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 |
|---|---|---|---|---|---|---|---|---|
3,029,037,729 | submodules: point gloo to new home in pytorch/ | d4l3k | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | MEMBER | Gloo moved to the PyTorch GitHub org. This updates PyTorch to point to the new location.
https://github.com/pytorch/gloo
Test plan:
CI | true |
3,029,036,649 | `nn.CrossEntropyLoss` accepts negative target probabilities | meilame-tayebjee | open | [
"module: performance",
"module: nn",
"module: error checking",
"triaged"
] | 1 | NONE | ### 📚 The doc issue
The `CrossEntropyLoss` [documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html) mentions that:
> **Target**:
> - If containing **class indices**, the shape should be:
> - `()` (scalar),
> - `(N)`, or
> - `(N, d1, d2, ..., dK)` with `K ≥ 1` (for K-dimensio... | true |
3,028,999,374 | [MPSInductor] Make sure sizevars are computed | malfet | closed | [
"Merged",
"topic: bug fixes",
"release notes: mps",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152436
* #152430
Before calling the kernel
This fixes `GPUTests.test_float_repr_dynamic_shapes_mps`
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @che... | true |
3,028,966,850 | Fx Graph cache hit generates guards that does not exists in the original cached program causing recompilations only at cache hit. | laithsakka | open | [
"triaged",
"oncall: pt2",
"module: dynamic shapes",
"module: inductor",
"module: compile-time",
"compile-cache",
"recompilations"
] | 3 | CONTRIBUTOR | repo:
run the following graph **twice** without fresh inductor cache
```
import math
@torch.compile(dynamic=True)
def func(x):
y= math.ceil((x.numel() // 5) / (math.ceil(math.sqrt(x.numel())))) > 64
if y:
return x*5,y
else:
return x*10,y
# with fresh_inductor_cache():
func(torch.rand(1... | true |
3,028,923,628 | [pt2] [AOTAutogradCache] Allow users to specify non torch functions as cacheable | jamesjwu | open | [
"triaged",
"oncall: pt2"
] | 0 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
Discussion on https://github.com/pytorch/pytorch/pull/152369 shows that users want the ability to add their own cacheable functions to the list of safe torch functions. The functions here need to already be allowed by the dynamo graph. In order for that to be fully safe, we sho... | true |
3,028,881,394 | [conda] Remove conda from lint-autoformat.yml | clee2000 | closed | [
"Merged",
"topic: not user facing",
"ciflow/autoformat"
] | 3 | CONTRIBUTOR | Installs setuptools since I get
https://github.com/pytorch/pytorch/actions/runs/14736804186/job/41364832984#step:5:60
```
+ python3 -m tools.generate_torch_version --is_debug=false
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _r... | true |
3,028,825,414 | [ROCm] cpp_extension allow user to override default flags | jithunnair-amd | open | [
"module: rocm",
"open source",
"release notes: rocm",
"ciflow/rocm"
] | 5 | COLLABORATOR | We need -fgpu-rdc for projects such as DeepEP + rocSHMEM. The default of -no-gpu-rdc doesn't work for such cases.
As per https://github.com/pytorch/pytorch/pull/152432#issuecomment-2840899088:
"rocshmem shares the same global variable in different files, as deepEP uses CUDAExtention to build the project https://git... | true |
3,028,822,721 | [conda] Remove conda usage from upload test stats while running workflow | clee2000 | closed | [
"Merged",
"topic: not user facing"
] | 3 | CONTRIBUTOR | The original uses python 3.10 and the base is 3.9 but I think that's ok | true |
3,028,799,573 | [MPSInductor] Fix type promotion in `_print_Max` | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152436
* __->__ #152430
Run into this problem while re-enabling `test_float_repr_dynamic_shapes`, where `_print_Max` were called for integer and long argument which resulted in the following compilation error
```
error: call to 'max' is amb... | true |
3,028,661,134 | Fix shadow local variables | dsjohns2 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 4 | CONTRIBUTOR | Summary: Fixing shadow local variables error: P1798875650
Test Plan: CI
Differential Revision: D73853605
| true |
3,028,625,632 | Remove unused Manylinux2014 Docker files and builds | atalman | closed | [
"Merged",
"topic: not user facing"
] | 6 | CONTRIBUTOR | Related to Manylinux 2.28 migration: https://github.com/pytorch/pytorch/issues/123649
Cleanup old Docker files and `manylinuxaarch64-builder:cpu-aarch64` image which has been replaced by `manylinux2_28_aarch64-builder:cpu-aarch64` | true |
3,028,574,555 | Add switch to disable truncation to long list print | sanshang-nv | open | [
"oncall: distributed"
] | 3 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
All numbers are needed for un even all2all bw calcuation. Need one method to disable this truncation.
