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 |
|---|---|---|---|---|---|---|---|---|
2,807,880,256 | Fix IdentationError of code example | pytorchbot | closed | [
"open source"
] | 3 | COLLABORATOR | I found there is IndentationError when try to copy paste the example of inference with torch.compile
fix the format in this pr
| true |
2,807,870,263 | Only include RMSNorm.h in layer_norm.cpp for MPS | manuelcandales | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 4 | CONTRIBUTOR | Test Plan: CI
Differential Revision: D68578213
| true |
2,807,867,465 | inductor.config.descriptive_names = False is not actually supported | exclamaforte | closed | [
"Merged",
"Reverted",
"Stale",
"ciflow/trunk",
"topic: deprecation",
"module: inductor",
"ciflow/inductor",
"release notes: inductor",
"ci-no-td"
] | 14 | CONTRIBUTOR | Summary:
This config is not supported (it throws an error when set), and doesn't really make sense imo.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang ... | true |
2,807,855,167 | use_const_ref_for_mutable_tensors doesn't work with out= overloads | ezyang | open | [
"triaged",
"module: pt2-dispatcher"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
Repro:
```
import torch
from torch._subclasses.fake_tensor import FakeTensorMode
from torch._dispatch.python import enable_python_dispatcher
from torch.fx.experimental.symbolic_shapes import ShapeEnv, DimDynamic, SymNode
from torch import SymInt
import torch._dynamo
def f():
shape_env = S... | true |
2,807,822,889 | General Changes for multi accelerators | rahulsingh-intel | open | [
"oncall: distributed",
"triaged",
"open source",
"release notes: distributed (fsdp)"
] | 36 | CONTRIBUTOR | Intend to generailze the framework for multiple accelerators.
Major changes includes:
> Add TEST_CUDA & TEST_HPU condition for generalization at common place.
> Move ".cuda()" to ".to(device_type)" call
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,807,811,876 | Fix allow_mutation_on_saved_tensors for inplace foreach | soulitzer | closed | [
"Merged",
"release notes: autograd",
"topic: bug fixes"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #145399
* #145533
* #145531
* __->__ #145520
| true |
2,807,806,834 | [dynamo][trace-rules-cleanup] Remove functools from the Builtins skiplist | anijain2305 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #145559
* #145558
* #145547
* __->__ #145519
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @deser... | true |
2,807,787,494 | [ROCM MI300 skips for flaky unit tests | jataylo | closed | [
"module: rocm",
"open source",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"ciflow/rocm",
"ciflow/inductor-rocm"
] | 6 | COLLABORATOR | Temporary skips to deal with the current CI disable issues due to MI300, this will allow us to run these tests again on MI200 CI.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @hongxiayang @naromero77amd @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaoz... | true |
2,807,766,045 | Document dispatch trace build flag | albanD | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | COLLABORATOR | Ok, the build flag seems to have been broken for a while since the function it calls doesn't exist anymore.
Repurposed it to enable dispatcher printing (which requires a full (and slow) debug build otherwise). | true |
2,807,725,961 | Make debugging flaky tests easier by having relevant logs in one place | janeyx99 | closed | [
"triaged",
"module: devx"
] | 2 | CONTRIBUTOR | ## Problem
@mikaylagawarecki and I were looking into https://github.com/pytorch/pytorch/issues/81732, a test that has been flaky for a while.
Following the "recent examples" of the issue body leads us to https://hud.pytorch.org/flakytest?name=test_non_contiguous_tensors_nn_ConvTranspose1d_cuda_complex32&suite=TestMod... | true |
2,807,703,779 | [inductor][5/N] triton support post-#5512, fix 1 and None handling | davidberard98 | closed | [
"Merged",
"ciflow/trunk",
"topic: bug fixes",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 15 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #145515
This fixes handling for "1" and "None" args with new Triton versions. TL;DR: triton_meta["constants"] (which is passed to ASTSource) should be a map of {"kwarg_name": constant_value} for values which are tl.constexpr, or have... | true |
2,807,668,789 | Can't properly implement backward method for custom op in C++ when the op takes List of tensors as argument | borisfom | closed | [
"module: cpp",
"triaged",
"module: custom-operators"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
We have to define our custom ops in C++ because I need to run them in C++ (see https://github.com/pytorch/pytorch/issues/143773).
I was able to port most of our custom ops from Python to C++ successfully, except for one case when operation takes inputs as a list of tensors. Below is a short rep... | true |
2,807,662,721 | Don't fail if fresh_inductor_cache fails to clean up its tmp dir. | masnesral | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #145513
Summary: I see we have a test failure due to an error removing the tmp dir: https://github.com/pytorch/pytorch/issues/141761. Seems like we should not raise an exception for this case in general. Also, let's clean up the excep... | true |
2,807,649,485 | Add missing autoreleasepool around runUniqueGraph to prevent leaks | jhavukainen | closed | [
"module: memory usage",
"triaged",
"open source",
"Merged",
"module: mps",
"release notes: mps",
"ciflow/mps"
] | 5 | COLLABORATOR | References were held onto longer than needed. Added autoreleasepool around the runUniqueGraph to allow the memory to be freed.
