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 |
|---|---|---|---|---|---|---|---|---|
2,989,714,486 | Add export specific tests for dynamo and export | Lucaskabela | closed | [
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151588
* __->__ #151135
* #151134
* #151133
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,989,714,401 | Add functionality for installing free variables | Lucaskabela | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151134
* #152036
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,989,714,274 | add basic unit tests and noop config | Lucaskabela | closed | [
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151588
* #151135
* #151134
* __->__ #151133
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,989,684,535 | [AMD] Block mem efficient attention for FP32 in CK backend | xw285cornell | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 7 | CONTRIBUTOR | Summary: CK doesn't support FP32 attention, but aotriton does. If we prefer CK, and the input dtype is FP32, we'll select mem efficient attention but CK doesn't support it. So we'll exclude mem eff attention and pick math.
Differential Revision: D72880985
| true |
2,989,611,599 | [dynamo] Use sentinel value for guard filter. | zhxchen17 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Summary: `None` can collide with the real values in the scope, so we should use a separate value. Also added "has_value" to the struct so that it's more clear whether the value is absent or not.
Test Plan: CI
Differential Revision: D72881300
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSu... | true |
2,989,526,907 | Using hasattr for `_boxed_call` is asking for trouble | aorenste | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Summary:
There are a number of places in the code checking for the existence of `_boxed_call` instead of checking for a `True` value. This is somewhat dangerous because one would assume that setting it to `None` or `False` would be the same as not setting it (output_code.py does this, for example).
Change `hasattr()` ... | true |
2,989,499,174 | Fix setUpClass() / tearDownClass() for device-specific tests | jbschlosser | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 22 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151129
Finishes up the work started in #121686 + adds test
Update: this was not as straightforward as I originally imagined. Context below.
**TL;DR:** `TestFoo{CPU, CUDA}` now actually derive from `TestFoo`! Also, `{CPU, CUDA}... | true |
2,989,478,881 | Reapply "Support tuning of _scaled_grouped_mm (#150421)" | bertmaher | closed | [
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151128
This reverts commit 6a65f2c4feb91f6dcc8b2879962b7f8badc3eac6.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amja... | true |
2,989,472,948 | Wrong handling of deferred == and != runtime checks in torch.compile! | laithsakka | open | [
"triaged",
"oncall: pt2",
"module: dynamic shapes",
"module: inductor"
] | 0 | CONTRIBUTOR | Two problems:
1) for the following we do not recompile but do also do not perform runtime assertions the calls to
func(torch.tensor([100]), torch.tensor([1,2]))
func(torch.tensor([1]), torch.tensor([1,2]))
should fail but they do not !! Here we do not generate the runtime assertion because its true in the gra... | true |
2,989,472,392 | [aarch64] Fixes to build with ArmPL's cblas.h | andrewjcg | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | CONTRIBUTOR | Summary:
Various fixes to make fbcode work w/ ArmPL's cblas header:
1) Avoid re-declaring prototypes for internal blas methods which ArmPL already declares.
2) Fix `std::complex` conversion when using these methods.
3) Drop `extern "C"` around include fo `cblas.h`.
Test Plan: CI
Differential Revision: D72808561
| true |
2,989,344,962 | Guard additional use of DriverAPI | pganssle-google | open | [
"oncall: distributed",
"open source",
"ciflow/trunk",
"release notes: distributed (c10d)",
"topic: not user facing"
] | 10 | CONTRIBUTOR | Most uses of DriverAPI are guarded by `PYTORCH_C10_DRIVER_API_SUPPORTED`, but this use is not, which causes compilation errors when building without C10 DriverAPI support.
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k | true |
2,989,246,489 | [profiler][retry] don't disable CUPTI_LAZY_REINIT for cuda >= 12.6 | davidberard98 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: profiler",
"module: inductor",
"ciflow/inductor"
] | 9 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151124
Retry of https://github.com/pytorch/pytorch/pull/150957, which was reverted due to internal meta failures
Credit to @mgmtea who wrote the initial version of this PR: https://github.com/pytorch/pytorch/pull/146604
Contex... | true |
2,989,240,677 | Fix tensor_constant name collision in aot_export_module | yushangdi | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: fx",
"fx",
"module: inductor",
"ciflow/inductor"
] | 11 | CONTRIBUTOR | Summary:
When we have an exported program that looks like this:
```
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, b__tensor_constant0: "f32[1]", ... c_lifted_tensor_0: "i64[925]", …. , tupleized_input_0_0: "f32[10, 2139]",
clone: "i64[925]" = torch.ops.aten.clo... | true |
2,989,219,287 | Avoid guarding on ids of optional dictionary tensor values: | laithsakka | closed | [
"dynamo-dicts"
] | 2 | CONTRIBUTOR | is it possible to not have recompilation here, instead of guarding on the object id guard on weather its none or not?
