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 |
|---|---|---|---|---|---|---|---|---|
2,841,770,803 | [Feature Request] Include sequence "add ()" method similar to Keras | jobs-git | closed | [] | 3 | NONE | ### 🚀 The feature, motivation and pitch
Many models are sequential or at least many parts are sequential.
In keras, we can create layers as simple as this:
```python
model = Sequential ()
model.add (Input (...))
model.add (Conv2D(...))
...
```
This is important when chaining layers in Blueprint-like interfaces. Ch... | true |
2,841,731,191 | On Linux, passing torch.Generator to multiprocessing.Process crashes for forkserver and spawn start method | foxik | open | [
"high priority",
"module: multiprocessing",
"triaged",
"module: random"
] | 11 | CONTRIBUTOR | ### 🐛 Describe the bug
On Linux, when the multiprocessing method is `forkserver` or `spawn`, passing `torch.Generator` to a new process via `multiprocessing.Process` causes a crash. Consider the following example:
```python
import time
import torch
def worker(*args):
print("Worker started with", *args, flush=Tr... | true |
2,841,718,641 | [Inductor] add mkldnn_max_pool2d support for CPU inductor | CaoE | closed | [
"open source",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146827
* #146826
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,841,718,319 | add mkldnn maxpool support on CPU dispatch | CaoE | closed | [
"module: cpu",
"module: mkldnn",
"open source",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor",
"ciflow/linux-aarch64"
] | 5 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146827
* __->__ #146826
Add mkldnn_max_pool2d support on CPU dispatch as aten kernels miss a version without indices on CPU and its performance is much worse than that of oneDNN maxpool with a gap of up to 10x.
cc @jgong5 @mingfeima ... | true |
2,841,701,008 | [func] move rearrange to torch.func | shingjan | closed | [
"triaged",
"open source",
"topic: not user facing"
] | 5 | CONTRIBUTOR | Fixes #92675
basically moved functorch.rearrange to torch.func.arrange.
| true |
2,841,665,365 | Inductor-CPU might load (and store) fewer elements than the vector-width | sanchitintel | open | [
"oncall: pt2",
"oncall: cpu inductor"
] | 2 | COLLABORATOR | ### 🐛 Describe the bug
## Problem
Discovered while working on an Inductor-CPP templated GEMM that 16 FP16 elements might be copied (loaded & stored) at a time instead of 32 from a local buffer to the output buffer, even if the machine has ZMM registers.
[Codegened code link](https://gist.github.com/sanchitintel/43e... | true |
2,841,636,522 | Use mkldnn_max_pool2d for max_pool2d when indices is not needed | CaoE | closed | [
"module: cpu",
"module: mkldnn",
"open source",
"ciflow/trunk",
"ciflow/periodic",
"module: inductor",
"ciflow/inductor",
"release notes: inductor",
"ciflow/linux-aarch64"
] | 3 | COLLABORATOR | cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @gujinghui @PenghuiCheng @jianyuh @min-jean-cho @yanbing-j @Guobing-Chen @Xia-Weiwen @snadampal @voznesenskym @penguinwu @EikanWang @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,841,590,341 | Update slow tests | pytorchupdatebot | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/slow",
"ci-no-td"
] | 6 | 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 |
2,841,581,160 | Deprecate DataLoader pin_memory_device param | zeshengzong | open | [
"triaged",
"open source",
"release notes: dataloader"
] | 15 | CONTRIBUTOR | Following [ #131858 suggestion](https://github.com/pytorch/pytorch/pull/131858#pullrequestreview-2517760602) to optimize DataLoader code
cc @albanD | true |
2,841,579,789 | ImportError: cannot import name 'DiagnosticOptions' from 'torch.onnx._internal.exporter' | ashok-arora | closed | [
"module: onnx",
"triaged"
] | 11 | NONE | ### 🐛 Describe the bug
Unable to run any model for inference.
Traceback:
```bash
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
Cell In[15], line 1
----> 1 results = model('./hallucinated.png')
File /opt/anacon... | true |
2,841,577,933 | [dynamo] Support list subclasses and fix dict subclasses mutation bugs | anijain2305 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 8 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146995
* __->__ #146819
This PR adds support for list subclasses. Among other things are
1) Tracking the mutations on internal vts like `_dict_vt` and `_list_vt` using sources. This helps identify if there was a mutation in the underlyi... | true |
2,841,573,786 | [mps] Implement eager support for spherical_bessel_j0 | dcci | closed | [
"Merged",
"topic: not user facing",
"module: mps",
"release notes: mps",
"ciflow/mps",
"module: inductor"
] | 4 | MEMBER | cc @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,841,493,698 | BF16 linear(matmul) operator 100x slower on odd matrix dimension sizes on A100 | piubwd | open | [
"module: performance",
"module: cuda",
"triaged",
"module: cublas",
"module: linear algebra",
"matrix multiplication"
] | 3 | NONE | ### 🐛 Describe the bug
This is an another reproduction of issues #106469 and #106485 under the newer version of pytorch (torch 2.6+cu126)
When performing linear (matrix multiplication) operator under bf16 on A100, if one dimension length is an odd number (I tried 3,5,101), the speed is 136x~283x slower than those of... | true |
2,841,481,865 | Optimize dataloader Self typing | zeshengzong | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: dataloader"
] | 5 | CONTRIBUTOR | Optimize `dataloader.py` method return type with Self typing
| true |
2,841,448,666 | Use __qualname__ in add_safe_globals and update Unpickling error raised for Unsupported GLOBAL | hanson-hschang | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 10 | CONTRIBUTOR | - Fixes #146814
Change
```python
for f in _marked_safe_globals_set:
module, name = f.__module__, f.__name__
```
to
```python
for f in _marked_safe_globals_set:
module, name = f.__module__, f.__qualname__
```
for avoiding same key string overwrite.