### Alternatives
_No response_
### Additional context
For bw calcuation and other ... | true |
3,028,473,132 | [Manylinux 2.28] Migrate Docker container to use gcc 13, CUDA 12.6 and gcc14 CUDA 12.8 | atalman | open | [
"module: binaries",
"triaged"
] | 4 | CONTRIBUTOR | ### 🐛 Describe the bug
Latest manylinux_2_28 (AlmaLinux 8 based) containers are using GCC 14 : https://github.com/pypa/manylinux#manylinux_2_28-almalinux-8-based
Our Docker build is still using GCC 11: https://github.com/pytorch/pytorch/blob/main/.ci/docker/manywheel/Dockerfile_2_28#L10
Lets migrate GCC version to d... | true |
3,028,437,855 | Silent incorrectness between static torch.compile vs eager | bobrenjc93 | open | [
"high priority",
"triaged",
"module: correctness (silent)",
"module: functionalization",
"oncall: pt2",
"module: inductor",
"module: pt2-dispatcher",
"ubn"
] | 5 | CONTRIBUTOR | Similar to #151799 but this time static compile produces the wrong output
```
import torch
def expand(x, n):
a = x.expand((n,))
a[-1] = 3
return a
def f(n: int, device: str):
numbers = torch.arange(2, device=device)
for i in range(len(numbers)):
expanded = expand(numbers[i], n)
pr... | true |
3,028,167,873 | Relax tolerance for test_quick_baddbmm_cpu_complex64 | Flamefire | open | [
"open source",
"ciflow/trunk",
"topic: not user facing"
] | 5 | COLLABORATOR | On Zen 2 (AMD EPYC) and Intel Sapphire Rapids this fails with small differences when compiled with native targeted optimizations. I.e. it fails with `-march=znver2` but succeeds with `-march=znver1`.
I assume some operator fusing is being used by GCC. Small differences like using `vmovdqa` can be seen in the minimiz... | true |
3,028,145,626 | Invalid handling of nans in compiled torch.quantile / torch.nanquantile on cuda | RoepStoep | open | [
"high priority",
"triaged",
"module: correctness (silent)",
"oncall: pt2",
"module: inductor",
"ubn"
] | 1 | NONE | ### 🐛 Describe the bug
It seems both torch.quantile and torch.nanquantile don't handle nans correctly when compiled on cuda. Results are consistent without nans or on cpu. I've tested this on pytorch 2.6 and 2.7, both standard pip install with cuda 12.6.
Minimal code to reproduce:
```
import torch
def eager_quant... | true |
3,028,074,742 | The test 'test_host_memory_stats' is failing in torch2.7.0+cu118 | 1274085042 | closed | [] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
When I run test_cuda.py locally, I've found an error in test_host_memory_stats
```
PYTORCH_TESTING_DEVICE_ONLY_FOR="cuda" python test_cuda.py -v
```
The error is similar to https://github.com/pytorch/pytorch/issues/148607
**output**
![Image](https://github.com/user-attachments/assets/8... | true |
3,028,060,180 | torch.nn.functional.ctc_loss raises cuDNN error in PyTorch versions >=2.5.0 | zhu-han | closed | [
"module: cudnn",
"triaged"
] | 1 | NONE | ### 🐛 Describe the bug
In torch.nn.functional.ctc_loss, when log_probs is on gpu, if we set the devices of input_lengths, target_lengths, and targets as cpu and their dtypes as torch.int32, it will raise the following error:
```
File "[...]/lib/python3.10/site-packages/torch/nn/functional.py", line 3069, in ctc_los... | true |
3,028,008,222 | Add MTIA memory info into the sharder | emasap | closed | [
"fb-exported"
] | 4 | CONTRIBUTOR | Test Plan: buck2 run //scripts/emmanuelmenage/apf_rec_investigations:planner_investigations now correctly reports OOM if the tables are too big.
Differential Revision: D72565617
| true |
3,027,965,096 | [ONNX] scatter_reduce with max reduction not correctly converted to ONNX for 2d input | spietz | closed | [
"module: onnx",
"triaged"
] | 2 | NONE | ### 🐛 Describe the bug
I discovered while trying to convert a pytorch-geometric based model, that torch.scatter_reduce cannot be correctly converted to ONNX when input is two dimensional.
A minimal example showing this is
```python
import torch
import onnxruntime
# 2d scatter_reduce example
# input: 2x2 tensor
in... | true |
3,027,744,829 | [Inductor][CPP] Enable vectorized fp8 quant dequant | leslie-fang-intel | open | [
"module: cpu",
"open source",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152418
* #152417
**Summary**
This PR enables the vectorization codegen with Inductor CPP backend for `FP8_E4M3` `quant` from `float32` and `dequant` to `float32`.
**Test Plan**
```
python test/inductor/test_cpu_repro.py -k tes... | true |
3,027,744,691 | Add Vectorized FP8 E4M3 | leslie-fang-intel | open | [
"module: cpu",
"open source",
"ciflow/trunk",
"topic: not user facing"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152418
* __->__ #152417
**Summary**
This PR mainly adding the `Vectorized<Float8_e4m3fn>` class to support the vectorization of `FP8 E4M3` with methods:
- Convert to/from `Vectorized<float>`
- Common vectorized methods like: `mul`, `a... | true |
3,027,737,898 | DISABLED test_comprehensive_index_select_cuda_int32 (__main__.TestInductorOpInfoCUDA) | pytorch-bot[bot] | open | [
"high priority",
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: inductor"
] | 14 | NONE | Platforms: inductor
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_comprehensive_index_select_cuda_int32&suite=TestInductorOpInfoCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/41325481121).
