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 |
|---|---|---|---|---|---|---|---|---|
2,938,657,074 | stride asserts should name the operator involved | zou3519 | open | [
"high priority",
"triaged",
"module: custom-operators",
"oncall: pt2",
"module: inductor",
"module: pt2-dispatcher"
] | 3 | CONTRIBUTOR | ```
File "/packages/aps.ads.icvr/icvr_launcher#link-tree/torch/_inductor/output_code.py", line 460, in __call__
return self.current_callable(inputs)
File "/packages/aps.ads.icvr/icvr_launcher#link-tree/torch/_inductor/utils.py", line 2348, in run
return model(new_inputs)
File "/tmp/torchinductor_nobody/27/... | true |
2,938,627,366 | Do not depend on numpy during the import | pytorchbot | closed | [
"open source"
] | 1 | COLLABORATOR | But a good followup would be to use torch primitives instead of numpy here
Fixes https://github.com/pytorch/pytorch/issues/149681
Test plan: Monkey-patch 2.7.0-rc and run `python -c "import torch;print(torch.compile(lambda x:x.sin() + x.cos())(torch.rand(32)))"`
cc @zou3519 @Chillee @samdow @kshitij12345 | true |
2,938,545,347 | [MPS] Replace indexed with strided flavor | malfet | closed | [
"Merged",
"release notes: mps",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149730
Which renders non-contiguous operations much faster for larger tensors, for example `fmax` of 1000x1000 strides tensors takes 270ms with new algorithm and 430ms with an old one, that needed additional tensor of 3e6 elements... | true |
2,938,545,174 | [MPS][BE] Get rid of `supports_dense` flag | malfet | closed | [
"Merged",
"topic: not user facing",
"release notes: mps",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149730
* __->__ #149729
* #149728
* #149727
As now all binary ops supports dense | true |
2,938,467,955 | [MPS][BE] Migrate complex_mul to tensor iterator | malfet | closed | [
"Merged",
"topic: not user facing",
"release notes: mps",
"ciflow/mps"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149730
* #149729
* __->__ #149728
* #149727
| true |
2,938,467,787 | [MPS][BE] Migrate `torch.complex` to binary_functor | malfet | closed | [
"Merged",
"topic: not user facing",
"release notes: mps",
"ciflow/mps"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149730
* #149729
* #149728
* __->__ #149727
As it's very similar in nature to `torch.polar`
Though rename kernel from `complex_kernel` to `make_complex` | true |
2,938,441,521 | SDPA gives different outputs compared to manual attention with `dropout>0.0` | abdulfatir | closed | [
"triaged",
"module: random",
"module: sdpa"
] | 3 | NONE | ### 🐛 Describe the bug
SDPA gives different outputs compared to manual attention when the `EFFICIENT_ATTENTION` backend is used and dropout is non-zero. Is this expected? Is the efficient kernel using a different RNG?
Here's an MWE:
```py
from torch.nn.functional import scaled_dot_product_attention
from torch.nn.at... | true |
2,938,232,466 | `torch.compile` does not work when `set_priority` is specified in `sdpa_kernel` | abdulfatir | closed | [
"oncall: pt2",
"module: sdpa"
] | 2 | NONE | ### 🐛 Describe the bug
Model compilation does not work when the `set_priority` kwarg is provied to the `sdpa_kernel` context manager. See example below.