A test is also added.
```
python test/tes... | true |
2,841,436,844 | Problem of same name nested class in serialization | hanson-hschang | closed | [
"module: serialization",
"triaged"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
The current implementation of `_get_user_allowed_globals` defined in the `_weights_only_unpickler.py` will encounter trouble when same name nested class added to safe globals through `torch.serialization.add_safe_globals`. The code that creates the problem is as follows:
```python
import torc... | true |
2,841,403,744 | Oneshot AllReduce not being triggered when there's nested intra- and inter-node process groups | donglinz | open | [
"oncall: distributed"
] | 1 | NONE | ### 🐛 Describe the bug
I am testing with 2 H100 nodes with 8 GPUs for each. Initialized a world process groups with size 16 and create intra-node process groups with ```torch.distributed.split_group``` thereafter.
I noticed that one short all reduce ops are not being triggered for intra-node process group all reduce... | true |
2,841,227,955 | fix #145064 , added error checking for empty tensor in _pdist_forward | AmalDevHaridevan | closed | [
"oncall: distributed",
"module: cpu",
"triaged",
"module: mkldnn",
"open source",
"NNC",
"ciflow/trunk",
"release notes: quantization",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"module: compiled autograd"
] | 5 | NONE | Fixes #145064
Added TORCH_CHECK to prevent iterating over nullptr and causing segfault.
We can verify this by running the following simple test:
```python import torch
print(torch.__version__)
input = torch.rand((11, 15,3))
print("Running test with non empty tensor")
print("="*50)
print(torch.ops.aten._pdis... | true |
2,841,219,771 | Added error checking for empty Tensor in _pdist_forward | AmalDevHaridevan | closed | [
"module: inductor"
] | 2 | NONE | Fixes #145064
Added TORCH_CHECK to prevent iterating over nullptr and causing segfault.
We can verify this by running the following simple test:
```python import torch
print(torch.__version__)
input = torch.rand((11, 15,3))
print("Running test with non empty tensor")
print("="*50)
print(torch.ops.aten._pdis... | true |
2,841,186,601 | DISABLED test_insignificant_strides (__main__.SDPAPatternRewriterCudaTests) | pruthvistony | closed | [
"module: rocm",
"triaged",
"skipped"
] | 2 | COLLABORATOR | Platforms: rocm
This test was disabled because it is failing on main branch ([recent examples](https://torch-ci.com/failure?failureCaptures=%5B%22inductor%2Ftest_fused_attention.py%3A%3ASDPAPatternRewriterCudaTests%3A%3Atest_insignificant_strides%22%5D)).
cc @jeffdaily @sunway513 @jithunnair-amd @ROCmSupport @dllehr-... | true |
2,841,159,931 | Memory access fault by GPU node when training on a 7900XTX | mesalon | closed | [] | 2 | NONE | ### 🐛 Describe the bug
When running a basic model trainer, I get this error.
```
(venv) mesalon@desktop-mesalon:~/markov/gpt2$ python3 trainer.py
Loaded pretrained model.
loss_type=None` was set in the config but it is unrecognised.Using the default loss: `ForCausalLMLoss`.
Training Epoch 1: 25%|███████████████████... | true |
2,841,120,246 | Generalize mixed precision in DDP | zhangxiaoli73 | closed | [
"oncall: distributed",
"open source",
"Merged",
"ciflow/trunk",
"release notes: distributed (ddp)"
] | 9 | CONTRIBUTOR | **Motivation:**
1. Generalize mixed precision in DDP.
2. Enable `SyncBatchNorm` for XPU device.
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @gujinghui @guangyey | true |
2,841,088,234 | _is_gcc Function Incorrectly Classifies clang++ as g++ | AmalDevHaridevan | closed | [
"open source",
"topic: not user facing",
"module: inductor"
] | 3 | NONE | Fixes #146712
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,841,082,377 | DISABLED test_inductor_all_gather_into_tensor_coalesced (__main__.CompileTest) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: c10d"
] | 86 | NONE | Platforms: linux, rocm, inductor
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_inductor_all_gather_into_tensor_coalesced&suite=CompileTest&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/36922272925... | true |
2,841,024,597 | chore: fix typos in error messages in FSDP | universome | closed | [
"oncall: distributed",
"open source",
"Merged",
"ciflow/trunk",
"release notes: distributed (fsdp)"
] | 7 | CONTRIBUTOR | Fixes two small typos in FSDP error messages
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,840,988,308 | `torch.library.register_fake` respects only positional order, but not kwargs order | HanGuo97 | open | [
"triaged",
"module: library",
"oncall: pt2",
"module: pt2-dispatcher"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
It seems like the registration process in `torch.library.register_fake` requires _order_ of arguments to be exactly aligned with the function to be registered. The argument names, however, could be arbitrary.
```python
import torch
import numpy as np
from torch import Tensor
# Example 1: a... | true |
2,840,974,180 | `Illegal Instruction` Error on Raspberry Pi 4 with `torch.nn.functional.interpolate` and `recompute_scale_factor=True` (Torch 2.6.0) | Chizkiyahu | closed | [
"high priority",
"triage review",
"module: onnx",
"module: regression",
"module: arm"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
# Description
When using `torch.nn.functional.interpolate` with `recompute_scale_factor=True` on a **Raspberry Pi 4**, PyTorch 2.6.0 causes an **Illegal Instruction error** during ONNX export.