Fixes #145151
cc @kulinseth @albanD @malfet @DenisVieriu97 | true |
2,807,646,776 | Activation Checkpointing composability with split backward computation | H-Huang | open | [
"oncall: distributed",
"module: activation checkpointing",
"triaged",
"module: pipelining"
] | 1 | MEMBER | Activation checkpointing avoids saving intermediate tensors in order to save memory. It does so by recomputing the forward pass on demand to obtain the intermediate values required for gradient computation during backward.
For pipelining, we are splitting up the backward computation into `stage_backward_input` and `st... | true |
2,807,637,955 | Add istft option to align window for center = false | jackzhxng | closed | [
"Stale",
"release notes: onnx"
] | 2 | CONTRIBUTOR | Following up from https://github.com/pytorch/pytorch/pull/145324, also add the `align_to_window` parameter for the inverse short fourier transform op.
_PENDING: stft round trip tests for center = false and align_window = true_
Pr chain:
- [Advance past fc window for stft center #145437](https://github.com/pytorc... | true |
2,807,601,072 | [dynamo][guards] Log guard latency to tlparse | anijain2305 | 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):
* #145132
* __->__ #145509
Example
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @j... | true |
2,807,587,301 | [ROCm] Bump AOTriton to 0.8.2b | xinyazhang | closed | [
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"keep-going",
"ciflow/rocm"
] | 6 | COLLABORATOR | We received reports AOTriton kernels mishandles the bias pointer and it causes NaN during fine-tuning llama3.2-11b vision model. This PR will fix the problem.
Note: this AOTriton 0.8.1b adds head dimension 512 support and thus the binary size increases, but it is considered experimental and will not be enabled righ... | true |
2,807,578,078 | [c10d] fix memory leak on shutdown | c-p-i-o | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 6 | CONTRIBUTOR | Summary:
Fix memory leak on shutdown when socket is closed.
We still need to free the buffer to make valgrind happy.
Test Plan:
Use `mtiavm`.
Repro steps provided by cristianlume.
on window 1:
```
vm ssh --vm=0 -- $(buck run @//neteng/ai/rdma_gen/mode/owl //neteng/ai/rdma_gen:rdma_gen --emit-shell) --rd... | true |
2,807,575,914 | serde and_ operator | avikchaudhuri | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: export"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #145506
Differential Revision: [D68565887](https://our.internmc.facebook.com/intern/diff/D68565887/) | true |
2,807,550,069 | Revert "Reverting the PR adding Kleidiai-based int4 kernels (#145392)" | nikhil-arm | closed | [
"module: cpu",
"open source",
"Merged",
"ciflow/trunk",
"release notes: linalg_frontend",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"arm priority"
] | 4 | COLLABORATOR | https://github.com/pytorch/pytorch/pull/134124 was reverted by https://github.com/pytorch/pytorch/pull/145392 due to KleidiAI clone issue.
1. This reverts commit 0940eb6d44f3cf69dd840db990245cbe1f78e770 (https://github.com/pytorch/pytorch/pull/145392 )and Fixes KleidiAI mirror issue.
2. KleidiAI is now cloned from ... | true |
2,807,528,915 | Resolve zip file permission issue when uploading artifacts on ROCm MI300 CI runners | amdfaa | closed | [
"module: rocm",
"open source",
"Merged",
"topic: not user facing",
"ciflow/unstable",
"ciflow/rocm",
"ci-no-td"
] | 10 | CONTRIBUTOR | E.g.: https://github.com/pytorch/pytorch/actions/runs/13500418791/job/37719437613#step:19:120
```
Beginning upload of artifact content to blob storage
Error: An error has occurred while creating the zip file for upload
Error: EACCES: permission denied, open '/home/runner/_work/pytorch/pytorch/test/test-reports/back... | true |
2,807,528,896 | Missing autorelease in lstm_mps caused a ton of leaked memory | jhavukainen | closed | [
"open source",
"Merged",
"release notes: mps",
"ciflow/mps"
] | 4 | COLLABORATOR | The dictionary held onto the new MPSGraphTensorData objects and MPSNDArrays. Regression caused by https://github.com/pytorch/pytorch/pull/95137
Fixes #145374
| true |
2,807,522,519 | [Submodule] Add flash as third-party submodule [Prep for later PRs] | drisspg | closed | [
"module: cuda",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 12 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #145502
# Context
Prototyped here: https://github.com/pytorch/pytorch/pull/144120, we are going to make flash-attention a 3rd party submodule. We will then use the c++ sources and include into our build of libtorch.so
This requ... | true |
2,807,520,186 | pip failure when trying to download nightly whl from pytorch.download.org : ERROR: THESE PACKAGES DO NOT MATCH THE HASHES FROM THE REQUIREMENTS FILE | atalman | open | [
"module: binaries",
"triaged"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
This is the failure on Jan 23, 2025:
https://github.com/pytorch/pytorch/actions/runs/12926060185/job/36048291540#step:4:124
Log:
```
Downloading https://download.pytorch.org/whl/nightly/cpu/torch-2.7.0.dev20250123%2Bcpu-cp39-cp39-manylinux_2_28_x86_64.whl (176.2 MB)
━━━━━━━━━━━━━━━━━━━━━━━━... | true |
2,807,503,343 | `torch._inductor.aoti_compile_and_package` fails when using dynamic shapes (PyTorch 2.6.0 RC) | dt-synth | closed | [
"high priority",
"triaged",
"oncall: pt2",
"module: dynamic shapes",
"oncall: export",
"module: aotinductor"
] | 7 | NONE | ### 🐛 Describe the bug
When I try to aot compile a simple example with dynamic shapes, it fails due to invalid generated C++ code.
