id int64 2.74B 3.05B | title stringlengths 1 255 | user stringlengths 2 26 | state stringclasses 2
values | labels listlengths 0 24 | comments int64 0 206 | author_association stringclasses 4
values | body stringlengths 7 62.5k ⌀ | is_title bool 1
class |
|---|---|---|---|---|---|---|---|---|
2,925,921,898 | ONNX: export failure ❌ 1.1s: Exporting the operator 'aten::fft_fft2' to ONNX opset version 17 is not supported. | EvelynCarter | closed | [
"module: onnx",
"triaged"
] | 12 | NONE | ### 🚀 The feature, motivation and pitch
Exporting the operator 'aten::fft_fft2' to ONNX opset version 18 is not supported.
Trying to convert torch model to onnx model.
How can I solve this problem?
(Da) D:\Create\YOLOv8-TensorRT-main>E:/anaconda3/envs/Da/python.exe d:/Create/YOLOv8-TensorRT-main/export.py
Ultralytics... | true |
2,925,878,988 | Migrate to new theme | svekars | closed | [
"oncall: distributed",
"module: docs",
"module: cpu",
"Merged",
"ciflow/trunk",
"topic: docs",
"ciflow/mps",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"release notes: distributed (checkpoint)",
"suppress-bc-linter"
] | 7 | CONTRIBUTOR | - Migrate pytorch docs, cpp docs and functorch docs to the pytorch_sphinx_theme2
- Migrate index.rst to markdown and restructure to use high-level horizontal bar sections Python API, Developer Notes
- Added python-api.md which becomes the main container for the API docs. This file will be used to add all api referenc... | true |
2,925,871,968 | [torch][ao] Do not crash numerics debugger if the shape of the tensors do not match | dulinriley | open | [
"fb-exported",
"ciflow/trunk",
"release notes: quantization",
"release notes: AO frontend"
] | 14 | CONTRIBUTOR | Summary: Occasionally we see the loss function to crash because the shapes of `a` and `b` tensors are different. This diff avoids crashing is such scenarios and lets the comparison work for other nodes where the shapes match.
Test Plan: - CI
Reviewed By: jerryzh168
| true |
2,925,868,583 | Expose GIL and GC events in profiler traces | cyme | open | [
"open source"
] | 3 | NONE | Fixes #ISSUE_NUMBER
| true |
2,925,846,769 | [codemod][lowrisk] Remove unused exception parameter from caffe2/aten/src/ATen/cuda/CUDABlas.cpp | r-barnes | closed | [
"oncall: jit",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: cpp",
"topic: improvements",
"topic: not user facing"
] | 10 | CONTRIBUTOR | Summary:
`-Wunused-exception-parameter` has identified an unused exception parameter. This diff removes it.
This:
```
try {
...
} catch (exception& e) {
// no use of e
}
```
should instead be written as
```
} catch (exception&) {
```
If the code compiles, this is safe to land.
Test Plan: Sa... | true |
2,925,822,081 | [MTIA] Support loading Tensors on mtia:0 for pytorch code | zimin2000 | closed | [
"oncall: jit",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: jit"
] | 14 | CONTRIBUTOR | Summary: The diff includes updates to the PyTorch code to enable loading tensors to MTIA.
Reviewed By: PatriceVignola
Differential Revision: D71176848
cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel | true |
2,925,805,553 | [BE]: Update mypy to 1.15 | Skylion007 | open | [
"open source",
"topic: not user facing"
] | 1 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,925,660,009 | ☂️ MPS support for large tensors | malfet | open | [
"module: crash",
"triaged",
"module: 64-bit",
"module: mps"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
Random ATen operations that use MPS backend can fail with `Error: total bytes of NDArray > 2**32` or other such errors that expect either tensor size to be less than 4Gb, or total number of elements to be indexable by 32-bit index.
This is umbrella issue to track those (should be searchable b... | true |
2,925,594,543 | Unguarded Usage of Facebook Internal Code? | BwL1289 | open | [
"triaged",
"module: third_party",
"oncall: pt2"
] | 1 | NONE | ### 🐛 Describe the bug
There is a [reference](https://github.com/pytorch/pytorch/blob/c7c3e7732443d7994303499bcb01781c9d59ab58/torch/_inductor/fx_passes/group_batch_fusion.py#L25) to `import deeplearning.fbgemm.fbgemm_gpu.fb.inductor_lowerings`, which we believe to be Facebook internal Python module based on descript... | true |
2,925,560,325 | DISABLED test_binary_op_with_scalar_self_support__foreach_pow_is_fastpath_True_cuda_complex128 (__main__.TestForeachCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: mta"
] | 9 | NONE | Platforms: linux, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_binary_op_with_scalar_self_support__foreach_pow_is_fastpath_True_cuda_complex128&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pyt... | true |
2,925,524,583 | [MTIA] Add _mtia_exchangeDevice to MTIA module | PatriceVignola | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 5 | CONTRIBUTOR | Summary: The FlexAttention path uses `_exchange_device`, so it will be needed eventually for MTIA as well.
Test Plan: `buck2 test fbcode//mtia/host_runtime/torch_mtia/tests:test_torch_mtia_api -- test_exchange_device`
Reviewed By: chaos5958
Differential Revision: D70072059
| true |
2,925,433,227 | [compile] Switch off inference mode during compilation | anijain2305 | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (fsdp)",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148953
* __->__ #149321
PR does following
* Turns `inference_mode` to False and `no_grad` for `convert_frame`, if the inference_mode is on globally.