````
@torch.compile()
def func(x):
for k, v in x.items():
if v is None:
return v*100
else:
return v*200
func({10:torch.tensor([1])})
func({20:torch.tensor([... | true |
2,989,215,736 | [ONNX] Fix bfloat16 support in onnx_program callable | justinchuby | closed | [
"module: onnx",
"open source",
"Merged",
"ciflow/trunk",
"release notes: onnx",
"topic: bug fixes"
] | 3 | COLLABORATOR | - Added a test to guard bfloat16. The optimizer incorrectly turns bfloat16 initializers into uint16, but this is not relevant to export logic.
- Fix bfloat16 support in onnx_program callable
Tested with the following with cuda
```py
import torch
class BfloatModel(torch.nn.Module):
def __init__(self):
... | true |
2,989,178,938 | Reland prologue transposed changes | eellison | open | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151120
* #151013
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,989,156,323 | hack to try to fix not empty triton dir | bertmaher | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 12 | CONTRIBUTOR | Differential Revision: D72741938
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,989,137,073 | Tune linalg_eigh_cusolver: better heuristic for syevj_batched selection on cuda | MauriceDHanisch | open | [
"triaged",
"open source",
"topic: not user facing"
] | 4 | NONE | This change is not tied to an open issue.
**Summary**
This PR updates the heuristics in linalg_eigh_cusolver for batched matrix diagonalization. The current logic only applies syevj_batched for matrix sizes ≤ 32, which is too conservative. In the favorable regions, this heuristic improves performance by more than 1... | true |
2,989,113,227 | Remove ls from filesystem base | ankitageorge | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"release notes: distributed (checkpoint)"
] | 4 | CONTRIBUTOR | Summary: User reported issue where they are inheriting from filesystembase but don't have the ls method which was added in the PR https://github.com/pytorch/pytorch/pull/150701#discussion_r2039840129. Removing the method from the base class but keeping it in derived class
Test Plan: buck test 'fbcode//mode/opt' fbcode... | true |
2,989,053,429 | TESTING: IGNORE | zxiiro | open | [
"open source",
"topic: not user facing"
] | 1 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,989,050,735 | [reland][AOTI] Add protocol field to OSS schema of ExternKernelNodes | yiming0416 | open | [
"fb-exported",
"module: inductor",
"ciflow/inductor",
"release notes: export",
"ci-no-td"
] | 2 | CONTRIBUTOR | Summary:
This diff adds a "protocol" field to `ExternKernelNodes` in the OSS AOTI schema.
Test Plan: CI
Differential Revision: D72804878
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames ... | true |
2,989,025,497 | DISABLED test_parity__foreach_acos_fastpath_outplace_cuda_float16 (__main__.TestForeachCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: mta"
] | 3 | NONE | Platforms: linux
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_parity__foreach_acos_fastpath_outplace_cuda_float16&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/40386187509).
Ov... | true |
2,988,899,885 | Failure in test_vmap_autograd_grad_nn_functional_conv2d_cpu_float32 | Flamefire | open | [
"module: tests",
"triaged",
"module: vmap",
"module: functorch"
] | 3 | COLLABORATOR | ### 🐛 Describe the bug
When running the test with `PYTORCH_OPINFO_SAMPLE_INPUT_INDEX=16 python functorch/test_ops.py TestOperatorsCPU.test_vmap_autograd_grad_nn_functional_conv2d_cpu_float32`
it fails with:
```
AssertionError: Tensor-likes are not close!
Mismatched elements: 2 / 144 (1.4%)
Greatest absolute differe... | true |
2,988,894,730 | [Quant][X86] add an op to compute uint8 pointwise mul | Xia-Weiwen | closed | [
"module: cpu",
"open source",
"Merged",
"ciflow/trunk",
"release notes: quantization",
"intel"
] | 5 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151112
**Summary**
Add a new op, `onednn.qmul.tensor`, for int8 elementwise mul, which accepts inputs on CPU device (instead of QuantizedCPU).
The new op is implemented by AVX512 instructions and it provides similar or better perf... | true |
2,988,863,344 | [Intel GPU] skip a cuda api call in amp to save some host overhead on xpu | jianyizh | closed | [
"triaged",
"open source",
"module: amp (automated mixed precision)",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 17 | CONTRIBUTOR | This can save ~0.2ms on non cuda devices by skip calling `amp_definitely_not_available()`. It can improve small models in torchbench like lennard_jones on xpu 10% on both eager and inductor in dynamo benchmarks.
cc @mcarilli @ptrblck @leslie-fang-intel @jgong5 | true |
2,988,837,503 | Update epsilon logic to improve numerical stability | albanD | open | [
"module: numerical-stability",
"module: bc-breaking",
"module: autograd",
"triaged"
] | 5 | COLLABORATOR | Many operations in PyTorch use a variety of "epsilon" to ensure numerical stability or avoid infinite value.
This is used in particular for normalization functions batch/rms/layer norm and optimizers.