# Code
```python
import torch
class Module(torch.nn.Module):
def forward(self, x):
# ... | true |
2,840,955,121 | AttributeError: partially initialized module 'torch._dynamo' has no attribute 'optimize' | fzimmermann89 | closed | [
"oncall: pt2"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
In a fresh conda/pip cpu-only torch2.6 environment
```
conda create -n dynamo python=3.12 -c conda-forge
conda activate dynamo
pip install --upgrade --index-url=https://download.pytorch.org/whl/cpu --extra-index-url https://pypi.org/simple/ einops "torch>=2.6" torchvision
```
trying to use... | true |
2,840,927,035 | Add mechansim for small intra kernel reductions | drisspg | closed | [
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146801
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,840,908,250 | [inductor] Remove _get_grid_fn_str | jansel | 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):
* __->__ #146800
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,840,877,532 | [MPS] cholesky ex version | Isalia20 | closed | [
"triaged",
"open source",
"Merged",
"topic: improvements",
"release notes: mps",
"ciflow/mps"
] | 6 | COLLABORATOR | PR #145701 didn't have experimental version of cholesky. This PR adds that version
| true |
2,840,719,237 | Torch 2.6 Unexpected Graph Break with SubConfigProxy | chengzeyi | open | [
"triaged",
"module: regression",
"oncall: pt2",
"module: graph breaks",
"module: compile ux"
] | 4 | CONTRIBUTOR | ### 🐛 Describe the bug
When I run with the following code which checks a value from a custom config module (similar to `torch._inductor.config`), I encounter unexpect graph break with latest torch 2.6.0, which does not occur with torch 2.5.0. This causes severe performance regression when running FLUX models with Par... | true |
2,840,715,145 | Torch 2.6 Unexpected Graph Break with contextmanager | chengzeyi | closed | [] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
When I run with the following context manager, I encounter unexpect graph break with latest torch 2.6.0, which does not occur with torch 2.5.0. This causes severe performance regression when running `FLUX` models with `ParaAttention`.
```python
class UnifiedAttnMode(TorchFunctionMode):
dis... | true |
2,840,623,887 | Segmentation Fault in `torch.ops.aten.matrix_exp_backward` | WLFJ | open | [
"module: crash",
"module: error checking",
"triaged",
"module: empty tensor",
"topic: fuzzer"
] | 0 | NONE | ### 🐛 Describe the bug
example:
```python
import torch
def f(*args):
sym_0, sym_1, sym_2, sym_3, sym_4, sym_5, sym_6 = args
var_976 = torch.ops.aten.blackman_window(window_length= sym_0, periodic= sym_1)
var_956 = torch.ops.aten.special_logsumexp(self= var_976, dim= sym_2, keepdim= sym_3)
var_781 = ... | true |
2,840,620,433 | Floating Point Exception in `torch.ops.aten.pixel_shuffle` with Large `upscale_factor` | WLFJ | open | [
"module: crash",
"module: error checking",
"triaged",
"topic: fuzzer"
] | 0 | NONE | ### 🐛 Describe the bug
example:
```python
import torch
def f(sym_3):
return torch.ops.aten.pixel_shuffle(
self=torch.randn((1, 1363, 1)), upscale_factor=sym_3
)
f(8070450532247928832)
```
result:
```
fish: Job 3, 'python3 sigsegv-pixel_shuffle.py' terminated by signal SIGFPE (Floating point excep... | true |
2,840,619,252 | Segmentation Fault in `torch.as_strided_copy` with Large `storage_offset` | WLFJ | open | [
"module: crash",
"module: error checking",
"triaged",
"topic: fuzzer"
] | 0 | NONE | ### 🐛 Describe the bug
example:
```python
from torch import eye, as_strided_copy
def f(*args):
sym_0, sym_1, sym_2, sym_3, sym_4 = args
var_964 = eye(sym_0, sym_1)
return as_strided_copy(var_964, sym_2, sym_3, sym_4)
f(0, 1, (4,), (1,), 7546629512955761371)
```
result:
```
fish: Job 3, 'python3 sigs... | true |
2,840,612,528 | Segmentation Fault in `torch.ops.aten.as_strided` with Large `storage_offset` | WLFJ | open | [
"module: crash",
"module: error checking",
"triaged",
"topic: fuzzer"
] | 0 | NONE | ### 🐛 Describe the bug
example:
```python
import torch
def f(sym_1, sym_2, sym_3):
var_564 = torch.ops.aten.as_strided(self= torch.tensor([True]), size= sym_1, stride= sym_2, storage_offset= sym_3)
return var_564
res = f((4096,), (0,), 9223372036854775807)
print(res)
```
result:
```
fish: Job 3, 'python... | true |
2,840,612,159 | `Illegal instruction (core dumped)` on Raspberry Pi 4 when exporting ONNX with `torch 2.6.0` | Chizkiyahu | closed | [
"high priority",
"module: crash",
"triaged",
"module: regression",
"module: arm"
] | 13 | CONTRIBUTOR | ### 🐛 Describe the bug
#### **Description**
On Raspberry Pi 4, `torch.onnx.export` fails with `Illegal instruction (core dumped)` in `torch 2.6.0`. The same code works fine on `torch 2.5.1`. The issue occurs when using `x.expand(x.shape[0], -1, -1)` inside a `torch.nn.Module`. The crash happens **only during ONNX exp... | true |
2,840,611,363 | Floating Point Exception in `torch.ops.aten.unfold_backward` with Specific Input | WLFJ | open | [
"module: crash",
"module: error checking",
"triaged",
"topic: fuzzer"
] | 0 | NONE | ### 🐛 Describe the bug
example:
```python
import torch
def f(*args):
sym_0, sym_1, sym_2, sym_3, sym_4, sym_5, sym_6 = args
var_789 = torch.ones(sym_0, dtype=sym_1, layout=sym_2)
return torch.ops.aten.unfold_backward(var_789, sym_3, sym_4, sym_5, sym_6)
f((2309,), torch.bool, torch.strided, (1531,... | true |
2,840,610,656 | Segmentation Fault in `torch.ops.aten.multi_margin_loss_backward` with Empty `grad_output` | WLFJ | open | [
"module: crash",
"module: error checking",
"triaged",
"topic: fuzzer"
] | 0 | NONE | ### 🐛 Describe the bug
example:
```python
import torch
sym_16 = 2
sym_17 = True
sym_18 = 0
grad_output = torch.tensor([])
self = torch.tensor([64.])
