Fixes #ISSUE_NUMBER
## Background
Task: [T222738229](https://www.internalfb.com/intern/tasks/?t=222738229)
It's the first starter task on the project **_Enabling TorchNative Standalone on Whisper_**. We are using cshim to create a layer of abstraction between _**libtorch**_ and **_AOTInductor generated artifacts_**.
So we needed to add an entry in the cshim for every API surface in libtorch. And we only care about operators that AOTInductor does not handle. And for this task, we only wanted to add it for the following ops.
## What I've done?
4 new fallback ops are added that show up in the Whisper model. (torchgen/aoti/fallback_ops.py)
- aten.permute (default)
- aten.squueze (dim)
- aten.abs (default)
- aten.hann_window (default)
Then I ran the below command to generate new header C shim header files. As it says [here](7e86a7c015/torchgen/gen.py (L2424-L2436%20for%20details))
`python torchgen/gen.py --update-aoti-c-shim`
Then, `python setup.py develop` to rebuild PyTorch
## Testing
Also 4 new tests have been added on test/inductor/test_aot_inductor.py
- test_proxy_executor_permute
- test_proxy_executor_abs
- test_proxy_executor_squeeze
- test_proxy_executor_hann
I ran these commands to test it (inside local pytorch root folder):
`python test/inductor/test_aot_inductor.py -k test_proxy_executor_permute`
`python test/inductor/test_aot_inductor.py -k test_proxy_executor_abs`
`python test/inductor/test_aot_inductor.py -k test_proxy_executor_squeeze`
`python test/inductor/test_aot_inductor.py -k test_proxy_executor_hann`
## NOTE:
I didn't see any order between the tests inside _test/inductor/test_aot_inductor.py_. That's why, I added new tests just after the test given in the example.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154251
Approved by: https://github.com/angelayi
Summary:
# Context
See the first PR https://github.com/pytorch/pytorch/pull/153670
# This PR
1. Migrate 3 clamp ops from out-of-tree to in-tree(had to migrate the 3 ops altogether, because clamp.out calls all 3 stubs, which are also called by the other 2 ops):
- clamp.out
- clamp_min.out
- clamp_max.out
2. Also enabled structured kernel codegen for MTIA, which is needed by clamp
3. Also introduced the `--mtia` flag to torchgen to prevent OSS from gencoding MTIA code.(Otherwise we got such link error `lib/libtorch_cpu.so: undefined reference to at::detail::empty_mtia`)
Differential Revision: D74674418
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154015
Approved by: https://github.com/albanD, https://github.com/nautsimon
Added AOTIModelContainerRunnerMps and a shim for mps fallback ops.
I also added a mps-specific shim which contains one operator, which will be used to set arguments being passed to the Metal kernel:
```
AOTI_TORCH_EXPORT AOTITorchError aoti_torch_mps_set_arg(
AOTIMetalKernelFunctionHandle func,
unsigned idx,
AtenTensorHandle tensor);
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153964
Approved by: https://github.com/malfet, https://github.com/desertfire
Added AOTIModelContainerRunnerMps and a shim for mps fallback ops.
I also added a mps-specific shim which contains one operator, which will be used to set arguments being passed to the Metal kernel:
```
AOTI_TORCH_EXPORT AOTITorchError aoti_torch_mps_set_arg(
AOTIMetalKernelFunctionHandle func,
unsigned idx,
AtenTensorHandle tensor);
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153964
Approved by: https://github.com/malfet, https://github.com/desertfire
Summary:
# Context
The MTIA New Aten Backend work is essentially to move MTIA operators from pytorch out-of-tree to in-tree, with following benefits:
1. Avoid duplicate code copied from pytorch, e.g. view ops implementation, util functions.
2. Utilize TensorIterator and structured kernel codegen, avoid manual implementation of broadcasting, dtype casting, asserting, etc.