These epsilons are usually added following paper formula or historical techniques, to improve uniformisation I would s... | true |
2,988,820,582 | [AOTI][reland] Remove typedef for half and bfloat16 | desertfire | open | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td",
"release notes: inductor (aoti)"
] | 11 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151109
Summary: Reland https://github.com/pytorch/pytorch/pull/150657
typedef is prone to name collision. Explicitly spell out the actual aten types, needed for the libtorch-free codegen.
cc @voznesenskym @penguinwu @EikanWang @jgo... | true |
2,988,624,263 | add sbgemv dispatch in torch cpu flash attention | taoye9 | open | [
"triaged",
"open source",
"module: arm",
"topic: not user facing"
] | 10 | NONE | # Summary
This PR introduces a dispatch to the OpenBLAS sbgemv kernel in PyTorch CPU Flash Attention kernel when the query sequence length is 1.
# Motivation
During the decoding phase in transformer models (e.g., for autoregressive inference), the shape of the query tensor often has sequence length = 1. Curren... | true |
2,988,469,718 | [HOP] Reworked DispatchKey.Autograd | bohnstingl | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo"
] | 7 | COLLABORATOR | This PR intends to rework the dispatching of the autograd key.
I.e., currently the DispatchKey.Autograd of the HOPs was triggered, even if non of the operands of the HOP have `requires_grad=True`. With this rework, the autograd is bypassed if non of the operands require gradients and only invoked if any of the operand... | true |
2,988,446,905 | distributed/tensor/_op_schema has_symints does not check args_schema | IvanKobzarev | open | [
"oncall: distributed",
"triaged",
"oncall: pt2",
"module: dynamic shapes",
"module: dtensor"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
distributed/tensor/_op_schema has_symints does not check args_schema
Then hash() that will reduce args will fail with error: `TypeError('unhashable type: non-nested SymInt')`
Potential fix:
```
└─ $ git diff
diff --git a/torch/distributed/tensor/_op_schema.py b/torch/distributed/tensor/_op_... | true |
2,988,351,869 | Whether `x` and `dx` can be used together in `torch.trapezoid()`? | ILCSFNO | closed | [
"module: docs",
"triaged",
"actionable",
"module: python frontend"
] | 4 | CONTRIBUTOR | ### 🐛 Describe the bug
The doc of [torch.trapezoid()](https://pytorch.org/docs/stable/generated/torch.trapezoid.html#torch-trapezoid) shows its description as below:
https://github.com/pytorch/pytorch/blob/d94cc0e9942087180305061dd693adff93448d2e/torch/_torch_docs.py#L12698-L12807
From the document, I can't find co... | true |
2,988,326,730 | Optional tag like `keepdim` may have been removed in several funcs | ILCSFNO | closed | [
"module: docs",
"triaged",
"actionable",
"module: python frontend"
] | 1 | CONTRIBUTOR | ### 📚 The doc issue
Seen from #146156 and its PR [pull#146485](https://github.com/pytorch/pytorch/pull/146485), maybe some other funcs also have optional tags removed:
What I met is `torch.any()`, but suggest fix this by change the description of `keepdim` and any other similar arguments, for that there may be more ... | true |
2,988,266,905 | Whether `recompute_scale_factor=True` needs `scale_factor` passed in or not in `torch.nn.Upsample()`? | ILCSFNO | closed | [
"module: nn",
"triaged"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
The doc of [torch.nn.Upsample()](https://pytorch.org/docs/stable/generated/torch.nn.Upsample.html#upsample) shows its argument as below:
https://github.com/pytorch/pytorch/blob/d94cc0e9942087180305061dd693adff93448d2e/torch/nn/modules/upsampling.py#L41-L48
But in repro:
### Minified Repro
```... | true |
2,988,227,690 | [BE] detect CXX pytree requirement with `TorchVersion` | XuehaiPan | closed | [
"open source",
"better-engineering",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 4 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148328
* #148180
* #137400
* #152624
* __->__ #151102
| true |
2,988,193,590 | The `size` of `x` can have more dims in `torch.cdist()` | ILCSFNO | closed | [
"module: docs",
"triaged",
"actionable",
"module: python frontend"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
The doc of [torch.cdist()](https://pytorch.org/docs/stable/generated/torch.cdist.html) shows its description as below:
https://github.com/pytorch/pytorch/blob/d94cc0e9942087180305061dd693adff93448d2e/torch/functional.py#L1462-L1464
But I tried repro below, it can run well, in which `size` of ... | true |
2,988,185,250 | Turn MemPool into a C++ custom class | lw | open | [] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150564
* __->__ #151100
* #150684
* #150683
| true |
2,988,133,285 | DISABLED test_parity__foreach_acos_fastpath_outplace_cuda_complex64 (__main__.TestForeachCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: mta"
] | 4 | NONE | Platforms: linux
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_parity__foreach_acos_fastpath_outplace_cuda_complex64&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/40370997282).
