Reference: https://docs.astral.sh/ruff/formatter/black/#assert-statements
> Unlike Black, Ruff prefers breaking the message over breaking the assertion, similar to how both Ruff and Black prefer breaking the assignment value over breaking the assignment target:
>
> ```python
> # Input
> assert (
> len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
>
> # Black
> assert (
> len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
> # Ruff
> assert len(policy_types) >= priority + num_duplicates, (
> f"This tests needs at least {priority + num_duplicates} many types."
> )
> ```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144546
Approved by: https://github.com/malfet
Summary:
X-link: https://github.com/pytorch/executorch/pull/5720
For smaller models the overhead of profiling ops might be prohibitively large (distorting the inference execution time significantly) so we provide users an option to disable op profiling and essentially only profile the important events such as inference execution time.
To disable operator profiling users need to do:
```
etdump_gen.set_event_tracer_profiling_level(executorch::runtime::EventTracerProfilingLevel::kNoOperatorProfiling);
```
Test Plan: Added test case.
Differential Revision: D61883224
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136838
Approved by: https://github.com/dbort
Adds a ruff lint rule to ban raising raw exceptions. Most of these should at the very least be runtime exception, value errors, type errors or some other errors. There are hundreds of instance of these bad exception types already in the codebase, so I have noqa'd most of them. Hopefully this error code will get commiters to rethink what exception type they should raise when they submit a PR.
I also encourage people to gradually go and fix all the existing noqas that have been added so they can be removed overtime and our exception typing can be improved.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124570
Approved by: https://github.com/ezyang
Summary:
This diff adds support in the ExecuTorch codegen layer to log the outputs of kernels to event_tracer. It does this by calling the `event_tracer_log_evalue` API.
When the `ET_EVENT_TRACER_ENABLED` flag is disabled this is essentially a no-op and will add no overhead.
Test Plan: CI
Reviewed By: larryliu0820
Differential Revision: D51534590
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114584
Approved by: https://github.com/larryliu0820
Summary:
Exposing a codegen mode for generating a hook for user to register their kernels.
If we pass `--manual-registration` flag to `gen_executorch.py`, we will generate the following files:
1. RegisterKernels.h which declares a `register_all_kernels()` API inside `torch::executor` namespace.
2. RegisterKernelsEverything.cpp which implements `register_all_kernels()` by defining an array of generated kernels.
This way user can depend on the library declared by `executorch_generated_lib` macro (with `manual_registration=True`) and be able to include `RegisterKernels.h`. Then they can manually call `register_all_kernels()` instead of relying on C++ static initialization mechanism which is not available in some embedded systems.
Test Plan:
Rely on the unit test:
```
buck2 test fbcode//executorch/runtime/kernel/test:test_kernel_manual_registration
```
Reviewed By: cccclai
Differential Revision: D49439673
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110086
Approved by: https://github.com/cccclai
Summary: Split out from D48975975, this handles the pytorch specific changes to add support for event_tracer in codegen layer.
Test Plan: CI
Reviewed By: dbort
Differential Revision: D49487710
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109990
Approved by: https://github.com/Jack-Khuu
Summary: Basically we generate `CustomOpsNativeFunctions.h` for registering custom ops into PyTorch JIT runtime. This header needs to hookup with the C++ kernel implementation of all the custom ops. For this reason it should include ATen headers instead of Executorch headers. This PR changes it.
Test Plan: Rely on existing CI jobs
Differential Revision: D48282828
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107064
Approved by: https://github.com/kirklandsign
Summary: Currently we rely on root operator, but we also need to check for et_kernel_metadata for used specialized kernels.
Test Plan: contbuild & OSS CI
Reviewed By: Jack-Khuu
Differential Revision: D46882119
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104005
Approved by: https://github.com/Jack-Khuu
Summary:
keys and change codegen to take ETKernelIndex
We are adding support for dtype and dim order specialized kernel registration. This requires us to reorganize `BackendIndex` (which is a `Dict[DispatchKey, Dict[OperatorName, BackendMetadata]]`) to be `Dict[OperatorName, Dict[ETKernelKey, BackendMetadata]]`. This PR adds new data structures in order to support this change:
* `ETKernelKey` to retrieve a certain kernel from the registry.
* `ETKernelIndex`, the dictionary from operator name to kernel key to kernel mapping.
Note that the codegen logic is not changed yet, we need subsequent diffs to actually generate code for different kernel keys.
Test Plan: Added tests
Reviewed By: Jack-Khuu
Differential Revision: D46407096
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102874
Approved by: https://github.com/Jack-Khuu, https://github.com/kirklandsign
keys and change codegen to take ETKernelIndex
We are adding support for dtype and dim order specialized kernel registration. This requires us to reorganize `BackendIndex` (which is a `Dict[DispatchKey, Dict[OperatorName, BackendMetadata]]`) to be `Dict[OperatorName, Dict[ETKernelKey, BackendMetadata]]`. This PR adds new data structures in order to support this change:
* `ETKernelKey` to retrieve a certain kernel from the registry.
