols are not leaked into other units, more link time optimisations are possible and the resulting binaries may be smaller.
The detected violations were mostly fixed by using static. In other cases, the symbols were indeed consumed by others files, then their declaring headers were included. Still some declarations were wrong and have been fixed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148948
Approved by: https://github.com/Skylion007
The previous PRs built up to this. We change compiled autograd's initial
trace to stop baking in metadata.
While tracing, we allocate some weirdly shaped tensors that we can put
proxies on. The initial trace should not be accessing any metadata of
these tensors (it will likely error out if it does because of how weird
the shapes are).
This involved fixing some various sites where we do specialize on the
metadata, like:
- we change CopySlices's apply_with_saved to proxy some calls
into the graph (this change is fairly hard to split out by itself).
- we stop calling InputBuffer::add
- we delete the weird metadata from the graph so that no graph passes
can make use of it.
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143417
Approved by: https://github.com/jansel, https://github.com/xmfan
ghstack dependencies: #143296, #143304, #143387, #143405
