Note. This is a replica PR of #155901 which will be closed. I had to create a new PR in order to add it into my ghstack as there are some later commits which depend on it.
### Summary
🚀 This PR moves the prioritized text linker optimization from setup.py to cmake ( and enables by default on Linux aarch64 systems )
This change consolidates what was previously manual CI logic into a single location (cmake), ensuring consistent behavior across local builds, CI pipelines, and developer environments.
### Motivation
Prioritized text layout has measurable performance benefits on Arm systems by reducing code padding and improving cache utilization. This optimization was previously triggered manually via CI scripts (.ci/aarch64_linux/aarch64_ci_build.sh) or user-set environment variables. By detecting the target architecture within setup.py, this change enables the optimization automatically where applicable, improving maintainability and usability.
Note:
Due to ninja/cmake graph generation issues we cannot apply the linker file globally to all targets to the targets must be manually defined. See CMakeLists.txt the main libraries torch_python, torch, torch_cpu, torch_cuda, torch_xpu have been targetted which should be enough to maintain the performance benefits outlined above.
Co-authored-by: Usamah Zaheer <usamah.zaheer@arm.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160078
Approved by: https://github.com/seemethere
Note. This is a replica PR of #155901 which will be closed. I had to create a new PR in order to add it into my ghstack as there are some later commits which depend on it.
### Summary
🚀 This PR moves the prioritized text linker optimization from setup.py to cmake ( and enables by default on Linux aarch64 systems )
This change consolidates what was previously manual CI logic into a single location (cmake), ensuring consistent behavior across local builds, CI pipelines, and developer environments.
### Motivation
Prioritized text layout has measurable performance benefits on Arm systems by reducing code padding and improving cache utilization. This optimization was previously triggered manually via CI scripts (.ci/aarch64_linux/aarch64_ci_build.sh) or user-set environment variables. By detecting the target architecture within setup.py, this change enables the optimization automatically where applicable, improving maintainability and usability.
Note:
Due to ninja/cmake graph generation issues we cannot apply the linker file globally to all targets to the targets must be manually defined. See CMakeLists.txt the main libraries torch_python, torch, torch_cpu, torch_cuda, torch_xpu have been targetted which should be enough to maintain the performance benefits outlined above.
Co-authored-by: Usamah Zaheer <usamah.zaheer@arm.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160078
Approved by: https://github.com/seemethere
Reland of #153153, which was incidentally closed.
Update the minimum CMake version to 3.27 because of it provides more CUDA targets such as CUDA::nvperf_host so that it is possible to remove some of our forked CUDA modules. See https://github.com/pytorch/pytorch/pull/153783.
It's also possible to facilitate future third-party updates such as FBGEMM (its current shipped version requires 3.21).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154783
Approved by: https://github.com/ezyang
### Changes
- Detect NVSHMEM install location via `sysconfig.get_path("purelib")`, which typically resolves to `<conda_env>/lib/python/site-packages`, and NVSHMEM include and lib live under `nvidia/nvshmem`
- Added link dir via `target_link_directories`
- Removed direct dependency on mlx5
- Added preload rule (following other other NVIDIA libs)
### Plan of Record
1. End user experience: link against NVSHMEM dynamically (NVSHMEM lib size is 100M, similar to NCCL, thus we'd like users to `pip install nvshmem` than torch carrying the bits)
2. Developer experience: at compile time, prefers wheel dependency than using Git submodule
General rule: submodule for small lib that torch can statically link with
If user pip install a lib, our CI build process should do the same, rather than building from Git submodule (just for its header, for example)
3. Keep `USE_NVSHMEM` to gate non-Linux platforms, like Windows, Mac
4. At configuration time, we should be able to detect whether nvshmem is available, if not, we don't build `NVSHMEMSymmetricMemory` at all.
For now, we have symbol dependency on two particular libs from NVSHMEM:
- libnvshmem_host.so: contains host side APIs;
- libnvshmem_device.a: contains device-side global variables AND device function impls.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153010
Approved by: https://github.com/ngimel, https://github.com/fduwjj, https://github.com/Skylion007
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
Fix: #141974
This PR makes `ViewMeta` sequence, present in functional tensors,
serializable with pickle. In order to accomplish that, it makes
`ViewMeta` an abstract class with overridable `forward` and `reverse`
functions. In this context, each operation that once instanciated
`ViewMeta`, should now create a new specialized class that inherits from
`ViewMeta. Therefore, this PR also uses codegen for creating these
specializations.
In summary, these are the changes this PR introduces:
- `ViewMeta` is turned into an abstract class (see
_FunctionalStorageImpl.cpp_). `forward` and `reverse` are pure virtual
functions that need to be implemented. `to_out_index` should be
implemented by operations that might return more than 1 output.
- New `ViewMeta` specializations for `resize_` and `_unsafe_view` are
created (see _FunctionalizeFallbackKernel.h_).
- New templates _ViewMetaClasses.{cpp,h}_ are created. They hold the
declaration and definition of the `ViewMeta` specializations, which
are automatically generated in the ATen codegen (see _gen.py_).
- New `_functionalization` Python sub-module is created (see
_Module.cpp_). It serves as namespace for the `ViewMeta`
specializations and `InverseReturnMode` enum.
- New template _ViewMetaClassesPythonBinding.cpp_ is created. It holds
the automatically generated Python bindings for the `ViewMeta`
specialization, which are generated in the torch codegen (see
_generate_code.py_).
Note that this PR makes use of codegen at 2 different moments:
- ATen codegen (_gen.py_): generates the `ViewMeta` specialized classes.
- Torch codegen (_generate_code.py_): generated the Python bindings for
them.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143712
Approved by: https://github.com/bdhirsh
Changes by apply order:
1. Replace all `".."` and `os.pardir` usage with `os.path.dirname(...)`.
2. Replace nested `os.path.dirname(os.path.dirname(...))` call with `str(Path(...).parent.parent)`.
3. Reorder `.absolute()` ~/ `.resolve()`~ and `.parent`: always resolve the path first.
`.parent{...}.absolute()` -> `.absolute().parent{...}`
4. Replace chained `.parent x N` with `.parents[${N - 1}]`: the code is easier to read (see 5.)
`.parent.parent.parent.parent` -> `.parents[3]`
5. ~Replace `.parents[${N - 1}]` with `.parents[${N} - 1]`: the code is easier to read and does not introduce any runtime overhead.~
~`.parents[3]` -> `.parents[4 - 1]`~
6. ~Replace `.parents[2 - 1]` with `.parent.parent`: because the code is shorter and easier to read.~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129374
Approved by: https://github.com/justinchuby, https://github.com/malfet
Changes by apply order:
1. Replace all `".."` and `os.pardir` usage with `os.path.dirname(...)`.
2. Replace nested `os.path.dirname(os.path.dirname(...))` call with `str(Path(...).parent.parent)`.
3. Reorder `.absolute()` ~/ `.resolve()`~ and `.parent`: always resolve the path first.
`.parent{...}.absolute()` -> `.absolute().parent{...}`
4. Replace chained `.parent x N` with `.parents[${N - 1}]`: the code is easier to read (see 5.)
`.parent.parent.parent.parent` -> `.parents[3]`
5. ~Replace `.parents[${N - 1}]` with `.parents[${N} - 1]`: the code is easier to read and does not introduce any runtime overhead.~
~`.parents[3]` -> `.parents[4 - 1]`~
6. ~Replace `.parents[2 - 1]` with `.parent.parent`: because the code is shorter and easier to read.~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129374
Approved by: https://github.com/justinchuby, https://github.com/malfet