Over t... | true |
3,027,737,486 | DISABLED test_input_moved_to_cuda_device_script (__main__.TensorPipeCudaRemoteModuleTest) | pytorch-bot[bot] | open | [
"oncall: distributed",
"module: flaky-tests",
"skipped"
] | 1 | NONE | Platforms: linux
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_input_moved_to_cuda_device_script&suite=TensorPipeCudaRemoteModuleTest&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/41321274671).
Over ... | true |
3,027,590,597 | [CUDA] Add new architectures | Aidyn-A | closed | [
"module: cuda",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 5 | COLLABORATOR | CUDA 12.9 will introduce a couple of new architectures `sm_103` and `sm_121`. We do not need to build for them, because they are going to be compatible with`sm_100` and `sm_120` respectively (similar to `sm_86` and `sm_89`), but PyTorch must be "aware" of them.
cc @ptrblck @msaroufim @eqy @jerryzh168 | true |
3,027,436,630 | [wip] use base tensor storage offset in gen_alias_from_base | bobrenjc93 | closed | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152413
Fixes #151799
We use the aliased base tensor's storage offset because the target_meta_tensor's offset
storage can be incorrect since we often times clone_preserve_strides to fix alignment. See
copy_misaligned_inputs in _induc... | true |
3,027,433,846 | [Dynamo][Typing] Enable typing hints for `tx` in `misc.py` | shink | closed | [
"open source",
"Merged",
"topic: not user facing",
"module: dynamo"
] | 4 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
3,027,423,356 | [Quant][X86] add ops to compute uint8 pointwise add/add_relu | Xia-Weiwen | open | [
"module: cpu",
"open source",
"ciflow/trunk",
"release notes: quantization",
"intel"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152811
* __->__ #152411
**Summary**
This PR adds two new ops, `onednn.qadd.tensor` and `onednn.qadd_relu.tensor`, for int8 elementwise add, which accepts inputs on CPU device (instead of QuantizedCPU).
The new ops are implemented with AV... | true |
3,027,328,382 | [Hierarchical Compile] Add mutation dependencies to topological sorting | mlazos | open | [
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152589
* #152572
* #152570
* #152506
* __->__ #152410
* #152505
* #152389
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
3,027,324,032 | Cleanup DeviceInterface in triton test | Flamefire | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | COLLABORATOR | - Remove inherited functions
- Return valid device_count (1 device: idx=0)
- Remove unused function `triton_supported`
Followup to #144399
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauh... | true |
3,027,250,710 | [Inductor][CPU] bug fix for int8 GEMM compensation epilogue | Xia-Weiwen | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"intel",
"module: inductor",
"ciflow/inductor"
] | 5 | COLLABORATOR | Fixes #152398
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @voznesenskym @penguinwu @EikanWang @Guobing-Chen @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
3,027,229,439 | Call torch.distributed.destroy_process_group() at the end of the example | wangkuiyi | open | [
"oncall: distributed",
"open source",
"release notes: distributed (dtensor)"
] | 3 | CONTRIBUTOR | Address the comment https://github.com/pytorch/pytorch/pull/152027#pullrequestreview-2800075775
## Test Plan
Running the following command
```shell
torchrun --nproc-per-node=4 torch/distributed/tensor/examples/visualize_sharding_example.py
```
should not print the following warning at the end of the exec... | true |
3,027,189,923 | [DTensor] Calling .item() on DTensor with Partial placement results in local value | dest1n1s | open | [
"oncall: distributed",
"triaged"
] | 3 | NONE | ### 🐛 Describe the bug
Currently when calling `.item()` method on a 0-dim `DTensor` with a `Partial` placement, it will directly give the local part of the distributed tensor as the result, without calling the reduction method:
```python
device_mesh = init_device_mesh("cuda", (2,))
t = torch.arange(8, dtype=torch.fl... | true |
3,027,115,839 | [Do not merge] poke CI with FX IR always on | blaine-rister | open | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Testing the CI with FX IR conversion always enabled, to find bugs.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
3,027,005,924 | use cutlass native BroadcastPtrArray in scaled group gemm | ngimel | closed | [
"Merged",
"ciflow/trunk",
"release notes: cuda"
] | 5 | COLLABORATOR | After cutlass update to 3.9 we can use BroadcastPtrArray instead of a local copy with small changes.
| true |
3,026,996,578 | [ROCm][TunableOp] Fix ScaledGEMM rowwise | naromero77amd | closed | [
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/rocm",
"ciflow/rocm-mi300"
] | 4 | COLLABORATOR | Fixes TunableOp ScaledGEMM regression for rowwise scaling caused by this https://github.com/pytorch/pytorch/pull/147548
Credit goes to @mawong-amd for fix.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang | true |
3,026,986,425 | [PowerPC] Fix vec256 for complex float and double in Power system | Tiwari-Avanish | closed | [
"module: cpu",
"open source",
"Merged",
"topic: build",
"release notes: cpu (x86)"
] | 5 | CONTRIBUTOR | Power System build is failing with below error.
After this commit it is failing:
https://github.com/pytorch/pytorch/commit/912102b4ecf776711436f95d2fe62d78e39ad880
Fix the build error along with test cases that are failing for complex double and complex float data type.