```py
import torch
from torch.nn.attention import SDPBackend, sdpa_kernel
from torch.nn.functional import scaled_dot_product_attention
class Model(torch.nn.Module... | true |
2,938,194,525 | `torch.linalg.ldl_factor_ex`, `torch.linalg.ldl_factor`, and `torch.linalg.lstsq` Raise INTERNAL ASSERT FAILED | vwrewsge | open | [
"module: error checking",
"triaged",
"module: linear algebra"
] | 0 | NONE | ### 🐛 Describe the bug
# Bug 1
Code:
```
import torch
from torch.linalg import ldl_factor_ex
A = torch.eye(3, 3)
A[-1, -1] = 0
ldl_factor_ex(A, hermitian=True, check_errors=True)
```
Output:
```
RuntimeError: false INTERNAL ASSERT FAILED at "/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp":1630, please repor... | true |
2,938,186,846 | [ONNX][verification] `find_mismatch` Raises `INTERNAL ASSERT FAILED` | vwrewsge | closed | [
"module: onnx",
"triaged"
] | 1 | NONE | ### 🐛 Describe the bug
Code
```
import torch
import torch.onnx
import torch.jit
from torch import nn, Tensor
import io
from torch.onnx.verification import find_mismatch
class Model(nn.Module):
def __init__(self):
super().__init__()
self.module = nn.Linear(8, 4)
self.module2 = nn.Linear(4... | true |
2,938,113,438 | `capturable` should express consistent with the message on `torch.optim.RMSprop()` and `torch.optim.AdamW()` | ILCSFNO | closed | [
"module: docs",
"module: optimizer",
"triaged"
] | 0 | CONTRIBUTOR | ### 📚 The doc issue
The docs of [torch.optim.RMSprop()](https://pytorch.org/docs/stable/generated/torch.optim.RMSprop.html#torch.optim.RMSprop) and [torch.optim.AdamW()](https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW) show their shared description as below:
https://github.com/pyto... | true |
2,938,016,411 | Let pointwise sharding take arg with largest number of dims in case of ties | fmassa | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 6 | MEMBER | Before, we would take the first argument with the largest number of shards, regardless if it had fewer dims than another arg with the same number of shards but more dimensions. This would lead to potentially fewer sharding options
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,937,934,168 | Implement `permute` for masked tensor | JackCaster | open | [
"triaged",
"module: nestedtensor"
] | 1 | NONE | ### 🚀 The feature, motivation and pitch
I have a RNN-like module, which reads the input step by step in for-loop. The batch contains sequences of different length, which are therefore padded. I would like to ignore the padding throughout the various layers in the network. I thought I could use masked tensors! But, th... | true |
2,937,827,269 | Utility function to get the best available device | Halyjo | closed | [
"triaged",
"module: accelerator"
] | 3 | NONE | ### 🚀 The feature, motivation and pitch
# Utility function to get best available device
A piece of code I need in all my projects is a function that simply checks which devices are available and selects the best available option. Could this be a pytorch utility function?
**The function I usually use for this:**
```... | true |
2,937,748,788 | Constraints for distributions with mixed support | sethaxen | open | [
"module: distributions",
"triaged"
] | 0 | NONE | ### 🚀 The feature, motivation and pitch
I'd like to implement a joint distribution of both discrete and continuous parameters and would like to be able to define a constraint that indicates that the support is mixed and which parameters are continuous. A potential use-case is representing an approximate posterior dis... | true |
2,937,676,192 | [export] Save unflattened gm | angelayi | closed | [
"fb-exported",
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: export",
"ci-no-td"
] | 14 | CONTRIBUTOR | Test Plan: CI
Differential Revision: D71082652
| true |
2,937,661,329 | [Inductor] Cache CUDA compilation errors | kadeng | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 15 | CONTRIBUTOR | Summary: Add support for caching of CUDA (nvcc) compilation errors to codecache.py
Test Plan: CI ( for example Cutlass backend unit tests )
Reviewed By: ColinPeppler
Differential Revision: D71562040
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayi... | true |
2,937,613,142 | [LNL][Windows][Inductor] Application error: The memory could not be read. | libohao1201 | closed | [] | 0 | NONE | ### 🐛 Describe the bug
When running E2E inductor on LNL, the following error appears randomly:
### Versions
- stock pytorch :
- pip install torch --index-url https://download.pytorch.org/whl/test/xpu
- git clone https... | true |
2,937,606,489 | [XPU] Update triton commit to fix to fix level_zero not found by env var LEVEL_ZERO_V1_SDK_PATH. | pytorchbot | closed | [
"open source",
"topic: not user facing",
"ciflow/inductor",
"ciflow/xpu"
] | 4 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149511
