This PR is a supplement to #130082. The previous PR #130082 fulfill the basic functionality of codegen, while we found it fails to handle the device sameness check in lots of uts. Current PR is aimed to facilitate the XPU device guard code generation.
With current PR, the code snippet in `RegisterXPU.cpp` is as follows, where we can see the device guard is successfully generated.
```c++
namespace {
at::Tensor & wrapper_XPU_Tensor_float_out_normal_out(const at::Tensor & mean, double std, ::std::optional<at::Generator> generator, at::Tensor & out) {
std::optional<Device> common_device = std::nullopt;
(void)common_device; // Suppress unused variable warning
c10::impl::check_and_update_common_device(common_device, out, "wrapper_XPU_Tensor_float_out_normal_out", "out");
c10::impl::check_and_update_common_device(common_device, mean, "wrapper_XPU_Tensor_float_out_normal_out", "mean");
const OptionalDeviceGuard device_guard(device_of(out));
return at::native::normal_out(mean, std, generator, out);
}
} // anonymous namespace
```
Nevertheless, without current change, the generated code is
```c++
namespace {
at::Tensor & wrapper_XPU_Tensor_float_out_normal_out(const at::Tensor & mean, double std, ::std::optional<at::Generator> generator, at::Tensor & out) {
// No device check
// DeviceGuard omitted
return at::native::normal_out(mean, std, generator, out);
}
} // anonymous namespace
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133980
Approved by: https://github.com/EikanWang, https://github.com/malfet
In gen.py, the code for generating CompositeViewCopyKernels.cpp includes *_native.h headers for "view_groups" but not "structured_native_functions". However, this results in the TORCH_API in the headers being ineffective and presents such functions being used outside libtorch_cpu.so
This patch ensures that gen.py includes the native headers for "structured_native_functions" in the same way as for "view_groups".
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131208
Approved by: https://github.com/bdhirsh
Summary: When looking up for what backend call to use for a fallback op (see get_backend_index_for_aoti), sometimes we need to search for a NativeFunction's structured delegate. Previous str:NativeFunctionsGroup dict missed some cases, such as aten.index.Tensor, and that's why aten.index.Tensor was specified in the fallback_ops list but no C shim entry was generated for it. This PR uses a more robust OperatorName:NativeFunctionsGroup mapping.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125962
Approved by: https://github.com/chenyang78
This is a subset of changes extracted from https://github.com/pytorch/pytorch/pull/124683/
This PR contains modifications to make Inductor work with unbacked symbol inputs, which can occur when a data-dependent sized tensor is saved for backwards. The problems to be fixed:
* When binding initial symbols, we unconditionally bind unbacked symbols (instead of computing if they are needed, which only looks at backed symbols)
* Benchmark generation code doesn't work with unbacked symints as we have no hints to actually feed in real values. So I pick a random number and you are expected to fix it if it doesn't work
* Need to make sure we don't install dependencies on unbacked SymInt inputs, that puts us down the "promptly deallocate the input" path, but that's pointless for unbacked SymInt
Fixes https://github.com/pytorch/pytorch/issues/124652
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124739
Approved by: https://github.com/jansel
ghstack dependencies: #124310, #124314, #124316, #124394
Summary: The current C shim layer manually implements a C interface for a handful of ops. Obviously that's not scalable if we want to extend it to cover all aten ops. This new torchgen script automatically generates C shim interfaces for CPU and CUDA backends. The interface follows the same parameter passing rules as the current C shim layer, such as
* Use plain C data types to pass parameters
* Use AtenTensorHandle to pass at::Tensor
* Use pointer type to pass optional parameter
* Use pointer+length to pass list
* Use device_type+device_index to pass device
* When a parameter is a pointer of pointer, e.g. AtenTensorHandle**, the script generates either a list of optional values or an optional list of values
https://gist.github.com/desertfire/83701532b126c6d34dae6ba68a1b074a is an example of the generated torch/csrc/inductor/aoti_torch/generated/c_shim_cuda.cpp file. The current version doesn't generate C shim wrappers for all aten ops, and probably generates more wrappers than needed on the other hand, but it should serve as a good basis.
This PR by itself won't change AOTI codegen and thus won't introduce any FC breakage. The actual wrapper codegen changes will come in another PR with some version control flag to avoid FC breakage.
Differential Revision: [D54258087](https://our.internmc.facebook.com/intern/diff/D54258087)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120513
Approved by: https://github.com/jansel
Summary: To be used in https://github.com/pytorch/pytorch/pull/113873. Since set_ is effectively an inplace view op, we'll need to skip caching them.