Build Failure Logs:
```
vec_base.h:79... | true |
3,026,947,999 | [NFC] [inductor] [compile async] Warn exception if pickler failed | ChuanqiXu9 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 8 | CONTRIBUTOR | A NFC to help us to find issues
See https://github.com/pytorch/pytorch/issues/151904
CC @aorenste
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
3,026,898,940 | Compilation Issues with sm_129 (RTX 5070 Ti) on WSL - Seeking Advice | kaworukevin | closed | [] | 1 | NONE | ### 🐛 Describe the bug
**Environment:**
- OS: Windows 11 with WSL (Ubuntu 22.04)
- GPU: NVIDIA GeForce RTX 5070 Ti (sm_129)
- CUDA: 12.8
- PyTorch Version: 2.8.0a0+gitc8b4a39 (compiled from source)
**Description:**
Hi PyTorch team,
I’m trying to compile PyTorch from source on WSL (Ubuntu 22.04) to support an NVID... | true |
3,026,893,525 | Remove 3.13 hack when installing TIMM | huydhn | closed | [
"Merged",
"topic: not user facing",
"test-config/default"
] | 3 | CONTRIBUTOR | A Docker build failure showing up at this step triggered by the landing of https://github.com/pytorch/pytorch/pull/152362. Here is the example logs https://github.com/pytorch/pytorch/actions/runs/14718029881/job/41305891896:
```
#37 29.72 + as_jenkins conda run -n py_3.13 pip install --progress-bar off --pre torch... | true |
3,026,872,250 | [CPU][UT] 16 UT of test/inductor/test_cpu_select_algorithm.py failed with PyTorch 2025-04-028 nightly wheel | LifengWang | closed | [
"oncall: cpu inductor"
] | 6 | CONTRIBUTOR | ### 🐛 Describe the bug
16 UT of test/inductor/test_cpu_select_algorithm.py failed.
The suspected guilty commit: d70490ecfee849149a05541008c2601487cf0012
```
FAILED [0.2334s] test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_per_channel_q... | true |
3,026,787,590 | Build Issue for power issue related to vec complex double and float | Tiwari-Avanish | closed | [
"module: cpu",
"open source"
] | 4 | CONTRIBUTOR | Power System build is failing with below error.
After this commit it is failing:
912102b4ecf776711436f95d2fe62d78e39ad880
Fix the build error along with test cases that are failing for complex double and complex float data type.
**Build Failure Logs:**
vec_base.h:790:6: error: use of deleted function ‘at... | true |
3,026,753,818 | set thread_work_size to 4 for unrolled kernel | ngimel | closed | [
"Merged",
"ciflow/trunk",
"release notes: cuda"
] | 16 | COLLABORATOR | Previous PRs enabling 8-vectorization inadvertently regressed unrolled kernel perf.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
3,026,656,726 | [dynamo] Relax guard introduced when tracing `__call__` on user defined object | StrongerXi | 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):
* __->__ #152395
* #152369
This relaxes the guard introduced in #100444 (which aggressively guard
on the object id, despite Dynamo is just tracing its `__call__` method.
This allows users to bypass the high compilation time issue in #150706
b... | true |
3,026,652,990 | [Accelerator] Fix Python typing in accelerator | cyyever | open | [
"triaged",
"open source",
"ciflow/trunk",
"topic: not user facing"
] | 6 | COLLABORATOR | There are some changes:
1. Some accelerator APIs require an accelerator device (that is, without `cpu`). In such cases, Optional device typing causes confusion. Therefore, `ExplicitDevice` is introduced and used in `set_device_index`.
2. Use keywords for arguments if possible.
3. `__exit__ ` of `device_index` is cha... | true |
3,026,652,517 | [cutlass backend] Add (limited) bmm dynamic shape support | henrylhtsang | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 10 | CONTRIBUTOR | Differential Revision: D73626732
In this PR, we add support for bmm dynamic shape, provided that the batch stride is the biggest in the stride for A, B, and D. For example, for A of size `(B, M, K)`, we support stride `(M*K, K, 1)` and `(M*K, 1, M)`. With this assumption, we can infer the batch stride from existing ... | true |
3,026,644,650 | [Inductor] Use `torch._dynamo.utils.same` in block pointer tests, adding atol/rtol kwargs to it. | blaine-rister | open | [
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | This refactor consolidates test utilities by calling `torch._dynamo.utils.same` in Inductor's block pointer tests. To facilitate this, it also adds `atol` and `rtol` kwargs to the function, which previously supported only the `tol` kwarg assigning the same value to both.
cc @voznesenskym @penguinwu @EikanWang @jgong5 ... | true |
3,026,637,236 | [Inductor] Wrapper code refactors to prepare for FX codegen | blaine-rister | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | This PR contains some refactors from https://github.com/pytorch/pytorch/pull/146942, which help to enable Wrapper FX codegen:
1. Remove `OutputLine`, which is unused.
2. Add an attribute to the backend classes specifying whether they support caching.
3. Before compiling a graph, query the registered backends and che... | true |
3,026,605,874 | [Inductor] Fix typing in cuda_template.py | mlazos | closed | [
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150910
* __->__ #152390
* #150909
* #150907
* #151406
* #150906
| true |
3,026,562,141 | [Hierarchical Compilation] Track node mutations | mlazos | open | [
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152589
* #152572
* #152570
* #152506
* #152410
* #152505
* __->__ #152389
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
3,026,553,818 | Add vec_reduce_all specialization for std::plus on AArch64 | swolchok | open | [
"module: cpu",
"fb-exported",
"ciflow/trunk"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152388
* #152366
* #152365
* #152364
AArch64 has an instruction for this.