... | true |
2,988,027,356 | precision error for attention like-operation | syheliel | open | [
"high priority",
"triaged",
"oncall: pt2",
"module: inductor"
] | 5 | NONE | ### 🐛 Describe the bug
tlparse file: [dedicated_log_torch_trace_ve9nm2rv.log](https://github.com/user-attachments/files/19702029/dedicated_log_torch_trace_ve9nm2rv.log)
The result before and after torch.compile have a big difference:
```
Maximum error between out_normal and out_opt: 2.276662826538086 # first try
Maxi... | true |
2,987,951,350 | [XPU] Upgrade the XPU support packages version to 2025.1 in CI/CD | chuanqi129 | open | [
"triaged",
"module: xpu"
] | 0 | COLLABORATOR | As the XPU support packages [deep learning essential 2025.1](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html?packages=dl-essentials&dl-essentials-os=linux&dl-lin=offline) has been public released.
- [ ] **Dependencies**
- - [x] Land https://github.com/pytorch/kineto/pull/1066... | true |
2,987,640,355 | [Intel GPU][Windows] test_xpu.py::TestXpuXPU::test_lazy_init_xpu - subprocess.CalledProcessError | LuFinch | open | [
"triaged",
"module: xpu"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
When running UT test/test_xpu.py::TestXpuXPU::test_lazy_init_xpu on Windows, it fails with
```
File "C:\ProgramData\miniforge3\envs\lfq\lib\multiprocessing\spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "C:\ProgramData\miniforge3\envs\lfq\lib\multiprocessing... | true |
2,987,612,466 | improve noop elimination for view | BoyuanFeng | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | This PR improves noop elimination.
### View Noop
```python
>>> torch.Size([1,2,3]) == [1,2,3]
False
>>> torch.Size([1,2,3]) == (1,2,3)
True
```
So we add `tuple(size)` in `view_noop`.
Example:
```python
import torch
@torch.compile()
def f(x):
batch_size = x.shape[0]
x = x.transpose(1, 2)... | true |
2,987,508,165 | [torch.export] pytorch 2.7.0 torch.export failed and the error message is very confused | shykoe | closed | [
"oncall: pt2",
"oncall: export"
] | 2 | NONE | ### 🐛 Describe the bug
here is my code
```python
import torch.nn as nn
import torch
class Smelu(nn.Module):
def __init__(self, beta: float = 1.0):
super(Smelu, self).__init__()
self.beta = beta
def forward(self, x):
return torch.where(torch.abs(x) <= self.beta, ((x + self.beta) ** 2) ... | true |
2,987,470,553 | DISABLED test_parity__foreach_acos_fastpath_outplace_cuda_complex128 (__main__.TestForeachCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: mta"
] | 5 | NONE | Platforms: linux, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_parity__foreach_acos_fastpath_outplace_cuda_complex128&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/40359724... | true |
2,987,451,684 | [Intel GPU][PT2E] Register qconv impls to general qconv_pointwise schema | ZhiweiYan-96 | closed | [
"module: cpu",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor",
"keep-going",
"ciflow/xpu"
] | 4 | COLLABORATOR | # Motivation
Refer to https://github.com/pytorch/pytorch/pull/150751, general scheme for `qconv_pointwise` is added and `qconv2d_pointwise` is removed in callers. This PR registers the XPU backend implementations to this operator.
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ ... | true |
2,987,451,563 | [Openreg][PrivateUse1] Fix releasing tensor issue when using pin_memory | FFFrog | closed | [
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"ci-no-td"
] | 24 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151091
* #151007
As the title stated.
Related PR: https://github.com/pytorch/pytorch/pull/147066
Co-authored-by: Zhenbin Lin <lin-zhenbin@qq.com> | true |
2,987,429,099 | DISABLED test_pp_fsdp_dp_type_FSDP_MP_ScheduleClass3 (__main__.ComposabilityTest) | jithunnair-amd | closed | [
"module: rocm",
"triaged",
"skipped"
] | 2 | COLLABORATOR | Platforms: rocm
This test was disabled because it failed on the MI300 runners in https://github.com/pytorch/pytorch/pull/150667: https://github.com/pytorch/pytorch/actions/runs/14372628446/job/40320881178
cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,987,427,886 | DISABLED test_pp_fsdp_dp_type_FSDP_ScheduleClass3 (__main__.ComposabilityTest) | jithunnair-amd | closed | [
"module: rocm",
"triaged",
"skipped"
] | 2 | COLLABORATOR | Platforms: rocm
This test was disabled because it failed on the MI300 runners in https://github.com/pytorch/pytorch/pull/150667: https://github.com/pytorch/pytorch/actions/runs/14372628446/job/40320881178
cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,987,426,968 | DISABLED test_pp_fsdp_dp_type_FSDP_ScheduleClass2 (__main__.ComposabilityTest) | jithunnair-amd | closed | [
"module: rocm",
"triaged",
"skipped"
] | 2 | COLLABORATOR | Platforms: rocm
This test was disabled because it failed on the MI300 runners in https://github.com/pytorch/pytorch/pull/150667: https://github.com/pytorch/pytorch/actions/runs/14372628446/job/40320881178
cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,987,426,381 | DISABLED test_pp_fsdp_dp_type_FSDP_ScheduleClass1 (__main__.ComposabilityTest) | jithunnair-amd | closed | [
"module: rocm",
"triaged",
"skipped"
] | 2 | COLLABORATOR | Platforms: rocm
This test was disabled because it failed on the MI300 runners in https://github.com/pytorch/pytorch/pull/150667: https://github.com/pytorch/pytorch/actions/runs/14372628446/job/40320881178
cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,987,425,034 | DISABLED test_pp_fsdp_dp_type_FSDP_ScheduleClass0 (__main__.ComposabilityTest) | jithunnair-amd | closed | [
"module: rocm",
"triaged",
"skipped"
] | 2 | COLLABORATOR | Platforms: rocm
This test was disabled because it failed on the MI300 runners in https://github.com/pytorch/pytorch/pull/150667: https://github.com/pytorch/pytorch/actions/runs/14372628446/job/40320881178
cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,987,423,097 | DISABLED test_pp_fsdp_dp_type_FSDP_MP_ScheduleClass2 (__main__.ComposabilityTest) | jithunnair-amd | closed | [
"module: rocm",
"triaged",
"skipped"
] | 2 | COLLABORATOR | Platforms: rocm
This test was disabled because it failed on the MI300 runners in https://github.com/pytorch/pytorch/pull/150667: https://github.com/pytorch/pytorch/actions/runs/14372628446/job/40320881178
cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,987,421,767 | DISABLED test_pp_fsdp_dp_type_FSDP_MP_ScheduleClass1 (__main__.ComposabilityTest) | jithunnair-amd | closed | [
"module: rocm",
"triaged",
"skipped"
] | 2 | COLLABORATOR | Platforms: rocm
This test was disabled because it failed on the MI300 runners in https://github.com/pytorch/pytorch/pull/150667: https://github.com/pytorch/pytorch/actions/runs/14372628446/job/40320881178
cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,987,420,265 | DISABLED test_pp_fsdp_dp_type_FSDP_MP_ScheduleClass0 (__main__.ComposabilityTest) | jithunnair-amd | closed | [
"module: rocm",
"triaged",
"skipped"
] | 2 | COLLABORATOR | Platforms: rocm
This test was disabled because it failed on the MI300 runners in https://github.com/pytorch/pytorch/pull/150667: https://github.com/pytorch/pytorch/actions/runs/14372628446/job/40320881178
cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,987,418,562 | DISABLED test_pp_ddp_ScheduleClass2 (__main__.ComposabilityTest) | jithunnair-amd | closed | [
"module: rocm",
"triaged",
"skipped"
] | 2 | COLLABORATOR | Platforms: rocm
This test was disabled because it failed on the MI300 runners in https://github.com/pytorch/pytorch/pull/150667: https://github.com/pytorch/pytorch/actions/runs/14372628446/job/40320881178
cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,987,417,254 | DISABLED test_pp_ddp_ScheduleClass1 (__main__.ComposabilityTest) | jithunnair-amd | closed | [
"module: rocm",
"triaged",
"skipped"
] | 2 | COLLABORATOR | Platforms: rocm
This test was disabled because it failed on the MI300 runners in https://github.com/pytorch/pytorch/pull/150667: https://github.com/pytorch/pytorch/actions/runs/14372628446/job/40320881178
cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,987,402,407 | docs: allow empty targets tensor in ctc_loss | jPorterDosch | closed | [
"open source",
"Merged",
"topic: not user facing"
] | 16 | CONTRIBUTOR | docs: allow empty targets tensor in ctc_losswhen target_lengths are zero, as described in issue
Fixes #150995
| true |
2,987,396,667 | Rewrite autograd producer consumer stream sync logic | soulitzer | open | [
"oncall: distributed",
"ciflow/trunk",
"release notes: autograd"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151079
Also see previous work https://github.com/pytorch/pytorch/pull/142097
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k | true |
2,987,393,265 | DISABLED test_pp_ddp_ScheduleClass0 (__main__.ComposabilityTest) | jithunnair-amd | closed | [
"module: rocm",
"triaged",
"skipped"
] | 2 | COLLABORATOR | Platforms: rocm
This test was disabled because it failed on the MI300 runners in https://github.com/pytorch/pytorch/pull/150667: https://github.com/pytorch/pytorch/actions/runs/14372628446/job/40320881178
cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,987,391,585 | DISABLED test_allgather_stress_cuda (__main__.ProcessGroupGlooTest) | jithunnair-amd | open | [
"module: rocm",
"triaged",
"skipped"
] | 1 | COLLABORATOR | Platforms: rocm
This test was disabled because it failed on the MI300 runners in https://github.com/pytorch/pytorch/pull/150667: https://github.com/pytorch/pytorch/actions/runs/14372628446/job/40320881178
cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,987,380,934 | When can the 50 series be installed with a lower version of torch? Now the pytorch version is too high and many things can't run | jhluaa | closed | [] | 1 | NONE | ### 🚀 The feature, motivation and pitch