* Turns off inference_mode for fake tensor prop. This ensures that converting from real ... | true |
2,925,397,867 | [DCP] Avoid in-place update and deepcopy during dudpe | saumishr | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: distributed (checkpoint)",
"oncall: distributed checkpointing"
] | 10 | CONTRIBUTOR | Summary:
Avoid in-place update and deepcopy during dudpe. Deepcopy becomes prohibitively expensive with models having a huge number of FQNs. This was manifestd in the Ads 2K experiment as well. Here are the results from the TextRay model in Mitra:
#### Control job with deepcopy regression:
First save ~24.8s
Globa... | true |
2,925,294,266 | Need some sort of pattern matcher recipes somewhere | zou3519 | open | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 4 | CONTRIBUTOR | I've been asked this 3x in the last week
cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @aakhundov | true |
2,925,156,351 | BypassFxGraphCache should have better error message for HOPs (also we should support more) | zou3519 | open | [
"triaged",
"oncall: pt2"
] | 1 | CONTRIBUTOR | torch._inductor.codecache.BypassFxGraphCache: Can't cache HigherOrderOperators
cc @chauhang @penguinwu | true |
2,925,128,104 | Refactoring Distributed test cases to be device agnostic [2/n] | AnantGulati | open | [
"oncall: distributed",
"triaged",
"open source",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Continuing the series from #145222
In this series of PR we intend to refactoring distributed test cases to enable to be completely device agnostic.
These changes will include the following approaches to do the same :
Allowing for multiple device types using instantiate_device_type_test
- Replacing calls to ... | true |
2,925,042,754 | Improve docker build cleanup on s390x runners | AlekseiNikiforovIBM | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | COLLABORATOR | Currently it sometimes still leaves a couple of processess running.
| true |
2,924,838,342 | How to Retain Computational Graph in torch.func.jvp() for Parameter Gradients? | edouardoyallon | open | [
"module: autograd",
"triaged",
"module: functorch"
] | 0 | NONE | ### 🚀 The feature, motivation and pitch
## Help Needed: Making `torch.func.jvp` Work with `torch.autograd.grad`
Hi all,
Thanks so much for all the functionalities of pytorch! I'm trying to make the following code valid (and efficient):
```python
output_values, output_grads = torch.func.jvp(model, input_value, inpu... | true |
2,924,828,478 | Inductor Incorrectly Handles `torch.view_copy` When Changing dtype | WLFJ | open | [
"triaged",
"module: viewing and reshaping",
"module: python frontend",
"module: edge cases",
"oncall: pt2",
"topic: fuzzer"
] | 3 | NONE | ### 🐛 Describe the bug
~The `torch.view_copy` function produces incorrect results in Eager mode when changing the `dtype` of a tensor. However, when using `torch.compile` (Inductor), the results are correct. This suggests a potential bug in the Eager implementation of `torch.view_copy`.~
Compile causes wrong answer ... | true |
2,924,788,689 | Training error: due to torch.load(weights, map_location='cpu') - Weights only load failed. | karishmathumu | open | [
"module: serialization",
"triaged"
] | 2 | NONE | ```
sudo apt-get update -y
sudo apt-get install -y python3-pip git
git clone https://github.com/WongKinYiu/yolov9.git /home/ubuntu/yolov9
pip3 install --upgrade 'numpy<2'
#pip install --upgrade torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1
pip3 install -r /home/ubuntu/yolov9/requirements.txt
cd yolov9
mkdir -p ... | true |
2,924,687,966 | Fix `lr_scheduler` unexpectedly calls `step()` when init argument last_epoch is larger than -1 | zeshengzong | open | [
"triaged",
"open source",
"topic: bug fixes",
"release notes: optim"
] | 5 | CONTRIBUTOR | Fixes #102261
## Changes
- Use flag `_is_initial` to replace `self.last_epoch == 0` condition to judge whether `lr` should be initial value
- Add test for `ExponentialLR` checkpoint usecase
## Test Result
```python
pytest -s test/optim/test_lrscheduler.py -vv
```
![image](https://github.com/user-atta... | true |
2,924,627,079 | Change profiler notation from 'b' to 'B' | NEGU93 | open | [
"oncall: profiler"
] | 0 | NONE | ### 🚀 The feature, motivation and pitch
Change profiler notation from 'b' to 'B' as we are talking about bytes.
Currently, profiles memory announces their consumption like 'Gb'. This is confusing as current notation says 'B' for Bytes and 'b' for bits. (Suffice to google "Bytes vs bits notation").