Differential Revision: [D73817183](https://our.internmc.facebook.com/intern/diff/D73817183/)
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jin... | true |
3,026,551,461 | [easy] Fix test_dynamo_timed | masnesral | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152387
Summary: I'm just trying to fix the test again. It's out of date because it's disabled and some dynamo_timed-related fields are gone now.
Test Plan: `python test/dynamo/test_utils.py -k dynamo_timed`
cc @voznesenskym @pengui... | true |
3,026,549,331 | [PT2]: fix add_passes and remove_passes naming issue | kqfu | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 8 | CONTRIBUTOR | Summary:
When defining pre_grad passes, they are initially defined as empty functions, then overriden in [customized_triton_kernel_passes.py](https://www.internalfb.com/code/fbsource/[b4eea3dcd7f22421e68a3c1533fd09a4281bc291]/fbcode/caffe2/torch/_inductor/fx_passes/fb/customized_triton_kernel_passes.py?lines=71-73). Th... | true |
3,026,538,621 | Illegal Instruction Caused by `grid_sample` Under Windows | ericspod | open | [
"high priority",
"module: build",
"module: windows",
"module: cpu",
"triaged",
"module: regression"
] | 15 | NONE | ### 🐛 Describe the bug
In Windows 10, Python 3.12.9, Pytorch 2.7.0+cu118, CUDA 12.2, the following code produces an "illegal instruction" causing an immediate crash:
```python
import torch
src = torch.rand((1, 1, 128, 64), dtype=torch.float64)
grid = torch.rand((1, 256, 256, 2), dtype=torch.float64)
dst = nn.functio... | true |
3,026,536,528 | [inductor][invoke_subgraph] Remove assertion checks for outputs of invoke_subgraph | anijain2305 | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 11 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152494
* #152490
* #152383
* __->__ #152384
* #152581
* #152547
For invoke_subgraph, input assertions are good. We don't need output assertions. This is the tlparse
Before
![image](https://github.com/user-attachments/assets/4ae14530-33... | true |
3,026,536,446 | [inductor][subgraph] Simplify the resulting output code for subgraph | anijain2305 | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 12 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152675
* #152494
* #152490
* __->__ #152383
Check out output code
Before this PR - - https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/.tmp3iXDVs/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1... | true |
3,026,517,901 | fix: outdated contents in dynamo overview | huijjj | open | [
"triaged",
"open source"
] | 4 | NONE | Fixes #152381
| true |
3,026,517,542 | Outdated contents in dynamo overview | huijjj | open | [
"module: docs",
"triaged"
] | 1 | NONE | ### 📚 The doc issue
contents in dynamo overview [document](https://pytorch.org/docs/stable/torch.compiler_dynamo_overview.html) are outdated, especially the ones regarding guards.
cache_entry does not have check_fn anymore, rat... | true |
3,026,510,940 | [aten] Enable vectorized 8byte copy for fp16/bf16 for index select kernel | jeetkanjani7 | closed | [
"Merged",
"ciflow/trunk",
"release notes: cuda",
"topic: performance"
] | 4 | CONTRIBUTOR | ## Summary
Enable aligned vector loading for 2 bytes data types for index select. Specifically:
- **4 element fp16/bf16 packing**: added 8-byte vector load/store to move 4 half values at once.
- **warp-wide predicate (__all_sync)**: decide fast vs fallback path per warp, eliminating lane level divergence
- **al... | true |
3,026,506,465 | [inductor] if unbacked symint in old-size or new-size skip mark_reuse check | ColinPeppler | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 18 | CONTRIBUTOR | Probably can run the `mark_reuse` check work with unbacked sizes under certain conditions.
For e.g. `x.repeat(u0, 2).repeat(2, u0)`.
But I think cases like those are rare so skipping the check for now.
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152379
cc @voznes... | true |
3,026,504,555 | [FP8][CUTLASS] xFail `honor_sm_carveout` on `sm100` | eqy | open | [
"module: cuda",
"triaged",
"open source",
"topic: not user facing",
"matrix multiplication",
"module: float8"
] | 2 | COLLABORATOR | CUTLASS only supports SM carveout via green contexts on `sm100`
cc @ptrblck @msaroufim @jerryzh168 @yanbing-j @vkuzo @albanD @kadeng @penguinwu | true |
3,026,502,071 | Run link linters on modified files only or on everything when scheduled | shoumikhin | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 5 | CONTRIBUTOR | null | true |
3,026,477,854 | [aten] Enable vectorized 8byte copy for fp16/bf16 for index select kernel | jeetkanjani7 | closed | [
"release notes: cuda"
] | 2 | CONTRIBUTOR | ## Summary
Enable aligned vector loading for 2 bytes data types for index select. Specifically:
- **4 element fp16/bf16 packing**: added 8-byte vector load/store to move 4 half values at once.