* `ETKernelIndex`, the dictionary from operator name to kernel key to kernel mapping.
Note that the codegen logic is not changed yet, we need subsequent diffs to actually generate code for different kernel keys.
Differential Revision: [D46206339](https://our.internmc.facebook.com/intern/diff/D46206339/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102565
Approved by: https://github.com/Jack-Khuu
Summary:
In ATen mode, we add the RuntimeContext arg, so we have something like
```
TORCH_API inline at::Tensor & gelu_outf(torch::executor::RuntimeContext & context, const at::Tensor & self, c10::string_view approximate, at::Tensor & out) {
return at::gelu_outf(self, approximate, out);
}
```
and user can use `<namespace like aten>::gelu_outf` and we will automatically dispatch the registered function in aten kernel using `at::gelu_outf` (dispatched by ATen/Functions.h header)
In optimized kernel tests, we can now automatically handle between aten kernel and optimized kernel.
The implication is that the test must depend on the correctness of codegen; an error in codegen can break the kernel tests.
Test Plan: CI
Differential Revision: D43777848
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96084
Approved by: https://github.com/larryliu0820
Applies the remaining flake8-comprehension fixes and checks. This changes replace all remaining unnecessary generator expressions with list/dict/set comprehensions which are more succinct, performant, and better supported by our torch.jit compiler. It also removes useless generators such as 'set(a for a in b)`, resolving it into just the set call.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94676
Approved by: https://github.com/ezyang
Preferring dash over underscore in command-line options. Add `--command-arg-name` to the argument parser. The old arguments with underscores `--command_arg_name` are kept for backward compatibility.
Both dashes and underscores are used in the PyTorch codebase. Some argument parsers only have dashes or only have underscores in arguments. For example, the `torchrun` utility for distributed training only accepts underscore arguments (e.g., `--master_port`). The dashes are more common in other command-line tools. And it looks to be the default choice in the Python standard library:
`argparse.BooleanOptionalAction`: 4a9dff0e5a/Lib/argparse.py (L893-L895)
```python
class BooleanOptionalAction(Action):
def __init__(...):
if option_string.startswith('--'):
option_string = '--no-' + option_string[2:]
_option_strings.append(option_string)
```
It adds `--no-argname`, not `--no_argname`. Also typing `_` need to press the shift or the caps-lock key than `-`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94505
Approved by: https://github.com/ezyang, https://github.com/seemethere
Rely on CI.
Avoid issues such as:
```
Traceback (most recent call last):
File "<string>", line 38, in <module>
File "<string>", line 36, in __run
File "/usr/local/fbcode/platform010/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/local/fbcode/platform010/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/re_cwd/buck-out/v2/gen/fbcode/2841b324ed9b88dd/caffe2/torchgen/__gen_executorch__/gen_executorch#link-tree/torchgen/gen_executorch.py", line 690, in <module>
main()
File "/re_cwd/buck-out/v2/gen/fbcode/2841b324ed9b88dd/caffe2/torchgen/__gen_executorch__/gen_executorch#link-tree/torchgen/gen_executorch.py", line 626, in main
parsed_yaml, custom_ops_parsed_yaml = parse_yaml_files(
File "/re_cwd/buck-out/v2/gen/fbcode/2841b324ed9b88dd/caffe2/torchgen/__gen_executorch__/gen_executorch#link-tree/torchgen/gen_executorch.py", line 505, in parse_yaml_files
translate_native_yaml(
File "/re_cwd/buck-out/v2/gen/fbcode/2841b324ed9b88dd/caffe2/torchgen/__gen_executorch__/gen_executorch#link-tree/torchgen/gen_executorch.py", line 448, in translate_native_yaml
for e in native_es:
TypeError: 'NoneType' object is not iterable
```
Differential Revision: [D42729435](https://our.internmc.facebook.com/intern/diff/D42729435)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92938
Approved by: https://github.com/JacobSzwejbka
As titled. To register a custom op into Executorch, we need:
* `custom_ops.yaml`, defines the operator schema and the corresponding native function.
* `custom_ops.cpp`, defines the kernel.
* `RegisterDispatchKeyCustomOps.cpp`, a template to register operator into PyTorch.
Added a new test for custom ops. The custom op `custom::add_3.out` takes 3 tensors and add them together. The test makes sure it is registered correctly and then verifies the outcome is correct.
Differential Revision: [D42204263](https://our.internmc.facebook.com/intern/diff/D42204263/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91291
Approved by: https://github.com/ezyang