This generated con... | true |
2,924,362,230 | [windows MSVC] Linker receives broken paths | loscrossos | closed | [
"module: windows",
"triaged",
"oncall: pt2",
"oncall: cpu inductor"
] | 2 | NONE | ### 🐛 Describe the bug
i am using the torch library (using not compiling) with torch compile enabled though another library (Zonos).
at runtime the code tried to compile some code so i run it though the Visual Studio developer command line.
it crashes wiht this error:
```
File "c:\code\.env\lib\site-packages\to... | true |
2,924,316,429 | Enable max autotune for AOTInductor benchmark | zxd1997066 | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 28 | CONTRIBUTOR | With this PR, AOTinductor can choose to run into max-autotune mode when benchmarking.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames @chuanqi129
| true |
2,924,313,602 | Add RECORD_FUNCTION for aoti_xxx | shiyang-weng | closed | [
"triaged",
"open source",
"module: inductor"
] | 8 | CONTRIBUTOR | Fixes #148650
add RECORD_FUNCTION for aoti_xxx
Example:
```python
import torch
from torch.testing._internal.common_utils import run_tests, TemporaryFileName, TestCase
from torch.utils import ThroughputBenchmark
from contextlib import nullcontext
from torch._dynamo import config
from torch._inductor imp... | true |
2,924,292,859 | [Inductor] `isin` causes AssertionError in Inductor IR but the Case Works Fine In Eager Mode | WLFJ | closed | [
"triaged",
"oncall: pt2"
] | 2 | NONE | ### 🐛 Describe the bug
test case:
```python
import torch
print(torch.__version__)
def f(*args):
sym_0, sym_1, sym_2, sym_3 = args
var_941 = torch.arange(start=sym_0, end=sym_1, step=1)
return torch.isin(var_941, sym_2, assume_unique=sym_3)
res = f(0, 1024, 1, True,)
print('eager: ', res)
res = torc... | true |
2,924,269,241 | `context_parallel` fails for training with `RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation` | ydshieh | open | [
"oncall: distributed",
"triaged",
"module: context parallel"
] | 10 | NONE | ### 🐛 Describe the bug
Hi, I am from Hugging Face and we are trying to use `context_parallel` (using `stable` and `nightly torch`). However, for training, it fails with
> RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation
I have created a reproducible with ... | true |
2,924,262,398 | [Submodule] [cpuinfo] cpuinfo update | ozanMSFT | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/binaries_wheel",
"ciflow/binaries_libtorch"
] | 3 | COLLABORATOR | Updating `cpuinfo` module.
Relevant:
https://github.com/pytorch/cpuinfo/issues/270 | true |
2,924,253,699 | Machine with isolated cores is not respected by torch | oeliyahoo | open | [
"module: multiprocessing",
"module: cpu",
"triaged"
] | 4 | NONE | ### 🐛 Describe the bug
First define your last cores as isolated on your system
e.g in my machine:
lscpu output:
NUMA node0 CPU(s): 0-55,112-167
NUMA node1 CPU(s): 56-111,168-223
cat /sys/devices/system/cpu/isolated output:
52-55,108-111,164-167,220-223
I defined it with:
sudo nano /etc/default/grub
GRUB_... | true |
2,924,119,545 | Allow Privateuse1 device for validating the arguments to sparse compressed tensor factory functions | ClowDragon | closed | [
"triaged",
"module: PrivateUse1"
] | 2 | NONE | ### 🚀 The feature, motivation and pitch
https://github.com/pytorch/pytorch/commit/bb8c4ecc6d7d546d553c0883422c53705355236d
similar to this commit, can we allow privateuse1 device to pass this TORCH_CHECK?
### Alternatives
_No response_
### Additional context
_No response_
cc @NmomoN @mengpenghui @fwenguang @cdzh... | true |
2,924,090,242 | Reduce memory consumption in broadcast bmm | zheyishine | closed | [
"module: cuda",
"module: memory usage",
"triaged",
"module: linear algebra",
"needs design",
"matrix multiplication"
] | 4 | NONE | ### 🚀 The feature, motivation and pitch
Here is a minimal example, consumes about 66GiB CUDA memory (I guess it may expand `b` to [8192,32,1024,128] before calculation). Is it possible to reduce the memory consumption without expanding?
`a=torch.rand((8192,32,1,1024),dtype=torch.bfloat16,device='cuda:0')`
`b=torch.ra... | true |
2,924,073,498 | Unexpected results w/ LayerNorm -- suspecting possible memory issue? | Apex95 | open | [
"module: numerical-stability",
"module: cuda",
"triaged",
"module: 64-bit",
"module: norms and normalization"
] | 4 | NONE | ### 🐛 Describe the bug
I'm noticing an interesting behaviour of LayerNorm when applied to large 4d tensors (bf16) when normalized shape is an int (i.e., normalizing over the final dimension of the tensor).
What I'm seeing is that the size of the first dimension (batch size) can impact the normalized values of existi... | true |
2,924,049,332 | Update slow tests | pytorchupdatebot | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/slow",
"ci-no-td"
] | 3 | COLLABORATOR | This PR is auto-generated weekly by [this action](https://github.com/pytorch/pytorch/blob/main/.github/workflows/weekly.yml).
