Threads inside the thread pools are not named, so they inherit the main process name or the name of the first thread. In our case if we set `pt_main_thread` as the thread name when a thread does `import torch`, this name will be inherited by all the threads in the created pools.
This PR names the threads in the pools I was able to find. There are other pools created, like OpenMP ones and we need to follow-up on those.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130270
Approved by: https://github.com/d4l3k, https://github.com/albanD
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
ghstack dependencies: #127122, #127123, #127124, #127125
If I do:
```
xla_device = xm.xla_device()
xla_tensor_0 = torch.tensor(42, dtype=torch.uint32).to(xla_device)
```
I got the error:
```
RuntimeError: false INTERNAL ASSERT FAILED at "/ansible/pytorch/torch/csrc/lazy/core/hash.h":139, please report a bug to PyTorch. Unsupported scalar type:UInt16
```
This PR intends to fix this issue.
The data type can be found in pytorch/c10/core/ScalarType.h.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125972
Approved by: https://github.com/JackCaoG
Update ruff to 0.4.1 .
This version fixes a lot false negatives/false positives, is 20-40% faster, and has various other bug fixes.
Below is a before and after table showing the execution time of ruff lint and ruff format in milliseconds courtesy of https://astral.sh/blog/ruff-v0.4.0
| Repository | Linter (v0.3) | Linter (v0.4) | Formatter (v0.3) | Formatter (v0.4) |
|----------------------------------------------------|---------------|---------------|------------------|------------------|
| [pytorch/pytorch](https://github.com/pytorch/pytorch) | 328.7 | 251.8 | 351.1 | 274.9 |
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124549
Approved by: https://github.com/ezyang
This PR proposes to use std::optional<Generator>& for underlying functions to avoid unnecessary copy and move operations. The torchgen code was changed to generate the new type.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120076
Approved by: https://github.com/malfet
This PR proposes to use std::optional<Generator>& for underlying functions to avoid unnecessary copy and move operations. The torchgen code was changed to generate the new type.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120076
Approved by: https://github.com/malfet
This PR proposes to use std::optional<Generator>& for underlying functions to avoid unnecessary copy and move operations. The torchgen code was changed to generate the new type.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120076
Approved by: https://github.com/malfet
This is a lot of files changed! Don't panic! Here's how it works:
* Previously, we set `follow_imports = silent` for our mypy.ini configuration. Per https://mypy.readthedocs.io/en/stable/running_mypy.html#follow-imports, what this does is whenever we have an import to a module which is not listed as a file to be typechecked in mypy, we typecheck it as normal but suppress all errors that occurred in that file.
* When mypy is run inside lintrunner, the list of files is precisely the files covered by the glob in lintrunner.toml, but with files in excludes excluded.
* The top-level directive `# mypy: ignore-errors` instructs mypy to typecheck the file as normal, but ignore all errors.
* Therefore, it should be equivalent to set `follow_imports = normal`, if we put `# mypy: ignore-errors` on all files that were previously excluded from the file list.
* Having done this, we can remove the exclude list from .lintrunner.toml, since excluding a file from typechecking is baked into the files themselves.
* torch/_dynamo and torch/_inductor were previously in the exclude list, because they were covered by MYPYINDUCTOR. It is not OK to mark these as `# mypy: ignore-errors` as this will impede typechecking on the alternate configuration. So they are temporarily being checked twice, but I am suppressing the errors in these files as the configurations are not quite the same. I plan to unify the configurations so this is only a temporary state.
* There were some straggler type errors after these changes somehow, so I fixed them as needed. There weren't that many.
In the future, to start type checking a file, just remove the ignore-errors directive from the top of the file.
The codemod was done with this script authored by GPT-4:
```
import glob
exclude_patterns = [
...
]
for pattern in exclude_patterns:
for filepath in glob.glob(pattern, recursive=True):
if filepath.endswith('.py'):
with open(filepath, 'r+') as f:
content = f.read()
f.seek(0, 0)
f.write('# mypy: ignore-errors\n\n' + content)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118414
Approved by: https://github.com/thiagocrepaldi, https://github.com/albanD
In certain edge cases when using lazy tensors, the base tensor stored in the `FunctionalStorageImpl` and the `value_` tensor stored in the `FunctionalTensorWrapper` diverge. For instance, take this simple example
```python
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.fc1 = torch.nn.Linear(4, 2, bias=False)
def forward(self, x):
return x @ self.fc1.weight.transpose(0, 1)
with torch.device("lazy"):
model = Model()
x = torch.ones(4)
out = model(x)
```
The call to `transpose` on the lazily initialized weight `fc1.weight` applies a view op on the functional tensor which only gets propagated to the functional tensor wrapper and not the base tensor in the storage. Thus, causing them to diverge.
To fix this behaviour, we need to reset the functional tensor's storage. To facilitate this, we add a `reset_storage` method to `FunctionalTensorWrapper` which clears away the old storage and view metas.