- **warp-wide predicate (__all_sync)**: decide fast vs fallback path per warp, eliminating lane level divergence
- **al... | true |
3,026,457,608 | Feature/enable 8 byte vector loading | jeetkanjani7 | closed | [
"release notes: cuda"
] | 3 | CONTRIBUTOR | ## Summary
Enable aligned vector loading for 2 bytes data types for index select. Specifically:
- **4 element fp16/bf16 packing**: added 8-byte vector load/store to move 4 half values at once.
- **warp-wide predicate (__all_sync)**: decide fast vs fallback path per warp, eliminating lane level divergence
- **al... | true |
3,026,419,012 | TORCH_COMPILE_DEBUG=1 does not consistently generate debug logs | xuxalan | open | [
"module: logging",
"triaged",
"oncall: pt2",
"module: dynamo"
] | 0 | NONE | ### 🐛 Describe the bug
I am trying to collect log files generated during `torch.compile` execution for debugging purposes, but the files do not always appear.
I created the following simple test script:
```
import torch
import torch.nn as nn
import torch.nn.functional as F
device = torch.device("cuda")
class Simpl... | true |
3,026,374,148 | complex.pow(2) on GPU by replacing with complex * complex to avoid numerical instability | Raman-Kumar | open | [
"triaged",
"open source",
"ciflow/trunk",
"topic: not user facing"
] | 10 | CONTRIBUTOR | Fixes #150951
Summary:
For complex.pow(2) on GPU:
Uses complex * complex directly.
Produces results consistent with CPU implementation.
Eliminates spurious imaginary components for real inputs.
🧪 Tests
Added unit tests to verify correctness of the new kernel path.
Verified numerical consistency with CPU r... | true |
3,026,362,260 | [Relandx2] Rewrite the guts of torch::jit::Lexer to speed it up | swolchok | open | [
"oncall: jit",
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: jit",
"ci-no-td",
"ciflow/s390"
] | 17 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152372
Reapplying with fix for linux-manylinux-2_28-py3-cpu-s390x / build
failure
(https://github.com/pytorch/pytorch/actions/runs/14716285820/job/41300304223#logs),
which is to just update a pair of static_assert constants I got wro... | true |
3,026,350,175 | [MPS] fix memory leak in sdpa float32 | Isalia20 | closed | [
"triaged",
"open source",
"Merged",
"module: mps",
"release notes: mps",
"ciflow/mps"
] | 5 | COLLABORATOR | Fixes #152344
Leak seems to be on the MPS Graph side, even though there is an identity tensor it seems like it's no longer enough to bypass the SDPA sequence which seems to leak memory.
Even adding 0.0f seems to be optimized to be ignored and still take the sdpa sequence(that's the reason for adding 1e-20)
c... | true |
3,026,336,931 | DISABLED test_remote_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_True_use_static_cuda_launcher_False (__main__.TestFxGraphCache) | pytorch-bot[bot] | open | [
"module: rocm",
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: inductor"
] | 1 | NONE | Platforms: rocm
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_remote_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_True_use_static_cuda_launcher_False&suite=TestFxGraphCache&limit=100) and the most recent trunk [workflow logs... | true |
3,026,228,048 | [AOTAutogradCache] Allow `torch.Tensor` and a non-torch op from einops | StrongerXi | closed | [
"Merged",
"topic: not user facing",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152395
* __->__ #152369
This addresses part of #150706.
Specifically, it reduces the warm start `torch.compile` overhead by
40~50% for GGUF models on
1. HuggingFace diffusers: [tlparse before, 224s](https://manifold.edge.x2p.facebook.net/v... | true |
3,026,221,694 | Remove conda refs in tools | Camyll | closed | [
"better-engineering",
"Merged",
"ciflow/trunk",
"release notes: dataloader"
] | 3 | CONTRIBUTOR | Fixes #152126
Did not find references in the two .ipynb files
| true |
3,026,210,520 | DISABLED test_reduce_stress_cuda (__main__.ProcessGroupGlooTest) | jithunnair-amd | open | [
"module: rocm",
"triaged",
"skipped"
] | 1 | COLLABORATOR | Platforms: rocm
This test was disabled because it failed on the MI300 runners in https://github.com/pytorch/pytorch/actions/runs/14713239294/job/41293256180
The stress_cuda tests seem to be flaky.
cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
3,026,165,767 | vec::map: directly process reduced-precision floats when reasonable | swolchok | open | [
"module: cpu",
"fb-exported"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152388
* __->__ #152366
* #152365
* #152364
The immediate motivation is to make map support match
ExecuTorch so we can delete ExecuTorch-specific mapping functions, but
this should also straightforwardly improve performance.
Testing: there... | true |
3,026,165,374 | add is_vec_specialized_for | swolchok | open | [
"module: cpu",
"fb-exported",
"ciflow/trunk",
"release notes: cpp"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152388
* #152366
* __->__ #152365
* #152364
Let people detect at compile time whether Vectorized is specialized for a given type. See vec_base.h.