Update the list of slow tests. | true |
2,923,991,531 | update aotinductor doc for XPU support | jingxu10 | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 9 | COLLABORATOR | as title. Since the AOTInductor feature starting from 2.7 works on Intel GPU, add the related contents into its doc. | true |
2,923,955,014 | [invoke_subgraph] Support unbacked | angelayi | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 10 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149298
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames
Differential Revision: [D71420641](https://our.internmc.facebook.... | true |
2,923,954,905 | [invoke_subgraph] Support pending unbacked symint | angelayi | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149298
* __->__ #149297
* #149296
The "PendingUnbackedSymbolNotFound" error is when an unbacked symbol is created within a piece of code, but this symbol never appears in any of the outputs. I believe the original intention is to help catc... | true |
2,923,954,786 | [export] Add mark_compiled_region support | angelayi | closed | [
"Merged",
"ciflow/trunk",
"release notes: export"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149298
* #149297
* __->__ #149296
Differential Revision: [D71298930](https://our.internmc.facebook.com/intern/diff/D71298930) | true |
2,923,954,690 | [export] Patch dynamo configs when nonstrict tracing | angelayi | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: export"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149295
Differential Revision: [D71298929](https://our.internmc.facebook.com/intern/diff/D71298929) | true |
2,923,954,578 | [export] Add TracingContext | angelayi | closed | [
"Merged",
"ciflow/trunk",
"release notes: export"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149295
* __->__ #149294
TracingContext is added to all tracing locations -- in torch.export this is where we call make_fx (for training IR) and aot_export_module (for inference IR), and in run_decompositions where we call aot_export_module... | true |
2,923,921,839 | When resuming training from a breakpoint using FSDP, an Out of Memory (OOM) error will occur during the model loading process. | nomadlx | open | [
"oncall: distributed",
"triaged",
"module: fsdp"
] | 4 | NONE | ### 🐛 Describe the bug
The original training was completely normal and there was no Out of Memory (OOM) issue. However, if the training is interrupted and when I try to resume the training from the last checkpoint, an unexpected OOM will occur during the loading of the FSDP model.
Compared with the code for trainin... | true |
2,923,808,062 | [CPU] Fix ARM float32 fmsub | zhangfeiv0 | closed | [
"triage review",
"triaged",
"module: vectorization",
"module: correctness (silent)",
"module: arm"
] | 7 | CONTRIBUTOR | ### 🐛 Describe the bug
In `vec_base.h`, the [fmsub](https://github.com/pytorch/pytorch/blob/916e8979d3e0d651a9091732ce3e59da32e72b0e/aten/src/ATen/cpu/vec/vec_base.h#L987) function implements the functionality of `a * b - c`. However, in `vec128_float_neon.h`, it uses [vfmsq_f32](https://github.com/pytorch/pytorch/bl... | true |
2,923,793,179 | [xpu] Adds XPU support in OffsetBasedRNGTracker | pkourdis | closed | [
"oncall: distributed",
"open source",
"topic: not user facing"
] | 5 | NONE | Met this error during training with torchtitan:
```
x4008c6s6b0n0.hostmgmt2008.cm.aurora.alcf.anl.gov 6: [rank6]: File "/lus/flare/projects/Aurora_deployment/pkourdis/conda/envs/pytorch/lib/python3.12/site-packages/torch/distributed/tensor/_random.py", line 82, in manual_seed
x4008c6s6b0n0.hostmgmt2008.cm.aurora.a... | true |
2,923,656,353 | as_subclass doesn't work under TorchDispatchMode | pritamdamania87 | closed | [
"triaged",
"module: __torch_dispatch__"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
We have a torch.Tensor subclass which shares autograd history with a passed in `data` torch.Tensor using the `as_subclass` method. This works well except in the case where we use `TorchDispatchMode`:
```
import torch
from torch.utils._python_dispatch import TorchDispatchMode
class Foo(TorchDi... | true |
2,923,635,342 | Fix the invalid link for FX | FFFrog | closed | [
"open source",
"Merged",
"topic: not user facing"
] | 4 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149289
As the title stated. | true |
2,923,626,898 | add support for numpy | tugsbayasgalan | open | [
"fb-exported",
"ciflow/inductor",
"release notes: export"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149288
* #148488
* #148485
Differential Revision: [D71294355](https://our.internmc.facebook.com/intern/diff/D71294355/) | true |
2,923,534,776 | [AOTI][refactor] Remove dead code | desertfire | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149287
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,923,465,903 | Replace c10::is_pod with std::is_trivial | cyyever | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 7 | COLLABORATOR | These remaining c10::is_pod calls can be replaced without compromising the semantics. | true |
2,923,462,583 | added documentation for masked_fill and masked_fill_ | julurisaichandu | open | [
"triaged",
"open source",
"release notes: python_frontend",
"topic: docs"
] | 6 | NONE | Fixes #149284
Added examples in the documentation of masked_fill and masked_fill_
| true |
