This PR is a bit more involved but effectively works to drastically simplify PyObjectSlot and PyInterpreter.
1) For PyObjectSlot we now use a global pyinterpreter since there only is one. From here we change all of the call sites to rely on this assumption.
2) We also remove the "tags" of the PyInterpreter by deprecating `PyInterpreterStatus`.
For the reviewer, sadly it seems like `functorch/csrc/dim/dim.cpp` needed to get linted, so there is an unreadable amount of changes there. Fortunately, the only actual change in the file is as follows which just removes `getPyInterpreter()` from the `check_pyobj` call.
```
mpy::handle handle_from_tensor(Arena& A, TensorRef t) {
- // fast case: tensor is live in python
- std::optional<PyObject*> mb_obj =
- t->unsafeGetTensorImpl()->pyobj_slot()->check_pyobj(getPyInterpreter(), /*ignore_hermetic_tls=*/false);
- if (mb_obj.has_value() && !t->unsafeGetTensorImpl()->pyobj_slot()->owns_pyobj()) {
- return *mb_obj;
- }
- return A.autorelease(mpy::object::checked_steal(THPVariable_Wrap(*t)));
-}
-}
+ // fast case: tensor is live in python
+ std::optional<PyObject*> mb_obj =
+ t->unsafeGetTensorImpl()->pyobj_slot()->check_pyobj(
+ /*ignore_hermetic_tls=*/false);
+ if (mb_obj.has_value() &&
+ !t->unsafeGetTensorImpl()->pyobj_slot()->owns_pyobj()) {
+ return *mb_obj;
+ }
+ return A.autorelease(mpy::object::checked_steal(THPVariable_Wrap(*t)));
+}
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158427
Approved by: https://github.com/albanD
# Motivation
This PR moves the implementation of `torch.cuda.memory._set_allocator_settings` to `torch._C._accelerator_setAllocatorSettings`.
Since the original API was intended as a temporary/internal utility, I am not exposing the new function as a public API.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156175
Approved by: https://github.com/albanD
ghstack dependencies: #149601, #157908, #150312, #156165
This PR removes the integration point torch.fx had with torch::deploy (and another minor change).
Note: This PR has some broken mypy errors, but I believe those should have been in the code base beforehand, and should be fixed in a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158291
Approved by: https://github.com/albanD
ghstack dependencies: #158290
This PR is part of the work to deprecate torch::deploy in OSS. Effectively it does 3 things to get started.
1. Remove test_deploy_interaction as we no longer need to worry about this
2. Remove all torch._running_with_deploy checks and use the False path always (surfaced 1)
3. Remove `USE_DEPLOY` and switch to the default path always
Note: MyPy does fail on a bunch of things here as a bunch of older files are touched. It may be better to fix these things on a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158288
Approved by: https://github.com/albanD
The MIOpen integration has changed over the years. In the past, the MIOpen default for benchmark was True and if it were set to False it would use MIOpen Immediate Mode. But with #145294 the MIOpen benchmark default changed to False and to activate immediate mode you would set the deterministic flag to True. This has proved too restrictive because benchmark and deterministic flags are independent from immediate mode. Thus, immediate mode needs its own flag. Though MIOpen still masquerades behind torch.backends.cudnn and its flags, it seemed inappropriate to add an miopen-exclusive flag to the set of cudnn flags. This PR adds the first miopen-only flag to control its immediate mode.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158951
Approved by: https://github.com/jeffdaily
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
Added `torch.hash_tensor` reduction function with a `mode` argument that defaults to reduction with xor.
- The hash is always uint64.
- Integers will be casted to uint64 before performing the xor_sum reduction
- Floats will be upcasted to double and then bitcasted to uint64 before performing the xor_sum reduction
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154149
Approved by: https://github.com/albanD
This PR removes the integration point torch.fx had with torch::deploy (and another minor change).
Note: This PR has some broken mypy errors, but I believe those should have been in the code base beforehand, and should be fixed in a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158291
Approved by: https://github.com/albanD
ghstack dependencies: #158288, #158290
This PR is part of the work to deprecate torch::deploy in OSS. Effectively it does 3 things to get started.
1. Remove test_deploy_interaction as we no longer need to worry about this
2. Remove all torch._running_with_deploy checks and use the False path always (surfaced 1)
3. Remove `USE_DEPLOY` and switch to the default path always
Note: MyPy does fail on a bunch of things here as a bunch of older files are touched. It may be better to fix these things on a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158288
Approved by: https://github.com/albanD
This PR is a bit more involved but effectively works to drastically simplify PyObjectSlot and PyInterpreter.
1) For PyObjectSlot we now use a global pyinterpreter since there only is one. From here we change all of the call sites to rely on this assumption.
2) We also remove the "tags" of the PyInterpreter by deprecating `PyInterpreterStatus`.