Differential Revision: [D73802129](https://our.internmc.facebook.com/intern/diff/D73802129/)
... | true |
3,026,165,241 | Format all headers under ATen/cpu/vec, not just top-level | swolchok | open | [
"module: cpu",
"fb-exported",
"ciflow/trunk",
"topic: not user facing",
"skip-pr-sanity-checks"
] | 11 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152388
* #152366
* #152365
* __->__ #152364
not formatting these seems like an oversight. Had to add a few clang-format suppressions to keep includes in the same order to avoid breaking builds.
This PR was generated using `lintrunner --... | true |
3,026,133,365 | [MPSInductor][BE] Make all reductions cacheable | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152363
By moving actual implementaiton to `_reduction_nocache` and make reduction a caching wrapper
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chen... | true |
3,026,086,804 | Add CUDA 12.8 almalinux image, remove CUDA 12.4 almalinux | atalman | closed | [
"Merged",
"topic: not user facing"
] | 3 | CONTRIBUTOR | This is general purpose image located in: https://hub.docker.com/r/pytorch/almalinux-builder
Updating it to match our supported CUDA matrix
Adding this build to use as general purpose image and use for Magma build | true |
3,026,077,531 | [Will This Work?] Build libgomp (gcc-11) from src on AArch64 | fadara01 | open | [
"open source",
"module: arm",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ciflow/linux-aarch64"
] | 4 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152361
cc @malfet @snadampal @milpuz01 @aditew01 @nikhil-arm @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauh... | true |
3,026,066,837 | Cast to unsigned char to avoid UB | io-no | closed | [
"triaged",
"open source",
"Merged",
"topic: not user facing"
] | 9 | CONTRIBUTOR | The standard requires that the argument to functions like `isdigit`, `isalpha`, and similar must be either `EOF` or an `unsigned char`; otherwise, the behavior is undefined (UB).
To avoid out-of-bounds reads, modern implementations of some libraries (such as glibc) deliberately pad their internal tables to guarantee... | true |
3,026,050,317 | fix:Update padding_mode to use Literal for type checking | sujeet4010 | closed | [
"triaged",
"open source",
"topic: not user facing"
] | 4 | NONE | Fixes #152280
| true |
3,026,039,978 | Use almalinux docker files for building Magma | atalman | closed | [
"module: cuda",
"Merged",
"topic: not user facing"
] | 8 | CONTRIBUTOR | Resolves https://github.com/pytorch/pytorch/issues/151707 for CUDA Nvidia Magma builds.
Removes deprecated cuda 12.4 build.
Using `pytorch/manylinux2_28-builder` image for magma build creates circular dependency.
For a while for magma builds we used `conda-builder` image since it does not have circular dependen... | true |
3,026,027,616 | [invoke_subgraph] Cache on tangent metadata and retrace if needed | anijain2305 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152547
* #152494
* #152490
* #152383
* #152384
* __->__ #152357
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
3,025,930,546 | [AOTI] Package lowered with package_constants_in_so=False still uses lots of memory when loaded | henrylhtsang | closed | [
"oncall: pt2",
"oncall: export",
"module: aotinductor"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
I am lowering a model with AOTI, with package_constants_in_so=False. So I expect the output .pt2 archive to be small (True) and no extra memory usage when I load it up (False).
The output .pt2 archive is 1.7MB which is good. But I notice a memory jump when I load the model with aoti_load_packa... | true |
3,025,922,019 | Pin setuptools runtime dependency | atalman | open | [
"module: binaries",
"module: build",
"module: cpp-extensions",
"triaged"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
This is related to https://github.com/pytorch/pytorch/issues/152276
We would like to pin setuptools dynamic dependency.
Currently setuptools version is not pinned in PyTorch METADATA:
```
Requires-Dist: setuptools; python_version >= "3.12"
```
We need to pin setuptools version to less than 8... | true |
3,025,873,311 | Add codeowner for merge rules | albanD | open | [
"ciflow/trunk",
"topic: not user facing"
] | 7 | COLLABORATOR | To ensure changes to merge rights are properly reviewed
Also make the codeowner file valid by removing invalid users | true |
3,025,825,393 | [inductor][dynamo] Include operator name in size/stride/alignment assertion | karthickai | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"keep-going",
"skip-url-lint"
] | 26 | COLLABORATOR | Fixes #151930
This PR updates the `assert_size_stride` and `assert_alignment` functions in [guards.cpp](https://github.com/pytorch/pytorch/blob/main/torch/csrc/dynamo/guards.cpp) to accept an optional `op_name` argument and includes it in the error messages.
The corresponding type stubs in [guards.pyi](https://g... | true |
3,025,782,716 | Provide list of files to link linters if desired | shoumikhin | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | CONTRIBUTOR | null | true |
3,025,711,953 | Use PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 to disable upper limit | anantwag19 | closed | [
"needs reproduction",
"module: mps"
] | 3 | NONE | ### 🐛 Describe the bug
File "/opt/anaconda3/envs/huggingface/lib/python3.10/site-packages/transformers/trainer.py", line 2560, in _inner_training_loop
tr_loss_step = self.training_step(model, inputs, num_items_in_batch)
File "/opt/anaconda3/envs/huggingface/lib/python3.10/site-packages/transformers/trainer.py"... | true |
3,025,685,570 | Add latex settings | svekars | closed | [
"module: docs",
"Merged",
"ciflow/trunk",
"topic: docs",
"topic: not user facing"
] | 12 | CONTRIBUTOR | - Fixes #147027
- Only lualatex can build our 3K pages PDF with reasonable quality, xelatex runs out of memory and pdflatex just fails.