2,923,461,946 | No examples in documentation for masked_fill and masked_fill_ | julurisaichandu | open | [
"module: docs",
"triaged",
"topic: docs"
] | 0 | NONE | ### 📚 The doc issue
No examples in documentation for masked_fill and masked_fill_
masked_fill - https://pytorch.org/docs/stable/generated/torch.Tensor.masked_fill.html
masked_fill_- https://pytorch.org/docs/stable/generated/torch.Tensor.masked_fill_.html
### Suggest a potential alternative/fix
add example functions... | true |
2,923,372,775 | Avoid unnecessary clone in torch.cuda.set_rng_state | ppwwyyxx | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 5 | COLLABORATOR | Clone has performance issue according to https://github.com/NVIDIA/Megatron-LM/blob/f49c3eb6e6d55ab6ffb085cb48d95c2d40276c3f/megatron/core/tensor_parallel/random.py#L77-L80 | true |
2,923,312,684 | [cuDNN][SDPA] cuDNN SDPA refactor/cleanup, nested tensor backward, test priority bump for `sm90`, `sm100` | eqy | open | [
"triaged",
"open source",
"topic: not user facing",
"module: sdpa"
] | 8 | COLLABORATOR | cleanup tuple/tensor boilerplate in cuDNN SDPA, preparation for nested/ragged tensor backward | true |
2,923,289,434 | "Significant" Numerical differences for different tensor shapes in CUDA | HaoxiangYou | closed | [
"module: numerical-stability",
"triaged"
] | 1 | NONE | ### 🐛 Describe the bug
I get a very different results when I do calculation on cuda with tensor operation order, below is a sample code for reproduce:
```
import torch
import torch.nn as nn
sequence_size = 32
env_size = 64
input_dim = 39
hidden_dim = 64
output_dim = 6
device = "cuda"
torch.backends.cudnn.determini... | true |
2,923,262,104 | [ROCm] support torch._C._set_sm_carveout_experimental - Parity with Nvidia | OrenLeung | open | [
"module: rocm",
"triaged"
] | 10 | CONTRIBUTOR | ### 🐛 Describe the bug
Hi @hliuca
On Nvidia, they support `torch._C._set_sm_carveout_experimental` for better compute-comms overlapping. this is useful during bwd pass of DDP and fwd/bwd pass of FSDP to ensure there is enough available `SM/CUs` for the rccl comms kernels to not be blocked by compute kernels that use... | true |
2,923,244,020 | CUDA Assertion Error in Scatter Operation During Training (RTX5090 cu128) | bstartek | open | [
"needs reproduction",
"module: windows",
"module: cuda",
"triaged",
"module: scatter & gather ops"
] | 6 | NONE | ### 🐛 Describe the bug
Description:
I encountered a CUDA error: device-side assert triggered while training nnUNetv2 using PyTorch Nightly (ci128) on an RTX 5090. The error occurs in ScatterGatherKernel.cu:367, suggesting that an index is out of bounds in a scatter operation. This leads to a crash in the loss calcula... | true |
2,923,238,775 | `torch.where` (`torch.nonzero(..., as_tuple=True)`) silently produces incorrect outputs with `torch.compile` | tohtana | closed | [
"triaged",
"oncall: pt2"
] | 2 | NONE | ### 🐛 Describe the bug
Hi PyTorch and `torch.compile` developers,
Thank you for your excellent work on `torch.compile`. I've been greatly enjoying its features.
I recently noticed that `torch.where` (`torch.nonzero(..., as_tuple=True)`) silently produces incorrect outputs when it is compiled with Inductor and `capt... | true |
2,923,199,047 | Fix spelling | TheodoreEhrenborg | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: sparse"
] | 7 | CONTRIBUTOR | null | true |
2,923,177,142 | ONNX: export failure ❌ 1.1s: Exporting the operator 'aten::fft_fft2' to ONNX opset version 17 is not supported. | EvelynCarter | closed | [
"module: onnx",
"triaged"
] | 1 | NONE | ### 🐛 Describe the bug
(Da) D:\Create\YOLOv8-TensorRT-main>E:/anaconda3/envs/Da/python.exe d:/Create/YOLOv8-TensorRT-main/export.py
Ultralytics YOLOv8.2.50 🚀 Python-3.10.16 torch-2.2.2+cu121 CPU (Intel Core(TM) i7-9750H 2.60GHz)
YOLOv8-SOEP summary (fused): 194 layers, 3307670 parameters, 0 gradients, 11.8 GFLOPs
P... | true |
2,923,129,981 | torch.onnx.export Dynamic shapes output not working | ducknificient | closed | [] | 3 | NONE | ### 🐛 Describe the bug
export not working when using `nvidia/nv-embed-v2` model, if i remove `sentence_embeddings` from the input, it would make the input static
```python
!pip install datasets==3.3.2
!pip install onnx==1.17.0 onnxscript==0.2.2 torch==2.6.0 torchvision==0.21.0 onnxruntime-gpu==1.21.0
!git clone htt... | true |
2,923,122,269 | `Floating point exception` in `torch.mkldnn_max_pool2d` | default1360 | closed | [
"module: error checking",
"triaged",
"module: mkldnn",
"module: empty tensor",
"topic: fuzzer"
] | 3 | NONE | ### 🐛 Describe the bug
Running the following PyTorch code results in a `Floating point exception` crash:
```
import torch
x = torch.randn(2, 64, 32, 32).to_mkldnn()
out2 = torch.mkldnn_max_pool2d(x, kernel_size=3, stride=0)
```
### Versions
PyTorch version: 2.6.0+cu124
Is debug build: False
CUDA used to build PyTo... | true |
2,923,088,127 | PyTorch Appears Locked Behind a Paywall on Pydroid 3 (Possible BSD License Violation) | MEME-KING16 | closed | [
"triage review"
] | 5 | NONE | I’ve noticed something concerning regarding PyTorch usage on the Android app Pydroid 3 (available on the Google Play Store). It seems that PyTorch, despite being open-source software under the BSD 3-Clause License, is being offered only as a premium feature requiring payment within the app.