target = torch.tensor([0])
torch.ops.aten.multi_margin_loss_backward(grad_output=grad_output, self=self, target=target, p=sym_16, margin=sym_17, weight=None, reductio... | true |
2,840,609,383 | Segmentation Fault in `torch.ops.aten.linalg_eigvals` After Invalid `unfold_copy` | WLFJ | open | [
"module: crash",
"triaged",
"module: linear algebra",
"topic: fuzzer"
] | 0 | NONE | ### 🐛 Describe the bug
example:
```python
import torch
sym_0 = 512
sym_1 = False
sym_2 = 1.7976931348623157e+308
sym_3 = -1
sym_4 = 65
sym_5 = 9223372036854775807
sym_6 = 1
sym_7 = 33
sym_8 = 1
var_547 = torch.ops.aten.hamming_window(window_length=sym_0, periodic=sym_1, alpha=sym_2)
var_462 = torch.ops.aten.unfold... | true |
2,840,608,418 | Segmentation Fault in `torch.choose_qparams_optimized` with Invalid Parameters | WLFJ | open | [
"module: crash",
"oncall: quantization",
"topic: fuzzer"
] | 0 | NONE | ### 🐛 Describe the bug
example:
```python
import torch
sym_3 = 0
sym_4 = -1
sym_5 = 1.7976931348623157e+308
sym_6 = 0
res = torch.choose_qparams_optimized(input=torch.tensor([]), numel=sym_3, n_bins=sym_4, ratio=sym_5, bit_width=sym_6)
print(res)
```
result:
```
fish: Job 3, 'python3 sigsegv-choose_qparams_…' te... | true |
2,840,607,175 | Floating Point Exception in `torch.ops.aten.native_channel_shuffle` with `groups=0` | WLFJ | open | [
"module: crash",
"module: error checking",
"triaged",
"module: empty tensor",
"topic: fuzzer"
] | 0 | NONE | ### 🐛 Describe the bug
example:
```python
import torch
print(torch.__version__)
sym_7 = 0
var_471 = torch.ops.aten.native_channel_shuffle(torch.tensor([[[0.]]]), groups=sym_7)
print(var_471)
```
result:
```
fish: Job 3, 'python3 sigfpe-native_channel_s…' terminated by signal SIGFPE (Floating point exception)
```... | true |
2,840,592,411 | Installing CPU-only PyTorch results in unnecessary CUDA dependencies during Docker build. | devroopsaha744 | closed | [] | 2 | NONE | ### 🐛 Describe the bug
#### **Issue:**
I am using the standard PyTorch version (`torch`) inside a Docker container, but CUDA dependencies (e.g., `nvidia-cublas`, `nvidia-cusparse`) are still being installed, even though I only need the CPU version of PyTorch.
#### **Steps to Reproduce:**
1. Create a Dockerfile with ... | true |
2,840,558,414 | AttributeError: '_OpNamespace' '_C' object has no attribute 'silu_and_mul' | mrblenderTBS | closed | [] | 4 | NONE | ### 🐛 Describe the bug
if current_platform.is_cuda_alike() or current_platform.is_cpu():
self.op = torch.ops._C.silu_and_mul
### Versions
When trying to run a model based on vLLM, it displays this message. This error frankly baffled me. While other errors could at least be found on other forums,... | true |
2,840,493,112 | [export] cache unflatten forward module | pianpwk | open | [
"fb-exported",
"Stale",
"release notes: export"
] | 3 | CONTRIBUTOR | Differential Revision: D69361235
| true |
2,840,461,658 | [4/N] Remove unnecessary once flag usage | cyyever | closed | [
"oncall: distributed",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 6 | COLLABORATOR | Fixes #ISSUE_NUMBER
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,840,458,736 | Suggestion: integration of einops test suite | arogozhnikov | open | [
"module: ci",
"module: tests",
"triaged",
"module: linear algebra"
] | 1 | NONE | Hi torch team,
Starting from einops 0.8.1, you can test torch against einops with:
```shell
# install numpy, einops, pytest and torch
python -m einops.tests.run_tests numpy torch
```
and I suggest having this in torch's CI.