For the reviewer, sadly it seems like `functorch/csrc/dim/dim.cpp` needed to get linted, so there is an unreadable amount of changes there. Fortunately, the only actual change in the file is as follows which just removes `getPyInterpreter()` from the `check_pyobj` call.
```
mpy::handle handle_from_tensor(Arena& A, TensorRef t) {
- // fast case: tensor is live in python
- std::optional<PyObject*> mb_obj =
- t->unsafeGetTensorImpl()->pyobj_slot()->check_pyobj(getPyInterpreter(), /*ignore_hermetic_tls=*/false);
- if (mb_obj.has_value() && !t->unsafeGetTensorImpl()->pyobj_slot()->owns_pyobj()) {
- return *mb_obj;
- }
- return A.autorelease(mpy::object::checked_steal(THPVariable_Wrap(*t)));
-}
-}
+ // fast case: tensor is live in python
+ std::optional<PyObject*> mb_obj =
+ t->unsafeGetTensorImpl()->pyobj_slot()->check_pyobj(
+ /*ignore_hermetic_tls=*/false);
+ if (mb_obj.has_value() &&
+ !t->unsafeGetTensorImpl()->pyobj_slot()->owns_pyobj()) {
+ return *mb_obj;
+ }
+ return A.autorelease(mpy::object::checked_steal(THPVariable_Wrap(*t)));
+}
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158427
Approved by: https://github.com/albanD
This PR removes the integration point torch.fx had with torch::deploy (and another minor change).
Note: This PR has some broken mypy errors, but I believe those should have been in the code base beforehand, and should be fixed in a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158291
Approved by: https://github.com/albanD
ghstack dependencies: #158288, #158290
This PR is part of the work to deprecate torch::deploy in OSS. Effectively it does 3 things to get started.
1. Remove test_deploy_interaction as we no longer need to worry about this
2. Remove all torch._running_with_deploy checks and use the False path always (surfaced 1)
3. Remove `USE_DEPLOY` and switch to the default path always
Note: MyPy does fail on a bunch of things here as a bunch of older files are touched. It may be better to fix these things on a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158288
Approved by: https://github.com/albanD
Implements https://github.com/pytorch/pytorch/issues/144908.
Implementation notes:
- `set_fullgraph` is implemented using `patch_config`, which changes config correctly during runtime and tracing.
- Moved setting `config.error_on_graph_break` from convert_frame.py to eval_frame.py. This is because this should only be done at the top-level decorated function. If we kept this in convert_frame.py, we would be changing `config.error_on_graph_break` on every top-level frame, which causes confusing behavior (see added test for example).
- InstructionTranslator reads from `config.error_on_graph_break` every `step()`. This is to determine the value of `config.error_on_graph_break` at the time of the graph break, because tracer cleanup will restore the value of `config.error_on_graph_break` .
- `convert_frame.py` determines whether we should abort tracing (fullgraph=True) or continue (fullgraph=False) by reading the value of the tracer's `error_on_graph_break`. If there is no tracer (failed to initialize), then default to reading `config.error_on_graph_break`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154289
Approved by: https://github.com/jansel, https://github.com/zou3519
ghstack dependencies: #154283
Implements https://github.com/pytorch/pytorch/issues/144908.
Implementation notes:
- `set_fullgraph` is implemented using `patch_config`, which changes config correctly during runtime and tracing.
- Moved setting `config.error_on_graph_break` from convert_frame.py to eval_frame.py. This is because this should only be done at the top-level decorated function. If we kept this in convert_frame.py, we would be changing `config.error_on_graph_break` on every top-level frame, which causes confusing behavior (see added test for example).
- InstructionTranslator reads from `config.error_on_graph_break` every `step()`. This is to determine the value of `config.error_on_graph_break` at the time of the graph break, because tracer cleanup will restore the value of `config.error_on_graph_break` .
- `convert_frame.py` determines whether we should abort tracing (fullgraph=True) or continue (fullgraph=False) by reading the value of the tracer's `error_on_graph_break`. If there is no tracer (failed to initialize), then default to reading `config.error_on_graph_break`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154289
Approved by: https://github.com/jansel, https://github.com/zou3519
ghstack dependencies: #154283
Implements https://github.com/pytorch/pytorch/issues/144908.
Implementation notes:
- `set_fullgraph` is implemented using `patch_config`, which changes config correctly during runtime and tracing.
- Moved setting `config.error_on_graph_break` from convert_frame.py to eval_frame.py. This is because this should only be done at the top-level decorated function. If we kept this in convert_frame.py, we would be changing `config.error_on_graph_break` on every top-level frame, which causes confusing behavior (see added test for example).
- InstructionTranslator reads from `config.error_on_graph_break` every `step()`. This is to determine the value of `config.error_on_graph_break` at the time of the graph break, because tracer cleanup will restore the value of `config.error_on_graph_break` .