Specifically, when trying t... | true |
2,922,947,159 | Make Subset dataset a true wrapper | adamjstewart | open | [
"triaged",
"open source",
"release notes: dataloader"
] | 4 | CONTRIBUTOR | Imagine you have a custom `FooDataset` class like so:
```python
from torch.utils.data import Dataset, random_split
class FooDataset(Dataset):
url = '...'
def plot(self, x):
...
```
You want to make train/val/test splits of this dataset like so:
```python
dataset = FooDataset(...)
train_da... | true |
2,922,940,517 | Fix unexpected keyword argument 'mode' when calling `CompileCounterWithBackend` | GdoongMathew | open | [
"open source",
"topic: not user facing",
"module: dynamo"
] | 20 | CONTRIBUTOR | Fixes #149209
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,922,934,377 | Add default XPU toolkit path to CMake | guangyey | closed | [
"open source",
"Merged",
"ciflow/trunk",
"release notes: xpu"
] | 12 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149270
# Motivation
Add default XPU runtime path to CMake to mitigate https://github.com/pytorch/pytorch/issues/149075
This ensures proper linking with `libtorch` when a user does not source the Torch XPU toolkit while working on... | true |
2,922,708,295 | Add Optimizer: Sharpness Aware Minimization | divyansha | open | [
"module: optimizer",
"triaged"
] | 2 | NONE | ### 🚀 The feature, motivation and pitch
[Sharpness Aware Minimization](https://arxiv.org/abs/2010.01412) is an optimizer that has been shown to improve robustness and model generalization. While open source implementations are available, SAM has not been added to the PyTorch library yet.
It would be great if we adde... | true |
2,922,707,327 | Fix mps scaled dot attention | rakshekaraj | open | [
"module: cpu",
"triaged",
"open source",
"module: amp (automated mixed precision)",
"release notes: quantization",
"release notes: mps"
] | 5 | NONE | Fixes #149261
Previously, computations on large sequence lengths (seq_len > 12288) were failing due to excessive memory usage.
My solution:
Implemented Chunking for Memory-Efficient Computation:
-Instead of processing the full tensor in one step, we split operations into chunks (chunk_size = 4096).This avoids m... | true |
2,922,674,805 | [REPLACED WITH SPLITTED STACK] Cache kernel code generation in TritonTemplate.generate() | laithsakka | open | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149267
In a model, we see ~~ 40% of the time in mm/addmm tuning. The model have 2000 mm,
many of which receives the same input shapes.
with autotune enabled, this become expensive, while we already cache auto tuning results, ... | true |
2,922,670,312 | [Pushed as separate PR on different stack] cache kernel codegen and loading | laithsakka | closed | [
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149266
* #148899
* #148893
* #148872
* #148742
* #148815
* #148809
* #148430
| true |
2,922,652,833 | [MPS/metal] Add missing `inline` to function definitions. | dcci | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps"
] | 3 | MEMBER | null | true |
2,922,650,450 | [MPS] Add support for modified_bessel_i0 in eager. | dcci | closed | [
"Merged",
"topic: improvements",
"module: mps",
"release notes: mps",
"ciflow/mps"
] | 5 | MEMBER | cc @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen | true |
2,922,571,965 | [MPS][BE] Move common binary ops macros to indexing.h | malfet | closed | [
"Merged",
"release notes: mps",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149263
* #149262
And binary op invocation logic to OperationUtils.mm
This is a no-op change, additional sanity checks/logic improvements will be added as followups | true |
2,922,555,350 | [EZ][BE] Reuse `result_of` from `c10/metal/utils.h` | malfet | closed | [
"Merged",
"topic: not user facing",
"release notes: mps",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149263
* __->__ #149262
No need for one more implementation | true |
2,922,480,434 | MPS Error: NDArray > 2^32 bytes in scaled_dot_product_attention | HFDLYS | open | [
"triaged",
"module: 64-bit",
"module: mps",
"module: sdpa"
] | 0 | NONE | ### 🐛 Describe the bug
An error, `/AppleInternal/Library/BuildRoots/d187755d-b9a3-11ef-83e5-aabfac210453/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShaders/MPSCore/Types/MPSNDArray.mm:850: failed assertion [MPSNDArray initWithDevice:descriptor:isTextureBacked:] Error: total bytes of NDArray > 2**32'`, appea... | true |
2,922,271,830 | ERROR: Could not find a version that satisfies the requirement torch==2.5.1 | TheNotary | closed | [] | 4 | NONE | ### 🐛 Describe the bug
I'm having trouble installing recent versions of pytorch using pyenv on mac. It's seems my pip isn't able to see any versions past 2.2.2.
```
# My usual commands to setup a virtualenv
$ python -m venv .venv
$ source .venv/bin/activate
$ pip cache purge
$ pip install --upgrade pip
$ wh... | true |
2,922,250,508 | Fix memory leak in subproc_pool future | masnesral | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149259
Summary: The future holds a reference to the callback, and the callback captures the outer future. Seems to create a cycle that the garbage collector doesn't clean up. Verified by compiling 15k synthetic Triton kernels and obs... | true |
2,922,232,536 | Auto-selective activation checkpointing is not optimal for speed (issue with min_cut_rematerialization_partition) | efsotr | open | [
"triaged",
"oncall: pt2",
"module: pt2-dispatcher"
] | 10 | NONE | ### 🐛 Describe the bug
I try the new api described in [pytorch blog: selective activation checkpointing](https://pytorch.org/blog/activation-checkpointing-techniques/#compile-only-memory-budget-api-new)
.