Example:
```python
import torch
class Model(torch.nn.Module):
def forward(self, x):
return x + 1
model = Model().eval()
input = torch.randn(10)
dim = torch.export.Dim("d... | true |
2,807,461,442 | Remove truncated normal initialization for 16-bit (and lower) tensors | hjlee1371 | closed | [
"module: nn",
"triaged",
"open source",
"Stale"
] | 4 | NONE | Fixes #145498
cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki | true |
2,807,460,897 | Unexpected behavior of `torch.nn.init.trunc_normal` with bf16 tensors | hjlee1371 | open | [
"module: distributions",
"module: nn",
"module: cpu",
"triaged",
"module: random"
] | 5 | NONE | ### 🐛 Describe the bug
Hi, I found that `torch.nn.init.trunc_normal` produces unexpected output for bf16 tensors due to the precision issue. When running the following code,
```python
import torch
fp32_tensor = torch.empty(100000)
bf16_tensor = torch.empty(100000).bfloat16()
torch.nn.init.trunc_normal_(fp32_tensor... | true |
2,807,452,155 | qmul.cpp:34:10: error: redefinition of 'xnn_binary_params' 34 | struct xnn_binary_params { | ^ | dbl001 | closed | [
"module: build",
"topic: build"
] | 1 | NONE | ### 🐛 Describe the bug
I am getting errors building pytorch on MacOS 15.2 on a 2021 iMac 27": qmul.cpp:34:10: error: redefinition of 'xnn_binary_params'
34 | struct xnn_binary_params {
| ^
I got these error before and made changes to work around the issue(s) in: qmul.cpp, AveragePooling.cpp, Activ... | true |
2,807,370,630 | Link to `third_party/eigen` git submodule is broken | BwL1289 | closed | [
"module: third_party"
] | 1 | NONE | ### 🐛 Describe the bug
The link to `third_party/eigen @ 3147391` git submod is [broken](https://github.com/pytorch/pytorch/tree/bf4f8919df8ee88e356b407bb84ed818ebfb407b/third_party/eigen).
Which begs a larger question: if building `PyTorch` from source, is `eigen` really required if it's not being used as a `BLAS` ... | true |
2,807,355,377 | [compiled_autograd] Rename interface to pyinterface | zou3519 | closed | [
"fb-exported",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"ci-no-td"
] | 10 | CONTRIBUTOR | Summary: interface is a reserved word in some MSVC variants.
Test Plan: build
Differential Revision: D68561379
| true |
2,807,335,613 | [inductor] Make triton kernel autotune config defaults backward-compatible | bertmaher | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 14 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #145494
If a model was torch.packaged using triton<=3.1, any user-defined
autotuned kernels will have reps/warmups burned in with the old defaults
(100/25). If this model is loaded with triton>=3.2, inductor's checks for
unsupported ... | true |
2,807,331,172 | Add a lint rule to avoid the word `interface` in C++ | zou3519 | open | [
"module: cpp",
"module: lint",
"triaged"
] | 2 | CONTRIBUTOR | This is a reserved word in some msvc implementations internally
cc @jbschlosser | true |
2,807,263,087 | [BE]: Fix OrderedSet equality oversight | Skylion007 | closed | [
"open source",
"topic: bug fixes"
] | 2 | COLLABORATOR | Test to see if #145489 even causes behavior difference in test suite
| true |
2,807,256,201 | Cannot print symbolic tensors from C++ | ezyang | open | [
"module: cpp",
"triaged",
"oncall: pt2",
"module: dynamic shapes"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
Steps to reproduce:
```
std::cerr << tensor;
```
where tensor is a Tensor with symbolic sizes (e.g., a fake tensor with a ShapeEnv and symbolic shapes).
It fails with:
```
RuntimeError: Cannot call numel() on tensor with symbolic sizes/strides ... | true |
2,807,255,061 | cpp_wrapper: Move #includes to per-device header files | benjaminglass1 | closed | [
"open source",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ciflow/rocm",
"ciflow/xpu"
] | 8 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #145490
This prepares us for the next PR in the stack, where we introduce pre-compiled per-device header files to save compilation time.
Re-lands #145083 after merge conflicts.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @... | true |
2,807,244,641 | OrderedSet is backed by normal Dict, does not check ordering in equality | Skylion007 | closed | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 2 | COLLABORATOR | ### 🐛 Describe the bug
One concering thing I noticed is that OrderedDict is backed by a normal dictionary and not an OrderedDictionary. In recent versions of Python, these are effectively same with one important difference, OrderedDict equality requires that the elements are in the same order, while our OrderedSet do... | true |
2,807,214,200 | [torchbench] Add meta function for _cudnn_rnn_flatten_weight | IvanKobzarev | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #145488
https://github.com/pytorch/pytorch/issues/144989
This fixes tts_angular model on torchbench for `--export-aot-inductor`
I put meta function in cpp, as shape calculation requires cudnn API calls.