CC: @behzad-a @GlebKazantaev @wconstab @bdhirsh
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115235
Approved by: https://github.com/bdhirsh
Fixes the string_view errors and reland the work. The previous changes in torch/csrc/utils/invalid_arguments.cpp were too aggressive and not tested thoroughly. They are discarded.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110518
Approved by: https://github.com/ezyang
### Add python binding variables for enabling and disabling
These changes will be used in the pytorch/xla repository for lowering HLO for the AWS Neuron compiler. For correct tensor lowerings the device cache size must be set to zero.
It is advantageous to be able to enable and disable the cache without deleting it. This allows use of the XLA device, and HLO lowering in a single python file, isolating cache disablement to a python context.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107827
Approved by: https://github.com/JackCaoG, https://github.com/wconstab, https://github.com/bdhirsh
In almost all cases this is only included for writing the output formatter, which
only uses `std::ostream` so including `<ostream>` is sufficient.
The istream header is ~1000 lines so the difference is non-trivial.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106914
Approved by: https://github.com/lezcano
In almost all cases this is only included for writing the output formatter, which
only uses `std::ostream` so including `<ostream>` is sufficient.
The istream header is ~1000 lines so the difference is non-trivial.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106914
Approved by: https://github.com/lezcano
Now that we have full C++17 support, we can use if constexpr in some identified cases.
<!--
copilot:summary
-->
### <samp>🤖 Generated by Copilot at df4c16d</samp>
The pull request improves the performance, readability, and consistency of various function templates in the `ATen` and `torch` modules by using `constexpr` keywords and C++17 features. It also fixes some type conversion and overflow issues for different input and output types. The changes affect the code for distributions, BLAS, batch normalization, embedding bag, random number generation, vectorized operations, cuBLAS, XNNPACK, CUTLASS, and shape inference. The affected files include `DistributionsHelper.h`, `vec256_int.h`, `vec512_int.h`, `BlasKernel.cpp`, `IndexKernel.cpp`, `EmbeddingBag.cpp`, `Normalization.cpp`, `rng_test.h`, `vec_test_all_types.h`, `TransformationHelper.h`, `CUDABlas.cpp`, `DistributionKernels.cpp`, `DistributionTemplates.h`, `RangeFactories.cu`, `RangeFactories.cpp`, `qconv.cpp`, `StructuredSparseLinearCUTLASS.cu`, `vec_test_all_types.cpp`, and `shape_inference.cpp`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102471
Approved by: https://github.com/Skylion007, https://github.com/malfet
The strategy is that we will heap allocate a LargeNegativeIntSymNodeImpl whenever we have a large negative int, so that we can keep the old `is_symbolic` test (now called `is_heap_allocated`) on SymInt. Whenever we need to do something with these ints, though, we convert them back into a plain `int64_t` (and then, e.g., wrap it in whatever user specificed SymNodeImpl they need.) We cannot wrap directly in the user specified SymNodeImpl as we generally do not know what the "tracing context" is from C++. We expect large negative ints to be rare, so we don't apply optimizations like singleton-ifying INT_MIN. Here's the order to review:
* c10/core/SymInt.h and cpp
* `is_symbolic` renamed to `is_heap_allocated` as I needed to audit all use sites: the old `is_symbolic` test would return true for large negative int, but it would be wrong to then try to dispatch on the LargeNegativeIntSymNodeImpl which supports very few operations. In this file, I had to update expect_int,
* If you pass in a large negative integer, we instead heap allocate it in `promote_to_negative`. The function is written in a funny way to keep compact constructor code for SymInt (the heap allocation happens out of line)
* clone is now moved out-of-line
* New method maybe_as_int which will give you a constant int if it is possible, either because it's stored inline or in LargeNegativeIntSymNodeImpl. This is the preferred replacement for previous use of is_symbolic() and then as_int_unchecked().
* Rename toSymNodeImpl to toSymNode, which is more correct (since it returns a SymNode)
* Complete rewrite of `normalize_symints.cpp` to use new `maybe_as_int`. Cannot easily use the old code structure, so it's now done doing a macro and typing out each case manually (it's actually not that bad.)
* Reimplementations of all the unary operators by hand to use `maybe_as_int`, relatively simple.
* c10/core/LargeNegativeIntSymNodeImpl.h - Just stores a int64_t value, but it has to be big and negative. Most methods are not implemented, since we will rewrap the large negative int in the real SymNodeImpl subclass before doing operations with it
* The rest of the files are just rewriting code to use `maybe_as_int`. There is a nontrivial comment in c10/core/SymIntArrayRef.h
Very minor test adjustment in c10/test/core/SymInt_test.cpp . Plan to exercise this properly in next PR.
Companion XLA PR: https://github.com/pytorch/xla/pull/4882
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99157
Approved by: https://github.com/albanD
Number of OSS PR were reverted, because new signed-unsigned comparison warnings, which are treated as errors in some internal builds.
Not sure how those selective rules are applied, but this PR removes `-Wno-sign-compare` from PyTorch codebase.