| true |
2,937,577,909 | [Windows][Inductor] Invalid include path for cl.exe. | etaf | closed | [
"module: windows",
"triaged"
] | 1 | COLLABORATOR | ### 🐛 Describe the bug
When running
`python benchmarks/dynamo/huggingface.py --accuracy --amp --amp-dtype bfloat16 -dxpu -n1 --inference --backend inductor --only XLNetLMHeadModel`
I met the following error:
```
torch._inductor.exc.InductorError: CppCompileError: C++ compile error
Command:
cl /I C:/Program Files/W... | true |
2,937,538,099 | [dynamo] Ensure placeholder name is not an intermediate node name | anijain2305 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"keep-going"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149758
* __->__ #149712
Fixes https://fb.workplace.com/groups/1075192433118967/permalink/1615671879071017/
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chen... | true |
2,937,521,060 | [Dynamo] Clean up old torch function flag | mlazos | closed | [
"Merged",
"ciflow/trunk",
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo"
] | 16 | CONTRIBUTOR | This is tracked via `SymbolicTorchFunctionState` now.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,937,479,334 | pretty print graph signature | avikchaudhuri | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: export"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149710
Fixes #141243
Differential Revision: [D71604218](https://our.internmc.facebook.com/intern/diff/D71604218/) | true |
2,937,461,518 | [ca] API comments and support dynamic shapes via configs | xmfan | closed | [
"Merged",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"module: compiled autograd"
] | 2 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149784
* #149773
* #149651
* __->__ #149709
* #149647
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,937,418,484 | [Quant][PT2E] add a lowering pass for x86 backend | Xia-Weiwen | closed | [
"open source",
"Merged",
"ciflow/trunk",
"release notes: quantization",
"intel"
] | 5 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149708
**Summary**
This PR adds a lowering pass for x86 backend
- Patterns of `dequantize -> conv/linear (-> quantize)` are fused to corresponding quantized onednn ops.
- Weights are prepacked ahead of time.
- Post ops of conv/l... | true |
2,937,359,439 | [aot] mark dynamic activations as maybe dynamic | xmfan | closed | [
"Merged",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"module: pt2-dispatcher"
] | 7 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152633
* #152119
* #151962
* #151731
* #151860
* __->__ #149707
Today, we mark graph outputs as maybe dynamic, this lets a compilation to communicate to future compilations whether certain graph inputs are dynamic. Similarly, we can do thi... | true |
2,937,359,354 | [ca] torch.compile API comments and support older dynamic shapes API used in benchmarks | xmfan | open | [
"module: dynamo",
"ciflow/inductor",
"module: compiled autograd"
] | 2 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* (to be filled)
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,937,337,272 | [MPS] Add support for scaled_modified_bessel_k0 for eager. | dcci | closed | [
"Merged",
"release notes: mps",
"ciflow/mps"
] | 4 | MEMBER | null | true |
2,937,317,870 | [MPS] Add inline to function definition. | dcci | closed | [
"Merged",
"topic: not user facing",
"module: mps",
"ciflow/mps"
] | 4 | MEMBER | cc @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen | true |
2,937,315,874 | [XPU][Inductor] Failed to run max-autotune in subprocess. | etaf | open | [
"triaged",
"module: xpu"
] | 0 | COLLABORATOR | ### 🐛 Describe the bug
Currently the max-autotune in subprocess on XPU get `RuntimeError: _share_fd_: only available on CPU`
```
python test/inductor/test_max_autotune.py TestMaxAutotune.test_benchmark_choice_in_subproc
ERROR: test_benchmark_choice_in_subproc (__main__.TestMaxAutotune)
-------------------------------... | true |
2,937,271,001 | [PT2] Port use_triton_lce to PT2 pre_grad passes | huxintong | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Summary:
`use_triton_lce_replace_simple_LCE` and `use_triton_lce_replace_normal_LCE`
code is mostly the same, some minor changes to support aten IR
Test Plan:
```
scripts/aetk/aetk -L
%run ~/fbsource/fbcode/caffe2/test/inductor/fb/test_customized_triton_kernel_passes.py
```
will verify the qps after everything done ... | true |
2,937,249,070 | [TP] add support for fused QKV Sharding | wanchaol | open | [
"oncall: distributed",
"triaged",
"open source",
"ciflow/inductor",
"release notes: distributed (dtensor)"
] | 7 | COLLABORATOR | This PR adds fused QKV sharding in the TP layer. There should be no "strided" sharding involved as fused QKV linear layer is more about combining three layers into one.