I've extracted shape ca... | true |
2,807,171,298 | Remove unnecessary "special linking" for `BLAS_LIBRARIES` | mgorny | closed | [
"module: build",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 11 | CONTRIBUTOR | Remove the "special linking" that involves listing `BLAS_LIBRARIES` thrice if `TH_BINARY_BUILD` is set, as it should not be any different from listing it just once.
The code seems to date back to commit cfcf2af95f91a88ec61cbcac8b30a718e7332aa5. The original code already listed `BLAS_LIBRARIES` thrice, but it provide... | true |
2,807,155,059 | feat: add SVE dispatch for non-FBGEMM qembeddingbag | Sqvid | closed | [
"module: cpu",
"triaged",
"open source",
"module: arm",
"release notes: quantization"
] | 6 | CONTRIBUTOR | Adds an accelerated kernel for `quantized::embedding_bag_byte` and integrates it with the dispatch mechanism.
The bulk of the SVE code has already been seen before and can be found here: https://github.com/pytorch/pytorch/pull/139753.
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @malfet @sn... | true |
2,806,994,623 | torch.jit.trace wrong function mapping: > maps to aten::lt | vkovinicTT | closed | [] | 2 | NONE | ### 🐛 Bug description
When running `torch.jit.trace()` on `nn.Module` sign '>' gets mapped to operation `aten::lt` instead of `aten::gt`.
```python
import torch
input_tensor = torch.zeros(1, 18, dtype=torch.float32)
class GT(torch.nn.Module):
def __init__(self):
super().__init__()
self.thresho... | true |
2,806,917,728 | Add a test for onnx exporter: export in a file | xadupre | closed | [
"open source",
"topic: not user facing"
] | 2 | COLLABORATOR | null | true |
2,806,906,943 | emphasized two words in the code | shaymolcho | closed | [
"open source",
"topic: not user facing"
] | 3 | NONE | I emphasized two words in the code to maintain a consistent text format.
Fixes #ISSUE_NUMBER
| true |
2,806,874,593 | [ca][bug_fix] Fix ref counting of objects in the set_autograd_compiler function. | BartlomiejStemborowski | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"module: compiled autograd"
] | 6 | CONTRIBUTOR | PR#141153 exposed the option to collect sizes as dynamic. After this
change, the function set_autograd_compiler returns PyTuple object which
is populated using PyTuple_SET_ITEM function. Yet, that function steals
reference to the object and doesn't INCREF it. So currently we are
missing INCREF on prior_compiler whe... | true |
2,806,829,033 | [custom ops] [2.7 nightly] custom ops with typing.List breaks when importing annotations from future | dabeschte | closed | [
"high priority",
"triaged",
"module: custom-operators",
"oncall: pt2",
"module: pt2-dispatcher"
] | 3 | NONE | ### 🐛 Describe the bug
The latest nightly version does not support `typing.List` in combination with `from __future__ import annotations`, but requires the use of `list`. While I agree that using `list` is better and more modern, this introduces a breaking change that makes it difficult to keep the custom ops working... | true |
2,806,701,573 | simplify torch.utils.cpp_extension.include_paths; use it in cpp_builder | h-vetinari | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 13 | CONTRIBUTOR | While working on conda-forge integration, I needed to look at the way the include paths are calculated, and noticed an avoidable duplication between `torch/utils/cpp_extension.py` and `torch/_inductor/cpp_builder.py`. The latter already imports the former anyway, so simply reuse the same function.
Furthermore, remov... | true |
2,806,657,364 | [Dynamo] Fix names collisions with foreach decomps | mlazos | closed | [
"Merged",
"ciflow/trunk",
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo"
] | 6 | CONTRIBUTOR | Fixes https://github.com/pytorch/pytorch/issues/138698
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,806,648,118 | Modify enable logic of COLLECTIVE_COMM profiler activity type | jushg | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 10 | CONTRIBUTOR | Summary:
Since `KINETO_NCCL_PROFILER` flag is not used anymore (we are moving from linking the profiler during compile time to loading it dynamically), we change the logic for enabling the profiler to use `TORCH_PROFILER_ENABLE_COLLECTIVE_PROFILING` environment variable for NCCL Collective Communication Profiler.
For... | true |
2,806,546,902 | [XPU] torch 2.7.0.dev20250121+xpu Import Error | qwebug | closed | [
"triaged",
"module: xpu"
] | 7 | NONE | ### 🐛 Describe the bug
It brings import error after updating PyTorch version from 2.5.1+xpu to 2.7.0.dev20250121+xpu.
```python
import torch
torch.xpu.is_available()
```
### **Error Logs**
```bash
Traceback (most recent call last):
File "/xxx/test.py", line 1, in <module>
import torch
File "/home/xxx/anac... | true |
2,806,500,917 | Adapt Dynamo Tests to HPUs | amathewc | closed | [
"triaged",
"open source",
"topic: not user facing",
"module: dynamo"
] | 16 | CONTRIBUTOR | This PR is a continuation of https://github.com/pytorch/pytorch/pull/144387 . Adapted two more files with the approach described below.