3. Eliminate MTIA's own codegen flow, which is unnecessary complexity.
4. Overall make MTIA's aten backend more pytorch native.
Differential Revision: D74672464
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153670
Approved by: https://github.com/albanD, https://github.com/nautsimon
https://github.com/pytorch/pytorch/pull/129001#discussion_r1645126801 is the motivation for the whole stack of PRs. In `torch/__init__.py`, `torch._C.Type` shadows `from typing import Type`, and there is no type stub for `torch._C.Type` in `torch/_C/__init__.pyi`. So we need to use `from typing import Type as _Type`. After enabling [Generic TypeAlias (PEP 585)](https://peps.python.org/pep-0585) in the `.pyi` type stub files, we can use `type` instead of `typing.Type` or `from typing import Type as _Type`.
------
- [Generic TypeAlias (PEP 585)](https://peps.python.org/pep-0585): e.g. `typing.List[T] -> list[T]`, `typing.Dict[KT, VT] -> dict[KT, VT]`, `typing.Type[T] -> type[T]`.
- [Union Type (PEP 604)](https://peps.python.org/pep-0604): e.g. `Union[X, Y] -> X | Y`, `Optional[X] -> X | None`, `Optional[Union[X, Y]] -> X | Y | None`.
Note that in `.pyi` stub files, we do not need `from __future__ import annotations`. So this PR does not violate issue #117449:
- #117449
------
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150727
Approved by: https://github.com/aorenste
ghstack dependencies: #150726
This change enables basic NestedTensor operations on HPU,
fixing the runtime error when creating a NestedTensor on HPU.
- Extended `NestedTensorImpl` to recognize `hpu` as a valid storage device.
- Added `NestedTensorHPU` to `DispatchKey` parsing in `DispatchKey.cpp`.
- Updated `torchgen/model.py` to include `NestedTensorHPU` in `dispatch_keys`.
- Modified `native_functions.yaml` to enable `NestedTensorHPU` support for various ops.
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148659
Approved by: https://github.com/jeromean, https://github.com/albanD, https://github.com/sujoysaraswati
Fix: https://github.com/pytorch/xla/issues/8755
This PR introduces `TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE`
environment variable. Setting this variable makes it so the
functionalization kernels won't run the meta reference, which is used to
propagate expected sizes and strides.
Currently, PyTorch/XLA doesn't actually propagates the correct strides
to its tensors. It was also shown that calling these meta functions may
incur in significant overhead.
Running the provided minimal reproducer (see issue), we see a speedup
close to 4.3x:
- Baseline: 0.0747s
- `XLA_DISABLE_FUNCTIONALIZATION=1`: 0.0159s
- `TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE=1`: 0.0175s
In summary, this PR:
- Creates the `disable_meta_reference()` function, which checks whether
the environment variable is set
- Modifies codegen for functionalization kernels, adding the call to
`disable_meta_reference()` function to the appropriate conditions
- Creates a new bash function for running `lazy/test_ts_opinfo.py` with
the environment variable set
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148822
Approved by: https://github.com/bdhirsh
Motivation
===
This PR is part of the plan of OneDNN Upstreaming, as #114848 [(comment)](https://github.com/pytorch/pytorch/issues/114848#issuecomment-2451553203) stated. The support of SDPA is via the overridable variance on XPU backend. Beside the added `Attention.cpp` file, `Graph.h` is added to hold utils for OneDNN graph including those for kernel/compile graph caching. In addition, a selection of testcases in `test/test_transformers.py` are copied into the new `test/xpu/test_transformers.py` and modified accordingly to provide additional tests beyond `./third_party/torch-xpu-ops/test/xpu/test_ops_xpu.py`.
Depends on OneDNN version v3.7 upgrade in #147498
Depends on BUILD_GRAPH switch in #147608
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147614
Approved by: https://github.com/jansel, https://github.com/EikanWang
Fixes https://github.com/pytorch/executorch/issues/8711
In ExecuTorch when we try to parse the following schema:
```
aten::__lshift__.Scalar(Tensor self, Scalar other) -> Tensor
```
Repro:
```python
from torchgen.model import FunctionSchema
native_schema = FunctionSchema.parse("aten::__lshift__.Scalar(Tensor self, Scalar other) -> Tensor")
```
It's failing because `BaseOperatorName` categorizes it to be a
inplace operator.