See design and discussions: https://github.com/pytorch/pytorch/issues/140069#issuecomment-2683153303
resolves https://github.com/pytorch/pytorch/... | true |
2,937,241,411 | Improve subproc autotuning implementation | masnesral | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 15 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149890
* __->__ #149700
Summary: The primary change is to update the autotune-in-a-subproc implementation to avoid using multiprocessing spawn. Spawn (re)executes the toplevel script in the subproc, which can be problematic. The approach h... | true |
2,937,204,247 | Supporting non-tensor-data write_size in planner write items. | pradeepfn | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: distributed (checkpoint)",
"oncall: distributed checkpointing"
] | 5 | CONTRIBUTOR | Summary:
1\ The current write item structure does not contain the amount of data that needs to be written.
2\ the planner.item already has a size primitive 'tensor_storage_size'. https://fburl.com/code/7a0gsmw7 But only for tensors.
3\ Right now, the only way the writer layer get hold of this property (fro non tensor d... | true |
2,937,195,041 | Fix subclass access custom op bug | tugsbayasgalan | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: export"
] | 5 | CONTRIBUTOR | Summary: When we call torch.inference_mode, we seem to skip Autograd key causing the custom op export uses to be not decomposed properly before subclass dispatching starts. We fix this by force desugaring this op at Python key
Test Plan: test
Differential Revision: D71599541
| true |
2,937,155,258 | Inductor logging + analysis of torch.profile | exclamaforte | open | [
"topic: improvements",
"module: inductor",
"ciflow/inductor",
"release notes: inductor",
"suppress-bc-linter"
] | 6 | CONTRIBUTOR | Prereqs:
- https://github.com/pytorch/pytorch/pull/152708
Features:
1. Adds inductor's estimate of flops and bandwidth to the json trace events that perfetto uses.
1. Only use the tflops estimation from triton if we don't have the info from the datasheet because Triton's estimates are inaccurate. I have a backlo... | true |
2,937,133,660 | Update torch-xpu-ops commit pin | xytintel | closed | [
"open source",
"topic: not user facing",
"keep-going"
] | 2 | CONTRIBUTOR | Update the torch-xpu-ops commit to [b18528c455d0297b89b255e93b86ff668069459f](https://github.com/intel/torch-xpu-ops/commit/b18528c455d0297b89b255e93b86ff668069459f), include
- Bugfix of performance issue relating to GRF configuration.
| true |
2,937,106,710 | Add "xpu" to __all__ for torch/version.py | FFFrog | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"release notes: xpu"
] | 9 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149695
As the title stated. | true |
2,937,082,208 | torch.det results in nans for torch.func.hessian | alecjacobson | open | [
"module: numerical-stability",
"triaged",
"module: functorch"
] | 0 | NONE | ### 🐛 Describe the bug
This minified example shows `torch.func.hessian` acting up when the function in question involves `torch.det`.