#MOTIVATION
We recently integrated support for Intel Gaudi devices (identified as 'hpu') into the common_device_type framework via the pull request at https://github.com/pytorch... | true |
2,806,469,580 | [dynamo] added support to trace torch.cuda.is_current_stream_capturing | chenyang78 | closed | [
"Stale",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"ciflow/rocm"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #145475
enabled tracing torch.cuda.is_current_stream_capturing
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @kadeng @chauhang @amjames | true |
2,806,456,805 | fix test_convolution error when use cudnn.flags | mengph | closed | [
"module: cudnn",
"module: convolution",
"triaged",
"open source",
"topic: not user facing"
] | 2 | CONTRIBUTOR | Fixes #145473
cc @csarofeen @ptrblck @xwang233 @eqy | true |
2,806,434,387 | torch.backends.cudnn.flags use error when test | mengph | closed | [
"module: cudnn",
"module: convolution",
"triaged"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
https://github.com/pytorch/pytorch/blob/main/test/nn/test_convolution.py#L3311
There is a problem with the use of cudnn.flags() here. The original purpose was to test the accuracy of allow_tf32 when it was turned on and off, but the call to cudnn.flags() causes allow_tf32 to always be True.
`... | true |
2,806,431,221 | torch.backends.cudnn.flags use error when test | mengph | closed | [] | 0 | CONTRIBUTOR | https://github.com/pytorch/pytorch/blob/main/test/nn/test_convolution.py#L3311
There is a problem with the use of cudnn.flags() here. The original purpose was to test the accuracy of allow_tf32 when it was turned on and off, but the call to cudnn.flags() causes allow_tf32 to always be True. | true |
2,806,384,481 | Is there a PyTorch version that can work properly on the Thor platform based on the Blackwell architecture? | wangxianggang1997 | open | [
"module: cuda",
"triaged"
] | 0 | NONE | Is there a PyTorch version that can work properly on the Thor platform based on the Blackwell architecture? I encounter many errors when compiling PyTorch source code on the Thor platform, and I don't know how to solve them. Is there any expert who can help me
cc @ptrblck @msaroufim @eqy | true |
2,806,254,541 | [inductor] [silence] `nn.LazyLinear-F.gumbel_softmax` return inconsistent resutls compared with eager | shaoyuyoung | closed | [
"oncall: pt2"
] | 5 | CONTRIBUTOR | ### 🐛 Describe the bug
**symptom:** for `nn.LazyLinear-F.gumbel_softmax`, if `hard=True`, we can see the explicit difference. If `hard=False`, the inconsistency still exists and can't pass the `allclose` check.
**device backend**: both triton and CPP
```python
import torch
import torch.nn as nn
import torch.nn.func... | true |
2,806,253,912 | libtorch_python.dylib not getting symlinked correctly in OSX 13 with pytorch-cpu | stefdoerr | closed | [] | 2 | NONE | ### 🐛 Describe the bug
Hi, I am installing the `pytorch-cpu` and `libtorch` packages on OSX 13 x86_64 and on OSX 14 Arm64 Github Action runners.
I noticed that on the OSX 14 Arm64 installation it installs the following packages:
```
pytorch-2.5.1 |cpu_generic_py310_h3256795_9 21.8 MB conda-... | true |
2,806,238,913 | [inductor][torchbench] Unsupported operator issue when running the torch_multimodal_clip model with batch size 4. | LifengWang | open | [
"module: nn",
"triaged",
"oncall: pt2",
"module: sdpa"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
I'm conducting an accuracy test for the `torch_multimodal_clip `model. I noticed that the graph breaks count is 0 when using a batch size of 1. However, when increasing the batch size to 4, the graph breaks count rises to 3. Upon printing the graph break details, I discovered the unsupported op... | true |
2,806,195,387 | [Intel GPU] Add TORCH_API macro to export symbol NestedTensor_to_mask for libtorch_xpu | min-jean-cho | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 12 | COLLABORATOR | Part of https://github.com/intel/torch-xpu-ops/issues/1141.
The `TORCH_API` macro is added to export the symbol `NestedTensor_to_mask`, which is needed by libtroch_xpu for `NestedTensor_softmax_dropout_xpu`.
| true |
2,806,170,426 | torch.sin/cos/tan+torch.floor/round may bring wrong results with torch.compile | qwqdlt | closed | [
"oncall: pt2"
] | 3 | NONE | ### 🐛 Describe the bug
torch.cos+torch.floor may bring wrong results with torch.compile on x86 CPU.
I find that torch.sin/cos/tan+torch.floor/round may also brings wrong results.