I understand we are not supposed to pass in namespace "aten::" into
`FunctionSchema.parse()` but unfortunately ExecuTorch requires this
feature to work.
This PR adds a new `namespace` attribute to `BaseOperatorName` and makes
sure the rest of the stack works as before, if a schema without
namespace is passed in
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148038
Approved by: https://github.com/bdhirsh
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:
Continuing the work from https://github.com/pytorch/pytorch/pull/146427
Adds the `torch.float8_e8m0fnu` dtype to PyTorch, as detailed in
https://github.com/pytorch/pytorch/issues/146414 . Please see the issue for a detailed definition of the format. Example of basic functionality:
```python
import torch
# round trip
x0 = torch.randn(4, 4, dtype=torch.float32)
x1 = x0.to(torch.float8_e8m0fnu) # RNE rounding
x2 = x1.to(torch.float32) # 2 ** exponent
# creation with empty
x0 = torch.empty(4, 4, dtype=torch.float8_e8m0fnu)
# printing
print(x0)
```
Done in this PR:
* numerical correctness
* op coverage (except for `torch._scaled_mm`): create tensor, cast to/from float32
* printing a tensor works
For future PRs:
* performance optimizations for casting
* torch._scaled_mm
* PT2
* various cleanups (detailed in comments with issue numbers)
Test Plan:
```
pytest test/quantization/core/experimental/test_float8.py -s
```
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147466
Approved by: https://github.com/drisspg
Adds a C-shim fallback for `set_.source_Tensor`, which is effectively required by `ir.SetSourceTensorKernel`. As a necessary prerequisite to use that IR node, updates `CppWrapperCpu` to handle in-place returns in C-shim ops (the arguments for those returns are silently dropped by `torchgen`).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145654
Approved by: https://github.com/desertfire
ghstack dependencies: #145095
This is one of a series of PRs to update us to PEP585 (changing Dict -> dict, List -> list, etc). Most of the PRs were completely automated with RUFF as follows:
Since RUFF UP006 is considered an "unsafe" fix first we need to enable unsafe fixes:
```
--- a/tools/linter/adapters/ruff_linter.py
+++ b/tools/linter/adapters/ruff_linter.py
@@ -313,6 +313,7 @@
"ruff",
"check",
"--fix-only",
+ "--unsafe-fixes",
"--exit-zero",
*([f"--config={config}"] if config else []),
"--stdin-filename",
```
Then we need to tell RUFF to allow UP006 (as a final PR once all of these have landed this will be made permanent):
```
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -40,7 +40,7 @@
[tool.ruff]
-target-version = "py38"
+target-version = "py39"
line-length = 88
src = ["caffe2", "torch", "torchgen", "functorch", "test"]
@@ -87,7 +87,6 @@
"SIM116", # Disable Use a dictionary instead of consecutive `if` statements
"SIM117",
"SIM118",
- "UP006", # keep-runtime-typing
"UP007", # keep-runtime-typing
]
select = [
```
Finally running `lintrunner -a --take RUFF` will fix up the deprecated uses.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145101
Approved by: https://github.com/bobrenjc93
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
Removes 4 fallback ops that are currently not possible to codegen, which does not break ABI-compatibility.
1. `_cudnn_rnn_backward` and `_histogramdd_bin_edges` both return `Tensor[]`, which we cannot codegen with the current design.
2. `_sparse_coo_tensor_with_dims_and_tensors` only supplies a Sparse operator, which we don't support.
3. `zeros.names` requires a `Dimname` input, which we can't currently codegen.
Removing these ops from the list will improve test performance, since the fallback op generation will use the Python proxy executor instead of calling non-existent C functions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143421
Approved by: https://github.com/desertfire
ghstack dependencies: #141371, #143223
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