```python
import torch
x = torch.tensor([[1,0],[1,1]],dtype=torch.float64,requires_grad=True)
def phi(x):
A = torch.tensor([[1,0],[-1,1]],dtype=torch.float64)
J = x @ A
... | true |
2,937,080,358 | Fix broken LazyLinear init | vmoens | closed | [
"module: nn",
"Merged",
"ciflow/trunk",
"release notes: nn"
] | 7 | CONTRIBUTOR | Fixes #149691
I beleive it does not impact negatively the fix in https://github.com/pytorch/pytorch/pull/147599 as the tests stilll pass but @FFFrog should confirm.
cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki | true |
2,937,073,364 | Improve error message for `torch.fft.ihfft2` when input's dtype is complex | shink | open | [
"triaged",
"open source",
"topic: not user facing"
] | 5 | CONTRIBUTOR | Fixes #149625
For the case mentioned in the issue, will get:
```
RuntimeError: Only supports floating-point dtypes, but found: ComplexDouble
```
| true |
2,937,072,396 | LazyLinear broken by new init logic | vmoens | closed | [
"high priority",
"module: nn",
"triaged",
"module: regression",
"module: lazy"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
The [following PR](https://github.com/pytorch/pytorch/pull/147599) broke the init of lazy linear:
```
python -c """
from torch import nn
import torch
l = nn.LazyLinear(4)
print(l(torch.randn(3)))
print(l(torch.randn(3)))
"""
```
prints
```
tensor([0., 0., 0., 0.], grad_fn=<ViewBackward0>)
```
... | true |
2,937,022,046 | flip test_cache | laithsakka | closed | [
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149690
* #149267
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,936,952,481 | [Dynamo] Cleanup state management for ctx managers | mlazos | closed | [
"Merged",
"ciflow/trunk",
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo"
] | 3 | CONTRIBUTOR | Removes state indirection for ctx managers. This isn't needed anymore since VTs are mutable.
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149689
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ch... | true |
2,936,947,478 | [hop] support base_hop._gen_schema | ydwu4 | closed | [
"oncall: jit",
"Merged",
"ciflow/trunk",
"release notes: jit",
"module: dynamo",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150369
* __->__ #149688
This PR creates two utils for generating a schema for hops from example inputs and use base hop as an exmaple.
1. HopArgumentInfoGen creates an argument or an output schema with mutation information.
2. CFuncitonS... | true |
2,936,936,894 | [MPS] Add support for `modified_bessel_k1` to eager and inductor. | dcci | closed | [
"Merged",
"release notes: mps",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 4 | MEMBER | cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,936,928,150 | [Dynamo] Remove partial graph printing on data-dependent graph breaks | mlazos | closed | [
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo"
] | 2 | CONTRIBUTOR | Checking with Bob offline, but this can be achieved on a conditional basis with `TORCH_LOGS="graph_code"`
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149686
* #149685
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @we... | true |
2,936,928,096 | [Hierarchical Compilation] Handle origin nodes without children | mlazos | closed | [
"Merged",
"ciflow/trunk",
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo"
] | 3 | CONTRIBUTOR | Bug discovered running Hierarchical Compilation on HF.
I don't have a smaller repro for this unfortunately.
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149686
* __->__ #149685
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzhe... | true |
2,936,907,478 | Add elu as core ATen | swolchok | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149684
Differential Revision: [D71590420](https://our.internmc.facebook.com/intern/diff/D71590420/) | true |
2,936,883,372 | Do not depend on numpy during the import | malfet | closed | [
"Merged",
"ciflow/trunk",
"topic: bug fixes",
"module: functorch",
"release notes: torch.func"
] | 5 | CONTRIBUTOR | But a good followup would be to use torch primitives instead of numpy here
Fixes https://github.com/pytorch/pytorch/issues/149681
Test plan: Monkey-patch 2.7.0-rc and run `python -c "import torch;print(torch.compile(lambda x:x.sin() + x.cos())(torch.rand(32)))"`
cc @zou3519 @Chillee @samdow @kshitij12345 | true |
2,936,858,272 | [BE] Introduce `lapack_work_to_int` function | malfet | closed | [
"Merged",
"ciflow/trunk",
"release notes: linalg_frontend",
"topic: bug fixes"
] | 3 | CONTRIBUTOR | That could be used to safely cast floating values to int by adding an ULP, which is a followup after https://github.com/pytorch/pytorch/pull/146456
Fixes https://github.com/pytorch/pytorch/issues/149591
(Not adding unittest as it's just going to be too slow)