The only tricky part in this PR, as making sure that non-ASCII character detection works for both signed and unsigned chars here:
6e3d51b08a/torch/csrc/jit/serialization/python_print.cpp (L926)
Exclude several files from sign-compare if flash attention is used, due to the violation in cutlass, to be fixed by https://github.com/NVIDIA/cutlass/pull/869
Do not try to fix sign compare violations in caffe2 codebase
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96723
Approved by: https://github.com/albanD
This PR introduces some modifications:
1. We find out some const function parameters that can be passed by reference and add the reference.
2. We find more opportunists of passing by value and change them accordingly.
3. Some use-after-move errors are fixed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95942
Approved by: https://github.com/Skylion007
Fixes for PyTorch/XLA functionalization integration
---
Some notable changes include:
- More asserts in `FunctionalTensorWrapper`, so bugs show up more cleanly in cases where we e.g. forget to wrap an output
- Make the *_scatter ops `CompositeExplicitAutogradNonFunctional`, so we get a better error message and XLA doesn't accidentally try to us them
- Fix LTC/XLA codegen in core to handle multi-tensor out= ops with no returns
- Better erroring: Allow XLA to use the CPU fallback from core in a way so that it always errors on view ops, which XLA should no longer see.
- Update MetaConverter to exclude XLA tensors in raising NotImplemented…
- Add `_propagate_xla_data` op
- Add meta tensor support for some ops
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94537
Approved by: https://github.com/bdhirsh
This PR do two things:
1. It moves some Windows warning suppression from various CMake files into the main CMakeList.txt, following the conventions of gcc and clang.
2. It fixes some Windows warnings in the source code. Most importantly, it fixes lots of dll warnings by adjusting C10_API to TORCH_API or TORCH_PYTHON_API. There are still some dll warnings because some TORCH_API functions are actually built as part of libtorch_python
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94927
Approved by: https://github.com/malfet
Not only is this change usually shorter and more readable, it also can yield better performance. size() is not always a constant time operation (such as on LinkedLists), but empty() always is.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/93236
Approved by: https://github.com/malfet
Replace cpp string comparisons with more efficient equality operators. These string comparisons are not just more readable, but they also allow for short-circuiting for faster string equality checks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92765
Approved by: https://github.com/ezyang
This clang-tidy check is disabled globally due to false positives on containers, but there are a few places here where adding clang-tidy would actually improve performance (by allowing STL containers to use the move operator / assignment)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92156
Approved by: https://github.com/ngimel
This makes it easier to narrow down who is throwing the error,
instead of having to use gdb.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: [D42088781](https://our.internmc.facebook.com/intern/diff/D42088781)
Applies various automated fixes that reduces the number of spurious copies in torch, aten, and c10. I also inlined any default dtors that would have made the type trivially destructible.
Follow up to #89000
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90629
Approved by: https://github.com/ezyang
The big idea is to add `create_unbacked_symfloat` and `create_unbacked_symint` to ShapeEnv, allowing you to allocate symbolic floats/ints corresponding to data you don't know about at compile time. Then, instead of immediately erroring out when you try to call local_scalar_dense on a FakeTensor, we instead create a fresh symint/symfloat and return that.
There a bunch of odds and ends that need to be handled:
* A number of `numel` calls converted to `sym_numel`
* When we finally return from item(), we need to ensure we actually produce a SymInt/SymFloat when appropriate. The previous binding code assumed that you would have to get a normal Python item. I add a pybind11 binding for Scalar (to PyObject only) and refactor the code to use that. There is some trickiness where you are NOT allowed to go through c10::SymInt if there isn't actually any SymInt involved. See comment.
* One of our unit tests tripped an implicit data dependent access which occurs when you pass a Tensor as an argument to a sizes parameter. This is also converted to support symbolic shapes
* We now support tracking bare SymInt/SymFloat returns in proxy tensor mode (this was already in symbolic-shapes branch)
* Whenever we allocate an unbacked symint, we record the stack trace it was allocated at. These get printed when you attempt data dependent access on the symint (e.g., you try to guard on it)
* Subtlety: unbacked symints are not necessarily > 1. I added a test for this.
These unbacked symints are not very useful right now as you will almost always immediately raise an error later when you try to guard on them. The next logical step is adding an assertion refinement system that lets ShapeEnv learn facts about unbacked symints so it can do a better job eliding guards that are unnecessary.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90624
Approved by: https://github.com/Skylion007, https://github.com/voznesenskym
Summary:
This pull request makes some LazyGraphExecutor private data structures protected such that XLAGraphExecutor can reuse them.
Here is the list:
1. DeviceLocker.
2. DeviceLockerArena.
3. DataCacheArena. In addition, it also introduces LazyGraphExecutor::ResetTrimCounter() such that XLAGraphExecutor can reuse the trim counter.
Test Plan:
CI.
P.S. This is to re-land #90457.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90598
Approved by: https://github.com/JackCaoG