Then I find that selective activation checkpointing is not optimal for speed.
A minimal reproducer:
```python
i... | true |
2,922,224,705 | [BE]: Apply ruff PERF403 to use dict comprehensions more often | Skylion007 | closed | [
"oncall: distributed",
"open source",
"better-engineering",
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: quantization",
"release notes: releng",
"fx",
"module: inductor",
"ciflow/inductor",
"release notes: distributed (checkpoint)",
"ci-no-td"
] | 8 | COLLABORATOR | Fixes #ISSUE_NUMBER
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv @voznesenskym @penguinwu @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,922,204,114 | Broken link | felipemello1 | closed | [
"module: docs",
"triaged"
] | 1 | NONE | ### 📚 The doc issue
https://pytorch.org/docs/stable/distributed.checkpoint.html
<img width="515" alt="Image" src="https://github.com/user-attachments/assets/5a59211d-c138-4bc2-9fc8-f020d11b15b9" />
I think that the torchtitan link should be: https://github.com/pytorch/torchtitan/blob/main/torchtitan/components/chec... | true |
2,922,188,791 | second-half | rec | closed | [
"module: inductor",
"ciflow/inductor"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149255
* #149210
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,922,167,909 | [BE][Ez]: Update CU126 to CUDNN 12.8 too | Skylion007 | closed | [
"open source",
"better-engineering",
"Merged",
"Reverted",
"ciflow/binaries",
"ciflow/trunk",
"topic: not user facing",
"ci-no-td"
] | 24 | COLLABORATOR | Have CUDNN have the same version for 12.6 and 12.8 for better performance and consistency. We can't do CU12.1 because it's not supported and CU12.4 isn't updated due to manywheel Linux compatibility reasons and dropping support for it. | true |
2,921,983,884 | Add batch dim sharding rule to sdpa | fmassa | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 3 | MEMBER | This is a trivial rule that for most cases isn't needed, but if we want to consider that the input data is actually `Shard(0)` (instead of `Replicated()` as it is currently assumed), then we need this rule.
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,921,917,669 | INTERNAL ASSERT FAILED in `torch.autograd` | x0w3n | closed | [
"module: autograd",
"triaged",
"module: assert failure",
"actionable"
] | 1 | NONE | ### 🐛 Describe the bug
torch.autograd raises a INTERNAL ASSERT FAILED accompanied by the message: "please report a bug to PyTorch."
minimal example:
```python
import torch
class CustomRepeatInterleave(torch.autograd.Function):
@staticmethod
def forward(ctx, input, repeats):
ctx.repeats = repeats
... | true |
2,921,910,989 | false INTERNAL ASSERT FAILED in `torch.jit.trace` | x0w3n | open | [
"oncall: jit"
] | 0 | NONE | ### 🐛 Describe the bug
torch.jit.trace raises a false INTERNAL ASSERT FAILED when trace the model, accompanied by the message: "please report a bug to PyTorch."
minimal example:
```python
import torch
import torch.nn as nn
import torch.nn.parameter as parameter
# Step 1: Define the PyTorch function
class StatefulMo... | true |
2,921,887,170 | Implement batching for torch.isin operator | nikhilgv9 | open | [
"triaged",
"module: vmap",
"module: functorch"
] | 0 | NONE | ### 🚀 The feature, motivation and pitch
Received the following error while invoking vmap() on a function making use of `torch.inis` operator.
```
...Temp/ipykernel_20808/3722652185.py#line=49)
: UserWarning: There is a performance drop because we have not yet implemented the batching rule for aten::isin.Tensor_Tenso... | true |
2,921,849,715 | [AOTInductor] [BE] Add macro for loading symbols in aoti runner | muchulee8 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149161
* __->__ #149249
Summary:
Add macro for loading symbols in aoti runner
Test Plan:
Existing tests
Reviewers:
Subscribers:
Tasks:
Tags: | true |
2,921,830,948 | Fix multiprocessing with CUDA_VISIBLE_DEVICES seems to give the wrong device | fzyzcjy | open | [
"triaged",
"open source",
"release notes: distributed (miscellaneous)"
] | 3 | CONTRIBUTOR | Fixes #149196
This is merely a proof-of-concept PR. I would like to hear a bit of feedback - is the direction acceptable - before working on it deeper.
Things that will be added if the direction of PR looks acceptable: Unit tests, caches, implement-in-C++ (to speedup), etc.
| true |
2,921,740,810 | [Async TP] More robust support for rowwise scales when fusing matmul reduce-scatter | danielvegamyhre | closed | [
"oncall: distributed",
"Merged",
"release notes: distributed (pipeline)",
"module: inductor",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Part of https://github.com/pytorch/torchtitan/issues/866
## Context
- Async TP needs to support the "reshape -> scaled_mm -> reshape" pattern because scaled mm only supports 2D input tensors and 2D scales.
- (a,b,c) => (a*b,c)
- (a\*b,c) @ (c,d) = (a\*b,d)
- (a\*b,d) => (a,b,d)
- Currently the i... | true |
2,921,641,615 | Not generate custom obj json when it's empty | yushangdi | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 7 | CONTRIBUTOR | Summary: as title.