```python
import torch
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, *args):
... | true |
2,806,086,713 | OpenReg: Refactor impl_registry | Zhenbin-8 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 11 | CONTRIBUTOR | Refactor impl_registry to use `driver.exec` as fallback.
cc @albanD | true |
2,806,071,913 | [fx] low overhead checking of nondeterministic_seeded | xmfan | closed | [
"release notes: fx",
"fx"
] | 1 | MEMBER | FIXES https://github.com/pytorch/pytorch/issues/144775
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #145464
```
dev,name,batch_size,accuracy,calls_captured,unique_graphs,graph_breaks,unique_graph_breaks,autograd_captures,autograd_compiles,cudagraph_skips,compilation_laten... | true |
2,806,059,279 | Mark Dynamic does not work for nn module constructor inputs | laithsakka | closed | [
"triaged",
"module: dynamic shapes"
] | 2 | CONTRIBUTOR | for the following code if we compile it we get the following graph :
code:
```
import torch
import torch._dynamo.config
from torch.utils.checkpoint import _is_compiling
@torch.compile()
class Y(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x.view(-1, s... | true |
2,805,969,991 | [TEST ONLY] Conv with `oc = 0` | chunyuan-w | closed | [
"open source",
"ciflow/trunk",
"topic: not user facing"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #145462
| true |
2,805,951,390 | Incomplete check of LR as a tensor in Optimizer | Tony-Y | closed | [
"module: optimizer",
"triaged",
"actionable"
] | 4 | CONTRIBUTOR | ### 🐛 Describe the bug
A tutorial "[Running the compiled optimizer with an LR Scheduler](https://pytorch.org/tutorials/recipes/compiling_optimizer_lr_scheduler.html)" presents how LR as a tensor is used. According to this tutorial, we should use a 0-dim tensor for LR. However, Optimizer can accept a 1-dim tensor of s... | true |
2,805,889,854 | Flex Attention not support score_mod with gradients | LoserCheems | closed | [
"triaged",
"oncall: pt2",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention"
] | 2 | NONE | ### 🐛 Describe the bug
`Flex Attention` does not support `score_mod with gradients`, making it impossible to define a learnable score_mod for the dynamic mask attention variants.
```python
import torch
from torch.nn.attention.flex_attention import flex_attention
A = torch.nn.Parameter(torch.zeros(1))
query = torch.... | true |
2,805,888,568 | [AOTInductor] Align behavior between CPU and GPU | muchulee8 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor",
"merging"
] | 29 | CONTRIBUTOR | Summary:
(1) Make sure CPU and GPU doesn't have different implementation and behavior when calling from the same path and API. Only difference between CPU and GPU after this PR should ONLY be the running hardware.
(2) This PR fixes the issue of memory access when it==constants_map.end()
(3) This PR resolves T179437596
... | true |
2,805,873,069 | [c10d] Flush file in file recorder | c-p-i-o | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 12 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #145458
Summary:
Flushing file to hopefully prevent file corruptions as reported in
https://github.com/pytorch/pytorch/pull/145125
Test Plan:
Couldn't get file corruption to occur in my tests.
cc @H-Huang @awgu @kwen2501 @wanchaol @... | true |
2,805,829,310 | DISABLED test_tensor_subclass_basic (__main__.TestCompiledAutograd) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: inductor"
] | 4 | NONE | Platforms: asan, linux, rocm, mac, macos, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_tensor_subclass_basic&suite=TestCompiledAutograd&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/3602855783... | true |
2,805,822,844 | torch._dynamo.exc.Unsupported: Graph break due to unsupported builtin torch._C._dynamo.eval_frame.set_eval_frame. | taowenqi | open | [
"oncall: pt2",
"export-triaged",
"oncall: export"
] | 2 | NONE | ### 🐛 Describe the bug
Part of an example as the following:
```python
from torch_mlir import fx
module = fx.export_and_import(
model,
input_tensor,
output_type="tosa",
func_name="forward",
)
```
when I use pyinstaller to pack the fx graph export and import to a binary file, it encounters the follo... | true |
2,805,818,451 | Update TorchBench commit to main | huydhn | closed | [
"Merged",
"topic: not user facing",
"test-config/default",
"module: dynamo",
"ciflow/inductor",
"ciflow/inductor-perf-compare",
"no-runner-experiments",
"ciflow/inductor-periodic"
] | 7 | CONTRIBUTOR | I'm adding sam2 to TorchBench https://github.com/pytorch/benchmark/issues/2566, so, as part of that, I'm updating PyTorch CI to use latest TorchBench commit.
The corresponding change from TorchBench is https://github.com/pytorch/benchmark/pull/2584
The main thing to call out that the newer transformers added by h... | true |
2,805,815,556 | Add check that envvar configs are boolean | Raymo111 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 12 | MEMBER | So we don't get unexpected behavior when higher typed values are passed in | true |
2,805,814,674 | [CUDA] Illegal Memory Access with `AdaptiveMaxPool2d` | jwnhy | open | [
"module: nn",
"module: cuda",
"triaged",
"module: edge cases"
] | 0 | NONE | ### 🐛 Describe the bug
Found by fuzzer, the parameter used to trigger this does not look too corner-case.