We don't just need torch 2.6, we need to be compatible with lower versions of torch under cuda 12.8
### Alternatives
_No response_
### Additional context
_No response_ | true |
2,987,303,477 | don't return logits for benchmark script | shunting314 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151075
PT2 benchmark scripts has a pattern like:
```
def forward_and_backward_pass(self, mod, inputs, collect_outputs=True):
cloned_inputs = clone_inputs(inputs)
self.optimizer_zero_grad(mod)
with se... | true |
2,987,290,381 | DISABLED test_remote_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_False_use_static_cuda_launcher_False (__main__.TestFxGraphCache) | pytorch-bot[bot] | open | [
"module: rocm",
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: inductor"
] | 1 | NONE | Platforms: rocm
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_remote_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_False_use_static_cuda_launcher_False&suite=TestFxGraphCache&limit=100) and the most recent trunk [workflow log... | true |
2,987,282,566 | [Not for land] save Q,K,V tensor at start of flash attention fwd and bwd | danielvegamyhre | closed | [] | 2 | CONTRIBUTOR | null | true |
2,987,240,689 | [dtensor] add op support for torch._grouped_mm | tianyu-l | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: distributed (dtensor)"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151072
This PR would make TP work with Grouped MM in MoE implementations like https://github.com/pytorch/torchtitan/pull/1084
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k | true |
2,987,240,637 | [dtensor] add op support for torch.cumsum | tianyu-l | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: distributed (dtensor)"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151072
* __->__ #151071
For `torch.cumsum`, any sharding placement shoud propogate through if the cumsum `dim` is not sharded; otherwise it needs to be replicated first.
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4... | true |
2,987,238,537 | [2/N] Use internal linkage in aten C++ files | cyyever | closed | [
"module: cpu",
"triaged",
"open source",
"oncall: mobile",
"Merged",
"ciflow/trunk",
"release notes: quantization",
"release notes: linalg_frontend",
"ciflow/periodic",
"ciflow/android"
] | 8 | COLLABORATOR | Turn functions and variables into static if they are not used outside the ten cpp files. In some cases, missing header inclusion is added. In other cases, unused functions are removed.
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 | true |
2,987,230,179 | [ONNX] Support float4 | justinchuby | open | [
"module: onnx",
"triaged",
"open source",
"ciflow/trunk",
"release notes: onnx",
"topic: new features"
] | 3 | COLLABORATOR | - Support exporting float4 models (note: currently we use IR version 10 universally in the exporter, which does not include float 4 support. Eventually when onnx runtime and the ecosystem moves to support the new IR version 11 we should bump our version to 11 in the exporter as well)
- The shape of the type is set acc... | true |
2,987,207,422 | [Profiler/Easy] Remove temp flag for on-demand Memory Snapshot | sraikund16 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: profiler",
"topic: not user facing"
] | 6 | CONTRIBUTOR | Summary: Now that we have profiler impl in we don't need the temporary flag. submodule update too.
Test Plan: CI
Reviewed By: sanrise
Differential Revision: D72672186
cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel | true |
2,987,178,908 | auto functionalize base_hop | ydwu4 | open | [
"release notes: fx",
"fx",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152984
* #152974
* __->__ #151067
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv @voznesenskym @penguinwu @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aak... | true |
2,987,173,494 | [ROCm] QR decomposition is much slower on MI300x than A100 | WenboGong | open | [
"module: performance",
"module: rocm",
"triaged"
] | 6 | NONE | ### 🐛 Describe the bug
Performing QR decomposition on large matrix with MI300x is significantly slower than that on A100. The average time consumption 5x more.
### Sample code
```python
import torch
import time
dim = 2048
n= 300
device = torch.device("cuda")
total_time = 0
for _ in range(n):
M= torch.randn(dim... | true |
2,987,154,203 | [export] Add draft-export to error msg | angelayi | closed | [
"Merged",
"ciflow/trunk",
"release notes: export"
] | 3 | CONTRIBUTOR | Given an exception in torch.export, I want to try/catch it to add the message "hey try out draft-export!". Currently I only add this message for errors that draft-export is known to fix, like DataDependentErrors, ConstraintViolationErrors, and no fake impl.