There are a couple of motivations:
1. einops tests actually reveal regressions in framewor... | true |
2,840,455,864 | [Inductor-CPU] FP16 X int8 WoQ GEMM for M <= 4 with FP16 accum & compute | sanchitintel | open | [
"module: cpu",
"open source",
"Stale",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 3 | COLLABORATOR | ## Summary
For FP16 activation, int8 weights (frozen) GEMM, for M dimension (batch size x sequence length) <= 4, the implementation in this PR is faster than the current Inductor implementation, and should accelerate next-token generation of LLMs during inference. Scale of int8 weight-only-quantization is applied wi... | true |
2,840,452,802 | TypeError when using torch.compile with RegionViT under torch.inference_mode() | hassonofer | open | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 0 | NONE | ### 🐛 Describe the bug
## Description
`torch.compile()` fails with TypeError when running inference on a RegionViT model specifically when using `torch.inference_mode()`. The same code works successfully:
- Without `torch.inference_mode()`
- During training
- When debug prints are added to the code
I've tried both P... | true |
2,840,428,303 | [not for commit] Add assert that is_parallel is true | jamesjwu | closed | [
"Stale",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146779
* #146417
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,840,423,603 | [torch.jit] INTERNAL ASSERT FAILED at "/pytorch/torch/csrc/jit/mobile/register_ops_common_utils.cpp":34, please report a bug to PyTorch. | cybersupersoap | open | [
"oncall: jit"
] | 0 | NONE | ### 🐛 Describe the bug
An INTERNAL ASSERT error will be raised when using TorchScript modules and `torch.jit.annotate`. The code is as follows:
```python
import inspect
from typing import Dict, Iterator, List, Optional, Tuple, Any
import torch
import torch.testing._internal.jit_utils
from torch.testing._internal.c... | true |
2,840,423,330 | Enable explicitly vectorized `_weight_int8pack_mm` op for FP16 dtype on x86_64 CPU | sanchitintel | open | [
"module: cpu",
"triaged",
"open source",
"ciflow/trunk",
"intel",
"release notes: intel"
] | 4 | COLLABORATOR | ## Summary
Currently, `_weight_int8pack_mm` is only explicitly vectorized for BF16 activations for x86_64 CPU, and has different AVX2 & AVX512 implementations.
This PR unifies its separate AVX512 & AVX2 implementations, and also makes it common for Float/BFloat16/Half activation dtypes, which is feasible since com... | true |
2,840,413,799 | INTERNAL ASSERT FAILED at "/pytorch/torch/csrc/jit/testing/file_check.cpp":607, please report a bug to PyTorch | cybersupersoap | open | [
"oncall: jit",
"module: testing"
] | 0 | NONE | ### 🐛 Describe the bug
An INTERNAL ASSERT error will be raised when using `torch.testing.FileCheck.checkcount`
```python
from torch.testing import FileCheck
FileCheck().check_count('is being compiled', 0).run("")
```
Error messages:
```
RuntimeError Traceback (most recent call last)
<... | true |
2,840,408,692 | [torch.jit] Crash would be raised when using torch.jit.script | cybersupersoap | open | [
"oncall: jit"
] | 1 | NONE | ### 🐛 Describe the bug
Segmentation fault would be triggered when using `torch.jit.script` and inserting a constant into the graph . The code is as follows:
```python
import torch
@torch.jit.script
def foo(inp):
x = inp + 1
y = x / 2
z = y * y
return z
with foo.graph.insert_point_guard(foo.graph.findNode('at... | true |
2,840,404,538 | [cuda] Simplify the sinc function a bit. | dcci | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | MEMBER | `else` after `return` can be removed & the indentation can be reduced, for readability. | true |
2,840,401,346 | INTERNAL ASSERT FAILED at "/pytorch/aten/src/ATen/native/quantized/cpu/qsigmoid.cpp":65, please report a bug to PyTorch. | cybersupersoap | open | [
"oncall: jit",
"oncall: quantization"
] | 0 | NONE | ### 🐛 Describe the bug
INTERNAL ASSERT Error would be raised when using `quantized tensor`and `torch.jit.trace`. The code is as follows:
```python
import torch
torch.backends.quantized.engine = "qnnpack"
def qpt(t, scale, zero_point, dtype=torch.quint8):
t = torch.tensor(t)
return torch.quantize_per_tensor(t... | true |
2,840,392,339 | Torch showing tensors are not equal, even though they are equal | Tylersuard | closed | [] | 2 | NONE | ### 🐛 Describe the bug
I create 2 tensors that should be identical, but PyTorch is saying they are not equal. I even print the two tensors out and they are identical.
import torch
first_tensor = torch.tensor([0.1, 0.2, 0.3]) + torch.tensor([0.4, 0.5, 0.6])
print(first_tensor)
second_tensor = torch.tensor([0.5... | true |
2,840,386,336 | [mps] Add a shader for spherical_bessel_j0. | dcci | closed | [
"Merged",
"topic: not user facing",
"module: mps",
"ciflow/mps",
"module: inductor"
] | 4 | MEMBER | In preparation for adding the operation to inductor/eager.