See internal Diff summary for more context.
Test Plan: buck run @fbcode//mode/dev-nosan //caffe2/test/inductor:torchbind -- -r config_not_generated
Differential Revision: D71241676
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wen... | true |
2,921,608,816 | [ROCm] Fixes and improvements to CUDA->HIP flag conversion for CPP extensions | naromero77amd | closed | [
"module: rocm",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/rocm"
] | 13 | COLLABORATOR | Fixes https://github.com/ROCm/hip/issues/3764.
Fixes and improvements to CUDA->HIP flag conversion for CPP extensions
- Log flag conversion for debugging purposes.
- Fix cases where it should not touch the -I flags or cases where CUDA appears more than once by replacing only the first instance.
- Fix case where... | true |
2,921,597,904 | torch.sum_of_squares() | ad8e | open | [
"triaged",
"module: python frontend"
] | 6 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
It would be equivalent to `torch.linalg.vector_norm(..., ord=2) ** 2`.
In my codebase, 14/18 of the norm calls have `** 2` immediately after. So the sum of squares is more common than the vector norm.
This is slightly faster because `vector_norm` calls `sqrt`, which is extran... | true |
2,921,565,160 | Fix AOTI update_constant_buffer issue. | henryoier | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: cpp",
"ciflow/inductor"
] | 13 | CONTRIBUTOR | Summary:
In D69553929 we changed the logic of constant & buffer update in AOTI. However this is incompatible with current Sigmoid runtime since we have different logics to pass in buffers, resulted in errors like
```
I0310 17:29:24.456960 3679102 AOTIDelegateExecutor.cpp:89] AOTIDelegateExecutor processing weights
... | true |
2,921,546,438 | [AOTI][XPU] Fix: model_container_runner_xpu.cpp is not built into libtorch_xpu.so | pytorchbot | closed | [
"open source",
"topic: not user facing"
] | 4 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149175
The missing of model_container_runner_xpu.cpp will cause compilation failure when user build CPP inference application on XPU.
| true |
2,921,539,670 | [DO NOT LAND] Padded Tensor PoC | BoyuanFeng | closed | [
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 1 | CONTRIBUTOR | Initial implementation of padded tensor. We use FakeTensor for shape propagation.
## Example 1
```python
import torch
from padded_tensor import PaddedTensor
@torch.compile(fullgraph=True)
def f(x):
x1 = x + 1
return x1 * 2
def run(shape):
x = torch.randn(*shape, device="cuda")
pad_x =... | true |
2,921,503,187 | [export] Minor refactor to trace.py | angelayi | closed | [
"Merged",
"ciflow/trunk",
"merging",
"release notes: export"
] | 7 | CONTRIBUTOR | Minor refactor to trace.py
* Removed `_strict_export_lower_to_aten_ir` in favor of just `_strict_export` and `_non_strict_export`
* Matched the APIs of `_strict_export` and `_non_strict_export`
* Instead of a `lower_to_aten_callback` which is a callable, or `dispatch_tracing_mode`, both functions take in a `_to_... | true |
2,921,482,553 | Fix torchbind schema str generation | yushangdi | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Summary: Fix Torchbind HOP schema generation when there's no input
Test Plan:
```
buck run fbcode//mode/dev-nosan //caffe2/test/inductor:torchbind -- -r schema
```
Differential Revision: D71231164
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisun... | true |
2,921,472,944 | [Reland] First version of statically compiled launcher for triton compiled CUDA kernels | jamesjwu | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148890
* __->__ #149238
This is a new version of https://github.com/pytorch/pytorch/pull/148561 fixing the ROCM test failure
Putting this up for a first pass review, though I will likely make a bunch of changes before landing to add mo... | true |
2,921,469,668 | [EZ][BE] Remove cross-compilation options from mac-build.yml | malfet | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149237
It has long been gone | true |
2,921,467,828 | [ARM64][CUDA] skip string pattern matching in `test_workspace_allocation_error` | eqy | closed | [
"open source",
"module: arm",
"Merged",
"module: cuda graphs",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | COLLABORATOR | `unwind()` on ARM64 seems to elide the strings of interest
cc @malfet @snadampal @milpuz01 @mcarilli @ezyang @eellison @penguinwu @BoyuanFeng @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,921,447,063 | [export] specialize for aten.to | pianpwk | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: quantization",
"ciflow/inductor",
"release notes: export"
] | 11 | CONTRIBUTOR | Changes decomposition behavior of `aten.to` to respect the aliasing/non-aliasing behavior in eager, and to specialize to the input/conversion dtype & device.
Before change: we always decompose `aten.to` into `_to_copy`, regardless of aliasing behavior. This leads us to ban mutations on the result of `_to_copy` when ... | true |
2,921,404,957 | [BE] Parametrize `TestMPS.test_binops_dtype_precedence` | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149234
* #149233
No op change, just splits a longer tests into a series of a smaller ones | true |
2,921,404,910 | [MPS] Fix type promotion for `torch.floor_divide` | malfet | closed | [
"Merged",
"release notes: mps",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149234
* __->__ #149233
And delete some duplicating glue code by relying on the stub
After this change `torch.arange(10, device = 'mps') // torch.arange(10., device='mps')` will return tensor of floats, which is a common dtype for float + i... | true |
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