Originally the error message looks like:
```
File "/dat... | true |
2,987,134,257 | [ONNX] Use dlpack to transfer tensors when onnxruntime implements proper support | justinchuby | open | [
"module: onnx",
"triaged"
] | 0 | COLLABORATOR | Dependent on https://github.com/microsoft/onnxruntime/issues/24071 | true |
2,987,126,003 | [c10d][fr] Fix the false positive in the dtype check in fr analysis script | fduwjj | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151063
When checking dtype in fr analysis script, we should only check it when the input of output numbel is larger than zero. For the case when it is gather or scatter, the output/input size will be an empty list for non-src or n... | true |
2,987,122,695 | [dynamo] Remove `traceable_tensor_subclasses`-related code | StrongerXi | 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):
* __->__ #151062
* #151061
* #151060
Since #149792 deprecates `traceable_tensor_subclasses` and it's been
landed for over a week, we can safely remove all the old code that uses
`traceable_tensor_subclasses` (they were primarily for testing pu... | true |
2,987,122,333 | [dynamo] handle tensor subclass with non-classmethod `__torch_function__` | StrongerXi | closed | [
"Merged",
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151062
* __->__ #151061
* #151060
As title, this patch fixes bugs in
1. emulating `has_torch_function`
2. emulating calling `__torch_function__`
3. building a callable VT for non-classmethod `__torch_function__`
Fixes #120799, #150265, #15... | true |
2,987,122,285 | [dynamo] Properly handle `super().some_classmethod(...)` | StrongerXi | closed | [
"Merged",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151062
* #151061
* __->__ #151060
Previously we were passing in the instance as first argument to a
`super().some_classmethod(...)` call, but we should've passed in the
type object instead, per semantics of `@classmethod`.
cc @voznesenskym... | true |
2,987,089,602 | [AOTI] Add _weight_int4pack_mm to the C shim fallback list | desertfire | closed | [
"Merged",
"ciflow/trunk",
"topic: improvements",
"module: inductor",
"ciflow/inductor",
"release notes: inductor",
"ciflow/rocm"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151059
Summary: As title
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,987,082,413 | Remove C10_DEPRECATED | desertfire | open | [
"oncall: distributed",
"fb-exported",
"module: amp (automated mixed precision)",
"release notes: sparse"
] | 2 | CONTRIBUTOR | Summary:
Revive https://github.com/pytorch/pytorch/pull/138406. In additional to the original code, fixed internal test failures and completely removed c10/util/Deprecated.h.
Summary from the original PR,
```
Looking in the code I see
// NB: __cplusplus doesn't work for MSVC, so for now MSVC always uses
// the "__de... | true |
2,987,072,894 | Cache the value of torch_key in subproc | oulgen | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 21 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151057
No need to recalculate torch_key in subprocs, lets pass it from main process.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kade... | true |
2,987,034,868 | [dynamo, nested graph breaks] pack resume function stack + locals into a list | williamwen42 | open | [
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 2 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151056
* #144516
We need to be able to pass frame stack+locals in lists to hand off to nested functions in the future, so we implement this part first.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @... | true |
2,986,987,213 | segfault when TORCH_LOGS=invalid_arg | BoyuanFeng | closed | [
"high priority",
"module: logging",
"triaged",
"oncall: pt2"
] | 2 | CONTRIBUTOR | `TORCH_LOGS=aot_g python3 reshape.py >torch_logs_error 2>&1` gives seg fault.
Error message: [P1782497957](https://www.internalfb.com/phabricator/paste/view/P1782497957)
cc @ezyang @gchanan @zou3519 @kadeng @msaroufim @chauhang @penguinwu | true |
2,986,964,382 | DISABLED test_parity__foreach_acos_fastpath_outplace_cuda_bfloat16 (__main__.TestForeachCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: mta"
] | 5 | NONE | Platforms: linux
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_parity__foreach_acos_fastpath_outplace_cuda_bfloat16&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/40345930112).
O... | true |
2,986,935,937 | Don't log benchmarking event to Scuba | jamesjwu | 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):
* __->__ #151053
These two events are really common, and also make up a huge portion of logs (~70%) we get internally in PT2 Compile Events. I don't think it's actually that useful to aggregate them, so instead of logging them to PT2 Compil... | true |
2,986,903,340 | [c10d][libuv] Add back correct EOF case check | fduwjj | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151052
We removed the wrong EOF case in https://github.com/pytorch/pytorch/pull/150987, and we added the correct one back in this PR. Since https://github.com/pytorch/pytorch/pull/150987 is a fix, so we merge that PR first and use t... | true |
2,986,900,664 | [export] Make draft-export predispatch=True by default | angelayi | closed | [
"Merged",
"release notes: export"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151050
* #151065
* __->__ #151051
| true |
2,986,900,559 | [export] Add DRAFT_EXPORT envvar | angelayi | closed | [
"release notes: export"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151050
* #151065
* #151051
| true |
2,986,891,275 | [ROCm] Support torch compile graph mode for custom ops - trigged by vLLM V1 and aiter | hongxiayang | closed | [
"module: rocm",
"triaged",
"enhancement"
] | 1 | COLLABORATOR | ### 🚀 The feature, motivation and pitch
To make vLLM to be able to work with aiter in V1 + graph mode.