Test plan:
```
% python3 -c "import torch; torch.pin... | true |
2,936,832,793 | Torch.compile is failing if numpy is not installed | atalman | open | [
"high priority",
"module: binaries",
"triaged",
"oncall: pt2"
] | 11 | CONTRIBUTOR | ### 🐛 Describe the bug
Install torch:
``pip3 install torch==2.7.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu126``
Run smoke test on torch 2.7 rc1:
https://github.com/pytorch/pytorch/blob/main/.ci/pytorch/smoke_test/smoke_test.py
Output
```
python3 smoke_test.py --package torchonly
/... | true |
2,936,830,681 | [MPS] nanmedian with dims | Isalia20 | closed | [
"open source",
"Merged",
"topic: improvements",
"module: mps",
"release notes: mps"
] | 4 | COLLABORATOR | Third most voted op from #77764
Tests were deleted because they are covered by the regular test_output_match tests so those were redundant and were added in the last PR before the nanmedian dim version would be implemented
cc @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen | true |
2,936,829,272 | cd: There's no way to test changes to container images for binary builds | seemethere | closed | [
"oncall: releng",
"triaged"
] | 2 | MEMBER | Was doing a bit of exploration in https://github.com/pytorch/pytorch/pull/149675 when I realized that we actually hardcode all of our binary builds to run against the `main` tag. (see [logs](https://github.com/pytorch/pytorch/actions/runs/13980596619/job/39144800674?pr=149675#step:13:86))
https://github.com/pytorch/py... | true |
2,936,824,365 | [ONNX] Set is_in_onnx_export for dynamo=True | titaiwangms | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: onnx",
"topic: new features",
"suppress-bc-linter"
] | 4 | COLLABORATOR | Fixes #149141
| true |
2,936,801,101 | [ROCm][TunableOp] Fix offline tuning for ScaledGEMM. | naromero77amd | closed | [
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"release notes: linalg_frontend",
"topic: not user facing"
] | 4 | COLLABORATOR | The main purpose of this PR is to fix offline tuning for ScaledGEMM. The previous UT passed because it was not strict enough. Additionally:
- All the offline tuning tests now do a comparison with the online results to ensure that ParamSignature match.
- We raise an error if submatrices are encountered as this is only... | true |
2,936,763,792 | [dynamo] keep chained exceptions in user-facing tracebacks | williamwen42 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"module: compile ux"
] | 6 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149676
This preserves graph breaks in the case that one graph break directly causes another, e.g. graph breaks in generic context managers.
```python
import torch
class CtxMgr:
def __enter__(self):
return se... | true |
2,936,761,180 | DO NOT MERGE: Testing sequential builds for cuda + cpu | seemethere | open | [
"topic: not user facing"
] | 1 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149675
* #143672
* #148419
Signed-off-by: Eli Uriegas <eliuriegas@meta.com> | true |
2,936,753,184 | [DTensor] Document uneven sharding semantics | wconstab | closed | [
"oncall: distributed",
"module: dtensor",
"release notes: distributed (dtensor)"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149764
* __->__ #149674
Defines and documents behaviors that are implicit in DTensor design
today.
Partially addresses #143372
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @d4l3k @c-p-i-o @tianyu-l @XilunWu | true |
2,936,718,583 | Extract reusable portions of elu_kernel into header | swolchok | closed | [
"module: cpu",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 11 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149673
Similar to #140425, we are making the implementation usable via header-only code sharing.
Review note: #62546 by @yanbing-j removed expm1 usage from this path. I don't know why and expm1 should be more efficient, so I've put ... | true |
2,936,718,337 | Add release branch push triggers to inductor-rocm-mi300.yml | jithunnair-amd | closed | [
"module: rocm",
"open source",
"Merged",
"topic: not user facing",
"ciflow/rocm"
] | 5 | COLLABORATOR | In similar vein as https://github.com/pytorch/pytorch/pull/149517
When we added the rocm-mi300.yml earlier this year, we had lower capacity and we were just pipecleaning the workflow, so we set the trigger to only respond to pushes to main branch. But now we have more stability as well as capacity, and we would real... | true |
2,936,716,047 | DISABLED test_ddp_graphs (__main__.StructuredTraceTest) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped"
] | 3 | NONE | Platforms: linux, rocm, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_ddp_graphs&suite=StructuredTraceTest&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/39135025462).