Adapted from the CUDA version of the shader.
cc @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @much... | true |
2,840,381,285 | There should be a single version of exec_unary_kernel() | dcci | closed | [
"triaged",
"module: mps"
] | 3 | MEMBER | ### 🐛 Describe the bug
Filing this one so I don't forget (and in case someone else wants to take a look)
```
davidino@davidino-mbp operations % git grep unary_kernel
SpecialOps.mm:static void unary_kernel_mps(TensorIteratorBase& iter, const std::string& name) {
SpecialOps.mm: unary_kernel_mps(iter, "i0");
SpecialOp... | true |
2,840,368,800 | MPS Error on sequoia 15.3: NDArray dimension length > INT_MAX' | fatemark | open | [
"needs reproduction",
"triaged",
"module: mps"
] | 9 | NONE | ### 🐛 Describe the bug
I get this error in comfyui on sequoia 15.3. The error only occurs beyond a certain size of the image i'm working with.
/AppleInternal/Library/BuildRoots/d187755d-b9a3-11ef-83e5-aabfac210453/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShaders/MPSCore/Types/MPSNDArray.mm:829: failed as... | true |
2,840,292,145 | [EZ] Add logic to build Metal shader with debug info | malfet | closed | [
"Merged",
"topic: not user facing"
] | 3 | CONTRIBUTOR | By appending `-frecord-sources -gline-tables-only` to the compilation command
Helpful when debugging shaders compiled into libtorch
Test plan: Run
`python ../tools/build_with_debinfo.py ../aten/src/ATen/native/mps/kernels/UpSample.metal ../aten/src/ATen/native/mps/operations/UpSample.mm`
And then run following... | true |
2,840,285,726 | Tensor Parallel (TP) broken on 2.6 (cannot `parallelize_module` correctly) | Cyrilvallez | closed | [
"oncall: distributed"
] | 5 | NONE | ### 🐛 Describe the bug
Hey! It looks like Tensor Parallel (TP) is broken in v2.6. Running the below simple snippet with `torchrun --nproc-per-node 4 test.py` would yield the following error:
`torch.distributed.DistBackendError: Attempt to perform collective on tensor not on device passed to init_process_group`
But as... | true |
2,840,238,335 | object of type 'SymInt' has no len() when split is called with tensor of specific dynamic sizes. | laithsakka | open | [
"needs reproduction",
"triaged",
"oncall: pt2",
"module: dynamic shapes"
] | 1 | CONTRIBUTOR | seen multiple times on internal model when dynamic = True. in different places
seems like issue in on of split implementations.
no local repo yet
1) example 1
aps-no_break2-de8c3fc544
```
return self._abstract_fn(*args, **kwargs)
File "/packages/aps.ads.icvr/icvr_launcher#link-tree/ads_mkl/ops/triton/trito... | true |
2,840,200,114 | Automatically resolve tensor mismatch issues, tensor conversion, and moving tensors to devices | Tylersuard | open | [
"triaged",
"module: python frontend"
] | 1 | NONE | ### 🚀 The feature, motivation and pitch
I love PyTorch, but if I ever have any problems, it's one of these 3:
1. Tensor dimensions mismatch
2. Numpy array not converted to tensor
3. Tensor is on the wrong device
It would be really cool if PyTorch could automatically resolve these. For number 1, it could silently c... | true |
2,840,161,030 | Fix standalone runner for CUTLASS auto-tuning backend | alexsamardzic | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 9 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146764
* #146755
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,840,114,632 | [Break XPU] Align meta calculation for fft_r2c with _fft_r2c_mkl | etaf | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor",
"ciflow/xpu"
] | 3 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146763
* #146880
* #145248
* #146762
Fix #146761 | true |
2,840,114,609 | [Break XPU][Inductor UT] Fix XPU Inductor UT failures introduced from community. | etaf | closed | [
"open source",
"Merged",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146763
* #146880
* #145248
* __->__ #146762
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,840,089,812 | [Break XPU][Inductor] The PR #145080 introduce wrong fft_r2c result on XPU. | etaf | closed | [
"triaged",
"module: xpu"
] | 0 | COLLABORATOR | ### 🐛 Describe the bug
I found XPU CI failure after the PR #145080 landed:
https://github.com/pytorch/pytorch/actions/runs/13158392419/job/36759585266
There are many FFT related OP failure in test_torchinductor_opinfo.py, for example:
```
=================================== FAILURES ==================================... | true |
2,840,073,144 | [torch.jit] INTERNAL ASSERT FAILED at "../aten/src/ATen/core/ivalue_inl.h":1967, please report a bug to PyTorch. | cybersupersoap | open | [
"oncall: jit"
] | 0 | NONE | ### 🐛 Describe the bug
An INTERNAL ASSERT error will be raised when using `torch.jit.script` and `torch.jit.freeze`. The code is as follows:
```python
import torch
from torch import nn
from torch.testing._internal.jit_utils import clear_class_registry
clear_class_registry()
conv1 = torch.nn.Conv2d(3, 64, kernel_size... | true |
2,840,069,298 | INTERNAL ASSERT FAILED at "../torch/csrc/jit/ir/alias_analysis.cpp":617, please report a bug to PyTorch. | cybersupersoap | open | [
"oncall: jit"
] | 0 | NONE | ### 🐛 Describe the bug
An INTERNAL ASSERT error will be raised when using `alias_db`. The code is as follows:
```python
from torch._C import parse_ir
graph_str = '\n graph(%a.1 : Tensor, %b.1 : Tensor):\n %11 : NoneType = prim::Constant()\n %8 : int = prim::Constant[value=0]()\n ... | true |
2,840,049,335 | INTERNAL ASSERT FAILED at "/pytorch/torch/csrc/autograd/functions/utils.h":74, please report a bug to PyTorch | cybersupersoap | open | [
"oncall: jit"
] | 0 | NONE | ### 🐛 Describe the bug
An INTERNAL ASSERT error will be raised when predicting. The code is as follows:
```python
import torch
class CustomLinear(torch.nn.Module):
def __init__(self, a, b):
super().__init__()
self.weight = torch.nn.Parameter(torch.randn(a, b))
def forward(self, x):
re... | true |
2,840,038,308 | [torch.jit.script] INTERNAL ASSERT FAILED at "./torch/csrc/jit/ir/ir.h":505, please report a bug to PyTorch | cybersupersoap | open | [
"oncall: jit"
] | 0 | NONE | ### 🐛 Describe the bug
An INTERNAL ASSERT error will be raised when using torch.jit.script. The code is as follows:
```python
import torch
@torch.jit.script
def foo(i: int, z):
y = z.view([z.size(i), 3, 2, z.size(i)])
return y
view = foo.graph.findNode('aten::view').input()
```
Error messages:
```
RuntimeEr... | true |
2,840,014,281 | [Inductor][CPU] Add GEMM templates for _weight_int4pack_mm_for_cpu with AVX512 | Xia-Weiwen | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"intel",
"module: inductor",
"ciflow/inductor"
] | 7 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146756
**Summary**
It's part of the task to enable max-autotune with GEMM template for WoQ INT4 GEMM on CPU.