@eqy
```python
import torch
m1 = torch.randn(8812, 1, 2).cuda()
model = torch.nn.AdaptiveMaxPool2d(output_size=[262143, 1]).cuda()
model(m1)
```
```bash
computer-sanitizer python3 poc7.py
```
compute-sanitiz... | true |
2,805,782,588 | Update TorchBench commit to main | huydhn | closed | [
"topic: not user facing",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | WIP, no need to review yet | true |
2,805,773,579 | [Dynamo]while_loop raise an exception | zhejiangxiaomai | open | [
"triaged",
"oncall: pt2",
"module: higher order operators",
"module: pt2-dispatcher"
] | 3 | NONE | ### 🐛 Describe the bug
while loop will raise an exception.
The reason is input tensor has been sliced.
```
tensor strided info: shape: torch.Size([192]), original_tshape: [64], slice:slice(None, None, 3)
```
This creates tensor with stride=3, but once the body_fn execute tensor+1 the stride is reset to 1 and this ... | true |
2,805,753,868 | Replace is_same with is_same_v for concise syntax | zeshengzong | closed | [
"module: cpu",
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: sparse",
"ci-no-td"
] | 28 | CONTRIBUTOR | Replace `std::is_same<T, U>::value` with `std::is_same_v` for concise and consistent syntax with other code.
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 | true |
2,805,747,931 | Fix incorrect type comparison | aorenste | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: fx",
"fx"
] | 6 | CONTRIBUTOR | Summary: This change was incorrectly made as part of #145166
Differential Revision: D68536221
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,805,701,539 | Record inputs at time of tracing, constrain to them for triton fn | eellison | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: fx",
"fx",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 17 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #145448
* #145953
Record input fake tensors at time of tracing and store them in the node meta. Inductor passes have the possibility of changing strides, so it is safer to record the strides of the inputs at tracing. See, https://git... | true |
2,805,696,593 | [dynamo][benchmarks] update compile time benchmarks to dump compile times to stdout and csv | xmfan | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 3 | MEMBER | ```python
# inductor.csv
dev,name,batch_size,accuracy,calls_captured,unique_graphs,graph_breaks,unique_graph_breaks,autograd_captures,autograd_compiles,cudagraph_skips,compilation_latency
cuda,cait_m36_384,8,pass,2510,1,0,0,0,0,0,87.705186
```
```python
loading model: 0it [01:27, ?it/s]
cuda eval cait_m36_384... | true |
2,805,694,771 | [dynamo] fix graph break on random.random | williamwen42 | open | [
"triaged",
"module: regression",
"oncall: pt2",
"module: dynamo"
] | 2 | MEMBER | We used to not graph break on `random.random`, but we do now.
```python
import random
import torch
@torch.compile(backend="eager", fullgraph=True)
def fn(x):
return x + random.random()
fn(torch.ones(5, 5))
```
This does not happen to other supported random functions - `randint`, `randrange`, `uniform`, liste... | true |
2,805,693,005 | [dynamo] `random.Random` gives wrong result on second call | williamwen42 | open | [
"triaged",
"oncall: pt2",
"module: dynamo",
"dynamo-triage-jan2025"
] | 0 | MEMBER | `random.Random` calls are not correct on the second call onward. This is because Dynamo generates RNG on `random.Random` objects by creating a new object and applying the RNG state at the time of tracing, which is not correct in general.
Example failing test:
```python
def test_random_object_repeat(self):
... | true |
2,805,691,790 | [Docs] Add clarification for target types in CrossEntropyLoss doc | spzala | closed | [
"module: nn",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | CONTRIBUTOR | CrossEntropyLoss function requires that target for class indices are provided as a long and class probabilities are provided as a float datatype.
The CrossEntropyLoss function distinguish the two scenarios (indices and probabilities) by comparing the shapes. When input and target shapes are the same it’s a case for ... | true |
2,805,684,813 | [draft_export] add LOC for data-dep error logging | pianpwk | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: export"
] | 8 | CONTRIBUTOR | Summary:
maybe this is too much info, but it's difficult to go through old draft export reports where the stack trace is out of sync with the current codebase. Data-dependent errors now look like:
```
2. Data dependent error.
When exporting, we were unable to evaluate the value of `u306`.
This occurred at ... | true |
2,805,671,221 | [ONNX] Remove LegacyDynamoStrategy | justinchuby | closed | [
"module: onnx",
"open source",
"Merged",
"ciflow/trunk",
"release notes: onnx",
"topic: improvements"
] | 6 | COLLABORATOR | It's legacy. So remove. Shouldn't affect anything and will facilitate cleaning up our legacy code. | true |
2,805,664,674 | seg fault in aot_inductor_package on arm GPU with 2.6.0 RC | tinglvv | closed | [
"high priority",
"module: crash",
"triaged",
"module: regression",
"oncall: pt2",
"oncall: cpu inductor"
] | 16 | COLLABORATOR | ### 🐛 Describe the bug
When running internal test for 2.6.0 RC ARM wheels (https://download.pytorch.org/whl/test/torch/) on Grace Hopper 1GPU, getting seg fault/bus errors which are happening alternating on below test
Reproduced errors on both CUDA and CPU wheels.