Over the past 3 hours, ... | true |
2,936,716,046 | DISABLED test_lazy_module_speculation_log_divergence (__main__.NNModuleTests) | pytorch-bot[bot] | closed | [
"module: nn",
"triaged",
"module: flaky-tests",
"skipped"
] | 2 | NONE | Platforms: linux, rocm
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_lazy_module_speculation_log_divergence&suite=NNModuleTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/39123845623).
Over the pa... | true |
2,936,675,485 | [easy] Do not logspam if static cuda launcher is disabled | jamesjwu | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149629
* #149657
* #149442
* #149054
* __->__ #149669
No need to log.info every time someone runs with StaticCudaLauncher disabled.
Test plan: Run any benchmark and see that we don't spam the bypass message in logs.
cc @voznes... | true |
2,936,642,158 | [ONNX] Improve docstring of onnx symbolic ops | justinchuby | closed | [
"module: onnx",
"open source",
"Merged",
"ciflow/trunk",
"release notes: onnx",
"topic: docs"
] | 6 | COLLABORATOR | Better examples | true |
2,936,637,083 | [invoke_subgraph][fake tensor cache] Add a finalizer for id hashed objects | anijain2305 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148953
* #150036
* __->__ #149667
* #149087
| true |
2,936,628,754 | Remove effect token unbacked bindings when removing with_effect nodes | yushangdi | closed | [
"oncall: jit",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: export"
] | 6 | CONTRIBUTOR | Summary:
**Export**
Fix `node.meta["unbacked_bindings"]`when removing `with_effect` wrapper in `ep.module()` call.
Test Plan:
```
buck run //caffe2/test:test_export -- -r test_custom_obj_unbacked_symint
```
Differential Revision: D71567148
cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel | true |
2,936,627,016 | Use source hashing to generate consistent symbolic ids | bobrenjc93 | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: fx",
"fx",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"keep-going",
"ciflow/slow",
"ci-no-td"
] | 34 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149665
This PR was inspired by internal models that were cache missing due to PGO. At a high level the problem looks as follows
Run 1, Invocation 1: We do static compile, save some example values in PGO/automatic dynamic
Run 1... | true |
2,936,623,406 | dynamo_compile: Log all compilation time under all_compilation_types | c00w | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 9 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149664
This counter is designed to include all compilation pytorch does (triton +
dynamo_compile). However this wasn't including all of dynamo compilation, since
it was put in at the fx_codegen_and_compile spot.
cc @voznesenskym @pe... | true |
2,936,615,810 | [export] Add min & max as attribute hints to Dim | ColinPeppler | closed | [
"fb-exported",
"release notes: export"
] | 2 | CONTRIBUTOR | Summary:
I see this pyre error.
```
Undefined attribute [16]: `torch.export.dynamic_shapes.Dim` has no attribute `max`.
```
Differential Revision: D71575304
| true |
2,936,582,842 | [ONNX] Use onnx Attention operator for scaled_dot_product_attention | csoiram | open | [
"module: onnx",
"triaged"
] | 1 | NONE | ### 🚀 The feature, motivation and pitch
ONNX have introduced MHA operator in opset 23 (https://onnx.ai/onnx/operators/onnx__Attention.html#l-onnx-op-attention-23). This could be used when exporting scaled_dot_product_attention to ONNX format. Currently the scaled_dot_product_attention gets broken down to constituent ... | true |
2,936,577,880 | preserve custom meta in placeholders | avikchaudhuri | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: export"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149661
Fixes #147338
Differential Revision: [D71573533](https://our.internmc.facebook.com/intern/diff/D71573533/) | true |
2,936,568,118 | cd: Migrate binary builds off of Jinja | seemethere | open | [
"oncall: releng",
"triaged",
"better-engineering"
] | 0 | MEMBER | The binary builds that we currently have are still on Jinja and they don't actually need to be.