This PR adds GEMM templates for `torch.ops.aten_weight_int4pack_mm_for_cpu`. The micro kernel used for the templates is... | true |
2,839,878,192 | Fix CUTLASS 2.x kernels for auto-tuning | alexsamardzic | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"merging"
] | 4 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146764
* __->__ #146755
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,839,766,409 | [MPS] fix inverse bug for N>1024 | Isalia20 | closed | [
"triaged",
"open source",
"Merged",
"module: mps",
"release notes: mps",
"ciflow/mps"
] | 12 | COLLABORATOR | Fixes #138200
cc @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen | true |
2,839,704,716 | [MPS] fix lu factor for large tensors with bs>1 | Isalia20 | closed | [
"open source",
"Merged",
"topic: bug fixes",
"release notes: mps",
"ciflow/mps"
] | 3 | COLLABORATOR | Try this:
```python
import torch
batch_size = 2
A = torch.eye(256, device="mps")[None, :, :].expand(batch_size, -1, -1) + 0.1 * torch.randn((batch_size, 256, 256), device="mps")
A_cpu = A.cpu()
LU_cpu, pivots_cpu = torch.linalg.lu_factor(A_cpu)
LU, pivots = torch.linalg.lu_factor(A)
torch.testing.assert_close... | true |
2,839,670,894 | realize stride symbols in estimate_runtime | laithsakka | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146752
Unfortuanlty could not create a local repo, or unit test.
fix https://github.com/pytorch/pytorch/issues/146686
| true |
2,839,665,509 | [MTIA] (4/n) Implement PyTorch APIs to query/reset device peak memory usage | chaos5958 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 10 | CONTRIBUTOR | Summary: Public summary (shared with Github): This diff updates the unit test for the PyTorch API "reset_peak_memory_stats".
Test Plan:
```
buck2 test //mtia/host_runtime/torch_mtia/tests:test_torch_mtia_api -- -r test_reset_peak_memory_stats
```
https://www.internalfb.com/intern/testinfra/testrun/9007199321947161
R... | true |
2,839,643,802 | Update instructions about faster linker | oraluben | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 8 | CONTRIBUTOR | This PR adds instructions to specify linker via cmake env `CMAKE_LINKER_TYPE` and also adds `mold` as a linker alternative.
Since 3.29, cmake introduced [`CMAKE_LINKER_TYPE`](https://cmake.org/cmake/help/latest/variable/CMAKE_LINKER_TYPE.html) that can specify linker without overwriting `ld` file or changing build s... | true |
2,839,639,588 | dest = zeros_like(source, dtype=DTYPE) changes source's DTensor dtype | janeyx99 | closed | [
"high priority",
"triage review",
"oncall: distributed",
"module: correctness (silent)",
"module: dtensor"
] | 4 | CONTRIBUTOR | ### 🐛 Describe the bug
Calling zeros_like on a DTensor should not have side effects on the source tensor, but it does. Specifically, the dtype recorded as a part of the DTensor spec is changed, which is wrong.
Example.
```
import torch
import torch.nn as nn
from torch.distributed.fsdp import fully_shard
lin1 = nn.... | true |
2,839,557,379 | Update strided test to float32 | drisspg | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 8 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146748
Fixes #146377
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,839,514,607 | Add hint message for `pack_padded_sequence` | zeshengzong | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 12 | CONTRIBUTOR | Fixes #144207
Add truncate hint message in docs [torch.nn.utils.rnn.pack_padded_sequence](https://pytorch.org/docs/stable/generated/torch.nn.utils.rnn.pack_padded_sequence.html)
## Test Result
| true |
2,839,465,359 | [Inductor] Fix the lowering of squeeze when input is not contiguous | leslie-fang-intel | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 5 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146746
**Summary**
Fix issue https://github.com/pytorch/pytorch/issues/143498. The issue happens when we lowering `select = torch.ops.aten.select.int(cat, 1, 0)`.