Test Plan: Built pytorch; specifically this step: `/home/slarsen/local/miniconda3/envs/pytorch-3.10/bin/python -m torchgen.gen --source-path /home/slarsen/local/pytorch/cmake/../aten/src/ATen --install_dir /home/slarsen/local/pytorch/build/aten/src/ATen --per-operator-headers --generate sources --output-dependencies /home/slarsen/local/pytorch/build/aten/src/ATen/generated_sources.cmake`
Differential Revision: [D52814561](https://our.internmc.facebook.com/intern/diff/D52814561)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115769
Approved by: https://github.com/bdhirsh
* Enable PERF402. Makes code more efficient and succinct by removing useless list copies that could be accomplished either via a list constructor or extend call. All test cases have noqa added since performance is not as sensitive in that folder.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115505
Approved by: https://github.com/malfet
Summary: To be used in https://github.com/pytorch/pytorch/pull/113873. Since set_ is effectively an inplace view op, we'll need to skip caching them.
Test Plan: Built pytorch; specifically this step: `/home/slarsen/local/miniconda3/envs/pytorch-3.10/bin/python -m torchgen.gen --source-path /home/slarsen/local/pytorch/cmake/../aten/src/ATen --install_dir /home/slarsen/local/pytorch/build/aten/src/ATen --per-operator-headers --generate sources --output-dependencies /home/slarsen/local/pytorch/build/aten/src/ATen/generated_sources.cmake`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115769
Approved by: https://github.com/bdhirsh
* Enables PIE807 + PIE810. PIE807 is do not reimplement list builtin function using lambda and PIE810 is to always fuse startswith / endswith calls (I applied the autofixes for this before we had ruff enabled).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106218
Approved by: https://github.com/albanD
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
The problem:
- The new CustomOp API depends on torchgen.model
- torchgen.model imports `yaml`
- `yaml` is not a PyTorch runtime dependency
To unblock myself, because I'm not sure how long it'll take to
convince people yaml should be a PyTorch runtime dependency
(unless one of you wants to approve #100166), this PR removes the
yaml dependency from torchgen.model.
It does so by splitting torchgen.utils (the offender) into
torchgen.utils (no yaml) and torchgen.yaml (which uses yaml).
Test Plan:
- CI
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100203
Approved by: https://github.com/ezyang, https://github.com/Skylion007
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
Changes:
1. `typing_extensions -> typing-extentions` in dependency. Use dash rather than underline to fit the [PEP 503: Normalized Names](https://peps.python.org/pep-0503/#normalized-names) convention.
```python
import re
def normalize(name):
return re.sub(r"[-_.]+", "-", name).lower()
```
2. Import `Literal`, `Protocal`, and `Final` from standard library as of Python 3.8+
3. Replace `Union[Literal[XXX], Literal[YYY]]` to `Literal[XXX, YYY]`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94490
Approved by: https://github.com/ezyang, https://github.com/albanD
Retry of #90590, which is a retry of #89594. Original PR reverted due to internal breakage.
This PR fixes the breakage by adding a default value to the new argument.
This PR allows `get_native_function_declarations` API to take a function as argument. This function should take `NativeFunction` as input and emit code for native function declaration. By default it is `dest.compute_native_function_declaration`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90780
Approved by: https://github.com/ezyang
Summary:
Sometimes we want to extend an existing custom namespace library, instead of creating a new one,
but we don't have a namespace config right now, so we hardcode some custom libraries defined
in pytorch today, i.e. quantized and quantized_decomposed
Test Plan:
ci
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88229
Approved by: https://github.com/ezyang
Our prevailing strategy for symbolic shapes in C++ is to only
write the SymInt version of the code, and pay a slight performance
tax from not knowing if it is symbolic or not. However, there are
some fastpath functions where this tax is unacceptable, and we want
to specialize for the int case. Sometimes, it is easy to template
the function; but when the function involves Tensors, it is not,
because the functions you may want to call are not templated,
e.g., t.view vs t.view_symint
This PR adds an at::symint:: namespace which contains templated
functions for all functions in PyTorch which you can use in this
way. To show this works, I refactored sum_to to stop incorrectly
reinterpret casting and instead use a template. Instead of
t.sizes(), we call at::symint::sizes<T>(t), and so forth.
The template functions are SFINAE'd using a template argument that
is not otherwise used. As such, deduction is impossible. Typically, deduction
is hard anyway, because many of the constructors are ambiguous (this
is why we split foo and foo_symint in the first place). So you must pass
a template argument to these functions.
These functions are codegened into Functions.h so they are subject
to per-operator headers. This matters most for methods, which likely
didn't include the per-operator header, so you will have to add an
include in that case. We never generate method variants for these.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86329
Approved by: https://github.com/bdhirsh, https://github.com/voznesenskym
Summary: Context: currently `static_dispatch` assumes that given a native function `f`, we always want to map from its `DispatchSignature` to its `CppSignature`. This assumption may not hold true for some use cases, where the source bindings may not come from its `DispatchSignature`. Here I'm changing the argument `sig: DispatcherSignature` to be `sig: Union[CppSignature, DispatcherSignature]`, also removes unused `f`
Test Plan: Rely on added unit test.
Differential Revision: D39192969
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84384
Approved by: https://github.com/iseeyuan