Example:
rocm/vllm-dev:llama4-20250409
model
```
export LLAMA_DIR=/data/Llama-4-Scout-17B-16E
export VLLM_ROCM_USE_AITER=1
export VLLM_ROCM_USE_AITER_RMSNORM=0
export VLLM_ROCM_USE_AITER_MOE=1
VLLM_USE_V1=1 VLLM_... | true |
2,986,878,692 | [c10d][fr] Add logging of nccl_version into fr and its dump | fduwjj | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 14 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151048
Users also want to see the nccl version in the FR dump so let's add it to FR. We only add it per rank per PG nccl comm, so this is really add a couple bytes to FR memory.
cc @H-Huang @awgu @wanchaol @fegin @wz337 @wconst... | true |
2,986,818,068 | [DRAFT] INitial version of sticky export | tugsbayasgalan | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: export"
] | 15 | CONTRIBUTOR | Summary: This is to make torchnative demos and benchmarking real models more simple by not requiring ppl to find example inputs first.
Test Plan: CI
Differential Revision: D72815584
| true |
2,986,816,689 | remove MTIA from the check of duplicate flow events | fenypatel99 | closed | [
"fb-exported",
"ciflow/trunk",
"topic: not user facing"
] | 6 | MEMBER | Summary: For MTIA, there can be more than one event with same correlation id. Need to omit this check
Test Plan: CIs
Differential Revision: D72815463
| true |
2,986,811,207 | c10d/Store: add clone feature (#150966) (#150966) | d4l3k | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 5 | MEMBER | Summary:
This adds a new `clone()` method to Store which will return a new Store instance that can be used from a different thread.
This is intended to better support multiple threads with stores such as when ProcessGroupNCCL needs a store to do error propagation.
Related issue: https://github.com/pytorch/pytorch/iss... | true |
2,986,802,859 | [dynamo] unimplemented -> unimplemented_v2 in variables/builder.py | williamwen42 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"keep-going",
"module: compile ux"
] | 3 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151044
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,986,799,967 | FlexAttention: create_block_mask is considerably slow when using flattened 1D sequences (document masking / jagged tensors) | mikkelfo | open | [
"triaged",
"oncall: pt2",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention"
] | 8 | NONE | ### 🐛 Describe the bug
FlexAttention seems considerably slow for 1D-vector approaches where the batch_size and seq_len is collapsed. This scenario is pretty common for any document_masking or jagged tensors setups and is even part of the [FlexAttention blog examples](https://pytorch.org/blog/flexattention/#document-m... | true |
2,986,798,922 | [MPS] Fix `determine_backend_memory_format` logic | malfet | closed | [
"module: cpu",
"Merged",
"topic: bug fixes",
"release notes: mps",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150824
* __->__ #151042
If input is channels last than MPS will return a channels last output
This fixed `GPUTests.test_convolution_4_mps` from test_torchinductor.py
That previous failed with
```
AssertionError: expected size 3=... | true |
2,986,792,127 | Log information about suppressed data dependent errors | laithsakka | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: fx",
"topic: not user facing",
"fx",
"ciflow/inductor",
"ci-no-td"
] | 9 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151041
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,986,786,052 | [ROCm] Improve behavior of get_torch_rocm_version helper function on non-ROCm systems. | naromero77amd | closed | [
"module: rocm",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/rocm"
] | 3 | COLLABORATOR | Fixes #150041
Return a zero tuple when ROCm is _not_ supported, similar to what is done for the CUDA version of this function.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang | true |
2,986,730,656 | torch.gather for float64 is still slow on AMD after float32 has been fixed | hfhongzy | closed | [
"module: performance",
"module: rocm",
"triaged"
] | 3 | NONE | ## 🐛 Describe the bug
`torch.scatter_add` for `float64` can be very slow running on AMD GPU compared with NVIDIA GPU.
According to the profiling results, the problem comes from the kernel `_scatter_gather_elementwise_kernel`. A previous issue [torch.gather can be slow on AMD with duplicated index](https://github.com... | true |
2,986,726,033 | Do not log exception when recording is disabled or already recording | laithsakka | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: fx",
"topic: not user facing",
"fx",
"ciflow/inductor",
"ci-no-td"
] | 15 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151038
I am not sure why do we log all exceptions here and re-raise them , but at least when recording is disabled this should be
transparent. namely logging dde could be spamming.
before:
<img width="995" alt="Screenshot 202... | true |
2,986,722,074 | [Inductor UT] Generalize device-bias code in `test_flex_attention.py` | anmyachev | closed | [
"triaged",
"open source",
"topic: not user facing",
"module: inductor"
] | 4 | COLLABORATOR | cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov
Part of https://github.com/pytorch/pytorch/pull/143553
@etaf @davidberard98 @hoshibara @guangyey could you take a look? | true |
2,986,663,219 | Remove conda usage in windows binary builds | atalman | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/binaries_wheel"
] | 4 | CONTRIBUTOR | This is related to : https://github.com/pytorch/pytorch/issues/146048
Removing conda from windows binary builds. At this point we are only removing conda and replacing it with python builds. Not rewriting all batch files as python or bash.
Additionally cleanup unused files:
```
.ci/pytorch/windows/internal/stati... | true |
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