- `convert_frame.py` determines whether we should abort tracing (fullgraph=True) or continue (fullgraph=False) by reading the value of the tracer's `error_on_graph_break`. If there is no tracer (failed to initialize), then default to reading `config.error_on_graph_break`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154289
Approved by: https://github.com/jansel, https://github.com/zou3519
ghstack dependencies: #154283
Previously when processing `sym_and(a, b, c)`, symbolic shapes wouldn't individually process a, b, and c and store their implications. This would lead us to data-dependent error on individual checks, e.g. we stored `u0 >= 0 & u0 <= 10`, but then couldn't figure out `u0 <= 10`.
This handles that, and also makes `sym_and/or` user-code friendly, for testing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154737
Approved by: https://github.com/laithsakka
Add comprehensive module docstring explaining the tracing rules and policies
that govern TorchDynamo's compilation decisions, including skip rules,
inlining policies, and library-specific handling.
Originally generated by claude but reviewed and edited by me.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155401
Approved by: https://github.com/williamwen42
I tried `beginAllocateToPool` instead of `_cuda_beginAllocateCurrentStreamToPool` and the error in #151199 does not happen any more.
However, this approach is unsafe for multithreading. When multiple run_eager happens concurrently, we expect memory allocation to different mem_pool. Since beginAllocateToPool does not check stream, these memory allocation may happen on the same mem_pool.
So, I use `_cuda_beginAllocateCurrentThreadToPool` to direct all memory allocation on the same thread to a given mem_pool. In particular, `_cuda_beginAllocateCurrentThreadToPool` records the launching thread id, and during runtime checks if the current thread id matches the launching thread id.
Fixes#151199
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152472
Approved by: https://github.com/eellison, https://github.com/ngimel
Implement traceable config patching for Dynamo: enables restricted patching of Dynamo config where user can use a context manager/decorator to change tracing behavior for parts of the code.
The new `dont_skip_tracing` decorator/context manager for ignoring most trace rules is easily implemented with this more generic traceable config patching feature.
Implementation:
- Create a new specialized context manager class representing a wrapper around torch._dynamo.config.patch
- Dynamo doesn't trace into the context manager but updates config at compile time
- Correctness is based on our correctness for handling supported context managers
- Implementation is inspired by how `GradModeVariable` is implemented.
Previous attempts: https://github.com/pytorch/pytorch/pull/148736 (decorator-only global approach) and https://github.com/pytorch/pytorch/pull/149439 (decorator-only traceback approach)
See https://docs.google.com/document/d/1vWNwKL_jpg-PLopifcaSa338wks3GqSVF4GHRguybGg/edit?tab=t.0 for more details on implementation - including previous approaches.
NOTE: this PR fixes a bug where skipped code objects were not tracked by convert_frame.py, leading to cases where code objects would be automatically skipped even after `torch._dynamo.reset()`. This exposed some latent dynamo-wrapped test failures in CI that previously passed in CI but not locally.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150586
Approved by: https://github.com/jansel, https://github.com/zou3519, https://github.com/anijain2305
Implement traceable config patching for Dynamo: enables restricted patching of Dynamo config where user can use a context manager/decorator to change tracing behavior for parts of the code.
The new `dont_skip_tracing` decorator/context manager for ignoring most trace rules is easily implemented with this more generic traceable config patching feature.
Implementation:
- Create a new specialized context manager class representing a wrapper around torch._dynamo.config.patch
- Dynamo doesn't trace into the context manager but updates config at compile time
- Correctness is based on our correctness for handling supported context managers
- Implementation is inspired by how `GradModeVariable` is implemented.
Previous attempts: https://github.com/pytorch/pytorch/pull/148736 (decorator-only global approach) and https://github.com/pytorch/pytorch/pull/149439 (decorator-only traceback approach)
See https://docs.google.com/document/d/1vWNwKL_jpg-PLopifcaSa338wks3GqSVF4GHRguybGg/edit?tab=t.0 for more details on implementation - including previous approaches.
NOTE: this PR fixes a bug where skipped code objects were not tracked by convert_frame.py, leading to cases where code objects would be automatically skipped even after `torch._dynamo.reset()`. This exposed some latent dynamo-wrapped test failures in CI that previously passed in CI but not locally.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150586
Approved by: https://github.com/jansel, https://github.com/zou3519, https://github.com/anijain2305
Summary:
as title
`export._trace._WrapperModule` is used to wrap functions into a Module so we can export the function.
We add `export._wrapper_utils` to `dynamo`'s `MOD_INLINELIST` so dynamo traces into `_WrapperModule`
Fixes https://github.com/pytorch/pytorch/issues/146867
Test Plan:
```
buck run fbcode//mode/dev-nosan //caffe2/test:test_export -- -r wrapper_module
```
Differential Revision: D72986826
Pull Request resolved: https://github.com/pytorch/pytorch/pull/151264
Approved by: https://github.com/angelayi