- Move notes under the same toctree as python-api which is needed for the PDF but doesn't change how the HTML is generated.
This is the produced PDF:
[pytorch.pdf](https://githu... | true |
3,025,671,776 | DISABLED test_e2e_compile_True_model_type1 (__main__.TestE2ESaveAndLoad) | jithunnair-amd | open | [
"module: rocm",
"triaged",
"skipped"
] | 1 | COLLABORATOR | Platforms: rocm
This test was disabled because it's failing on the [MI300 runners](https://hud.pytorch.org/failure?name=periodic-rocm-mi300%20%2F%20linux-focal-rocm-py3.10%20%2F%20test%20(distributed%2C%201%2C%203%2C%20linux.rocm.gpu.mi300.4.test-2%2C%20module%3Arocm%2C%20oncall%3Adistributed)&jobName=linux-focal-rocm... | true |
3,025,656,863 | [WIP] DeadCodeEliminator Mark(block) improvement | shinyehtsai | open | [
"oncall: jit",
"fb-exported",
"release notes: jit"
] | 10 | NONE | Summary:
This diff seeks to optimize the DeadCodeEliminator within the mark(block) function.
The primary concept is to prevent redundant traversals of a fully marked block, particularly in the markLoop scenario, if all nodes within a block are marked, we can subsequently mark the block as fully marked.
Test Plan: Exi... | true |
3,025,654,637 | torch.nonzero_static is not documented on the website | albanD | open | [
"module: docs",
"triaged"
] | 4 | COLLABORATOR | See https://pytorch.org/docs/main/search.html?q=nonzero_static
cc @svekars @sekyondaMeta @AlannaBurke @ngimel | true |
3,025,653,907 | compile generates inefficient code for mutations on small slices of inputs | bdhirsh | open | [
"triaged",
"oncall: pt2"
] | 2 | CONTRIBUTOR | easy repro from slack:
```
import torch
def plus_one(x):
x[0].add(1.0)
return x
x_og = torch.randn(32 * 1024, 1024, device="cuda", dtype=torch.float32)
x = x_og.clone()
plus_one(x)
plus_one_compiled = torch.compile(plus_one)
x = x_og.clone()
plus_one_compiled(x)
```
If you run the above with `TORCH_LOGS="out... | true |
3,025,649,684 | Magma build for Docker build | atalman | closed | [
"topic: not user facing"
] | 2 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
| true |
3,025,623,679 | MPS SDPA `float32` memory leak | SalmanMohammadi | closed | [
"module: memory usage",
"triaged",
"module: mps"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
```python
# usage python test_mps_leak.py {dtype}
import sys
import torch
import torch.nn.functional as F
def get_mps_memory_usage():
current_allocated = torch.mps.current_allocated_memory() / (1024 * 1024)
driver_allocated = torch.mps.driver_allocated_memory() / (1024 * 1024)
r... | true |
3,025,622,097 | cudagraphs: `static_input_indices` incorrectly including SymInt graph args when using tensor subclasses + dynamic shapes | bdhirsh | open | [
"triaged",
"module: cuda graphs",
"module: __torch_dispatch__",
"oncall: pt2",
"module: dynamic shapes"
] | 1 | CONTRIBUTOR | See the comment here for more details: https://github.com/pytorch/pytorch/pull/152287/files#r2064120003
cc @mcarilli @ezyang @eellison @penguinwu @BoyuanFeng @Chillee @zou3519 @albanD @samdow @chauhang @bobrenjc93 | true |
3,025,611,482 | [Memento] Enable on-demand mode | mzzchy | open | [
"fb-exported",
"topic: not user facing"
] | 8 | CONTRIBUTOR | Summary:
# Context
Post: https://fb.workplace.com/groups/ai.efficiency.tools.users/permalink/2020094788475989/
On CUDA side, Memento enables the on-demand mode to trace a remote process without requiring code changes. In this diff, we want to enable the same features.
Overall, we follow the same approach to leverage... | true |
3,025,582,293 | [ROCm][Inductor][CK] Add ck-tile based universal gemm kernels to torch.mm autotune choices | tenpercent | open | [
"module: rocm",
"triaged",
"open source",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ciflow/rocm"
] | 7 | COLLABORATOR | This PR adds code generation for CK-tile based universal gemm kernels to the CK backend for Inductor, and adds these kernels to autotune choices.
Unlike legacy-CK based kernels (which are generated by parsing the CK instances from CK library), we generate the set of instances by manually specifying the tuning parame... | true |
3,025,572,654 | Add a label to skip URL lint if needed | shoumikhin | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"skip-url-lint"
] | 6 | CONTRIBUTOR | Some URLs may be down due to server side issues we can't control | true |
3,025,508,291 | [ROCm] Unskipped test_rnn_dropout_state for ROCm | iupaikov-amd | closed | [
"module: rocm",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/rocm"
] | 9 | CONTRIBUTOR | Unskipping the test, should work fine now.
Related PR: https://github.com/pytorch/pytorch/pull/144572
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
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