```python test/inductor/test_aot_inductor_package.p... | true |
2,805,663,942 | reset dynamo cache for Inductor tests under test_ops_gradients.py | shunting314 | closed | [
"ciflow/trunk",
"ciflow/inductor",
"keep-going"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #145440
| true |
2,805,662,057 | Add check that envvar configs are boolean | Raymo111 | closed | [
"topic: not user facing"
] | 1 | MEMBER | So we don't get unexpected behavior when higher typed values are passed in | true |
2,805,649,878 | Backout PEP585 use of Iterable | aorenste | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: dataloader",
"topic: not user facing"
] | 4 | CONTRIBUTOR | Summary:
Importing Iterable from collections.abc here causes an internal product to fail
MRO discovery causing a collision between Iterable and Generic.
This fixes the failure on D68461304
Differential Revision: D68531443
| true |
2,805,644,973 | Advance past fc window for stft center | jackzhxng | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"ciflow/slow",
"ci-no-td"
] | 14 | CONTRIBUTOR | Long overdue follow-up on https://github.com/pytorch/pytorch/pull/73432/files#diff-5f3d4caa0693a716fc46fd7f6339312f1b5f0bf89e3a3ff58e9dc13a9486b17aR719
Onnx stft doesn't support centering, [and all of the existing tests are for center = False](https://github.com/pytorch/pytorch/blob/main/test/onnx/test_pytorch_onnx_... | true |
2,805,642,948 | [NVIDIA] Full Family Blackwell Support codegen | johnnynunez | closed | [
"module: cuda",
"open source",
"Merged",
"ciflow/trunk",
"release notes: build"
] | 23 | CONTRIBUTOR | cc @ptrblck @msaroufim @eqy @Fuzzkatt
More references:
https://github.com/NVIDIA/nccl
| true |
2,805,637,186 | [dynamo] save/restore system random state more carefully | williamwen42 | closed | [
"ciflow/trunk",
"topic: bug fixes",
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo"
] | 10 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #145435
Fixes https://github.com/pytorch/pytorch/issues/145329.
We we need to save/restore system `random` state in 2 places:
- in `eval_frame.py`, we need to make sure that wrapper code between the user call to a `torch.compile`... | true |
2,805,621,015 | Crash in wrapper_benchmark.py with --profile enabled | mgraczyk | closed | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 4 | CONTRIBUTOR | ### 🐛 Describe the bug
Generated code crashes when compiled output code is run with profiling.
## Steps to reproduce
Run the following with the latest version of pytorch
```
# Generate the output code
$ TORCH_LOGS_FORMAT="" TORCH_LOGS=output_code python 2>/tmp/output.py <<EOF
import torch, sys
print("# torch vers... | true |
2,805,612,037 | XPU - UserWarning: Failed to initialize XPU devices. when run on the host without Intel GPU Driver | atalman | closed | [
"triaged",
"module: xpu"
] | 6 | CONTRIBUTOR | ### 🐛 Describe the bug
Based on following issue: https://github.com/pytorch/pytorch/issues/145290#issuecomment-2606374858
Test on Linux x86 without Intel Driver installed:
```
pip install --pre torch --index-url https://download.pytorch.org/whl/test/xpu
Looking in indexes: https://download.pytorch.org/whl/test/xpu... | true |
2,805,608,959 | [dynamo][dicts] Insert LENTGH guard on an if condition on dict | 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):
* __->__ #145432
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,805,592,336 | [dynamo] Re-enable `test_torch_name_rule_map_updated` | StrongerXi | closed | [
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #145431
This patch re-enables `test_torch_name_rule_map_updated` and adds
relevant fixes for the failures.
Fixes #114831.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @j... | true |
2,805,589,977 | [c10/metal] Add a vectype variant for `short`/`int`/`long` | dcci | closed | [
"Merged",
"topic: not user facing",
"module: mps",
"ciflow/mps",
"module: inductor"
] | 4 | MEMBER | Some of the kernels (exp_complex/atan_complex) need the specialization.
cc @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @des... | true |
2,805,586,380 | [ca][hop] test CA on all HOPs | xmfan | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 6 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #145429
* #145422
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aak... | true |
2,805,583,543 | torch._neg_view correctness | FindHao | closed | [
"module: correctness (silent)",
"topic: not user facing"
] | 1 | MEMBER | ### 🐛 Describe the bug
```
import torch
device = "cuda"
dtype = torch.float64
# Basic test for negative view
x = torch.randn(20, 20, device=device, dtype=dtype, requires_grad=False)
physical_neg = torch.neg(x)
view_neg = torch._neg_view(x)
assert torch.is_neg(view_neg), "view_neg should be negative"
assert not tor... | true |
2,805,580,914 | add pt2 callbacks for backward pass | burak-turk | closed | [
"fb-exported",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Summary: This change adds callbacks for lazy backwards compilation.
Differential Revision: D68515699
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aak... | true |
2,805,574,923 | Fix aot inductor intermediate debug printing | exclamaforte | closed | [
"ciflow/trunk",
"topic: bug fixes",
"module: inductor",
"ciflow/inductor",
"release notes: inductor",
"module: aotinductor"
] | 13 | CONTRIBUTOR | Fixes https://github.com/pytorch/pytorch/issues/145425
The other way to fix this would be to change `_print_debugging_tensor_value_info` to handle constants.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @m... | true |
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