For example, when `cat` is contiguous with size[2, 2] stride[2,1... | true |
2,839,464,754 | [Flex Attention] Errors with Dynamic Shapes (Cannot determine truth value of Relational) | ChenlongDeng | closed | [
"oncall: pt2",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention"
] | 4 | NONE | ### 🐛 Describe the bug
Thanks for the team's great work! But it seems that the latest version (torch==2.6.0) still hasn't resolved the issue with dynamic shape inputs. I can easily reproduce this problem with a few lines of chunked-prefill code. I am curious if this is the same issue reported in https://github.com/py... | true |
2,839,439,912 | `torch.nn.utils.rnn.pack_padded_sequence` need better check for `input` dim | zeshengzong | closed | [] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
In [`torch.nn.utils.rnn.pack_padded_sequence`](https://pytorch.org/docs/stable/generated/torch.nn.utils.rnn.pack_padded_sequence.html) docs, there's a presumption about `T` is longest
> The returned Tensor’s data will be of size T x B x * (if batch_first is False) or B x T x * (if batch_first ... | true |
2,839,389,191 | [cutlass backend][BE] refactor tests to remove duplicate logic | henrylhtsang | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147173
* #147169
* #147158
* #147148
* __->__ #146743
Doing many things here:
* remove duplicate hip checking logic
* check for CUDA in setup
* remove CUTLASS_DIR setting. That is not needed when building from source and fbcode anymore
... | true |
2,839,384,955 | [Dynamo][autograd.Function] Relax backward speculation strict mode: support .requires_grad | yanboliang | 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):
* __->__ #146742
* #146741
* #146571
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,839,384,923 | [Dynamo][autograd.Function] Relax backward speculation strict mode: support .data | yanboliang | closed | [
"Merged",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146742
* __->__ #146741
* #146571
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,839,362,824 | PyTorch Compilation on AGX Xavier Error with -march=armv8.2-a+bf16 in KleidiAI | MaTwickenham | closed | [
"module: build",
"triaged",
"module: arm"
] | 4 | NONE | ### 🐛 Describe the bug
I am trying to compile PyTorch on my Jetson AGX Xavier, but I encounter the following error when compiling the third party lib `kleidiai`:
```
FAILED: third_party/kleidiai/CMakeFiles/kleidiai.dir/kai/ukernels/matmul/pack/kai_lhs_quant_pack_bf16p_f32_neon.c.o
/usr/bin/cc -DONNXIFI_ENABLE_EXT=1 ... | true |
2,839,340,321 | Testing | mikaylagawarecki | closed | [
"release notes: releng",
"ciflow/binaries_wheel"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146739
* #145748
This reverts commit 5cd5b4d2d54c0220b92ee488dd36d789c9b60af3. | true |
2,839,333,662 | [audio hash update] update the pinned audio hash | pytorchupdatebot | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 18 | COLLABORATOR | This PR is auto-generated nightly by [this action](https://github.com/pytorch/pytorch/blob/main/.github/workflows/nightly.yml).
Update the pinned audio hash. | true |
2,839,332,048 | [dynamo][user-defined] Unify standard and non-standard __new__ codebase | anijain2305 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"keep-going"
] | 9 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146819
* __->__ #146737
* #146677
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,839,327,914 | Document dynamo | Raymo111 | closed | [
"better-engineering",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"module: compiled autograd"
] | 6 | MEMBER | Many files in dynamo are currently lacking file/module-level documentation, which makes it hard to know what they do at a glance and without digging into the code. This fixes that.
Note: documentation was AI-generated and could be incorrect, please review carefully.
cc @voznesenskym @penguinwu @EikanWang @jgong5 ... | true |
2,839,297,293 | [ca] log graph before reodering passes | xmfan | closed | [
"Merged",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"module: compiled autograd"
] | 1 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147021
* #146875
* __->__ #146735
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames @StrongerXi @yf225 | true |
2,839,287,326 | [CUDA][CUDNN][SDPA] Pass dropout seed and offset to cuDNN in `int64` | eqy | closed | [
"module: cudnn",
"module: cuda",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: sdpa"
] | 12 | COLLABORATOR | Workaround for limitation in cuDNN that does not accept dropout seed/offset in `int32` for SM 10.0 kernels.
cc @csarofeen @ptrblck @xwang233 @msaroufim | true |
2,839,286,302 | [CUDA][SDPA] Don't dispatch to mem eff attn for batch_size >= 65536 | eqy | open | [
"module: cuda",
"open source",
"Stale",
"topic: not user facing",
"module: sdpa"
] | 3 | COLLABORATOR | #146704
cc @ptrblck @msaroufim | true |
2,839,274,170 | increase lwork/rwork sizes for all float->int conversions | wdvr | open | [
"triaged",
"module: linear algebra"
] | 0 | CONTRIBUTOR | This is a follow up to https://github.com/pytorch/pytorch/issues/145801 and https://github.com/pytorch/pytorch/pull/146456.
To do:
- extract the solution in https://github.com/pytorch/pytorch/pull/146456 to a method
- call the method in all lapack functions
cc @jianyuh @nikitaved @pearu @mruberry @walterddr @xwang23... | true |
2,839,234,257 | dont specialize symints when testing truthiness | bdhirsh | closed | [
"Merged",
"ciflow/trunk",
"release notes: composability",
"module: dynamo",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #133044
* __->__ #146731
* #146729
* #146642
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,839,226,304 | [BaseHOP] change hop(subgraph, operands) to hop(subgraph, *operands) | zou3519 | closed | [
"Merged",
"ciflow/trunk",
"release notes: foreach_frontend",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 10 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146730
Our three main users are OK with this, with two of them (foreach_map,
invoke_quant) prefering it like this.
I was originally worried about BC issues (this now means you cannot add
any positional args) but I think that's not a... | true |
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