This is the following list of things that need to be done:
- [ ] Nightly branch Linux
- [ ] Main branch Linux
- [ ] Nightly branch Windows
- [ ] Main branch Windows
- [ ] Nightly branch Windows arm64
- [ ] Nightly branch m... | true |
2,936,563,589 | Fix broken dynamo_timed test due to python_version field | masnesral | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149659
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,936,552,515 | unexpected kwarg 'no_python_abi_suffix' when installing stable version of pytorch with sam2 | rbavery | open | [
"high priority",
"module: build",
"module: cpp-extensions",
"triaged",
"has workaround"
] | 7 | NONE | ### 🐛 Describe the bug
Installing sam2, which requires a build of a cpp extension and pytorch 2.6 used to work. today this now doesn't but nothing has changed about my environment
```
> [wherobots-inference-gpu 13/14] RUN uv pip install -e dockers/gpu/. --extra-index-url https://download.pytorch.org/whl/cu124 --ind... | true |
2,936,497,496 | [StaticCudaLauncher] Support sharedMemBytes > 48KB | jamesjwu | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150108
* #150107
* #149054
* __->__ #149657
Triton does some special handling when requesting more than 48 KB of shared memory: specifically it queries the device for maximum device memory, then sets the maximum amount of dynamic memory to... | true |
2,936,491,507 | [WIP] avoid speicializing sym_max and sym_min | laithsakka | closed | [
"release notes: fx",
"fx",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149656
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,936,437,864 | elif is not a cmake keyword | atupone | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 10 | CONTRIBUTOR | Test for pocketfft_header not in its place is wrong | true |
2,936,417,727 | Make sure to write to caches atomically | aorenste | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | This is an attempt to fix #119698
I was unable to reproduce the original described problem on the latest trunk but the proposed fix makes sense. Instead of adding locks like the original (unlanded) fix I changed a few of the cache writes to be atomic file swaps (write to temp file, rename file) which should have th... | true |
2,936,398,244 | Compile generating empty cudagraphs when generated graph has no compute | HDCharles | open | [
"triaged",
"module: cuda graphs",
"oncall: pt2"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
Compile seems to generate empty cudagraphs when the graph has no compute, this can create a deluge of warning messages for a multi-layer model. compile should avoid cudagraphing empty graphs.
tlparse:
https://gist.github.com/HDCharles/c0d418e1307d9f5248b359b2ffa25427
repro:
https://gist.git... | true |
2,936,392,211 | partitioner: ensure collectives saved by SAC that are actually unused in the bw are properly not saved | bdhirsh | closed | [
"Merged",
"ciflow/trunk",
"release notes: autograd",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | This PR fixes one of the issues described here: https://github.com/pytorch/torchtitan/issues/866#issuecomment-2726015248
I spent some time trying to write a unit test and ultimately failed. If folks are interested I can spend more time trying to, but otherwise I have an E2E test with torchtitan. command:
```
CUDA_... | true |
2,936,380,553 | [ca] fix accumulate grad polyfill when different strides between param and grad | xmfan | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 3 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149784
* #149773
* __->__ #149651
* #149709
* #149647
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhun... | true |
2,936,380,445 | [aot] maybe mark activations as dynamic | xmfan | closed | [
"ciflow/inductor"
] | 2 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149651
* __->__ #149650
* #149649
* #149647
* #149229
| true |
2,936,380,317 | [ca] torch.compile API comments and support older dynamic shapes API used in benchmarks | xmfan | closed | [
"module: dynamo",
"ciflow/inductor",
"module: compiled autograd"
] | 2 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149651
* #149650
* __->__ #149649
* #149647
* #149229
| true |
2,936,380,284 | [WIP][dynamic shapes] size-oblivious rewrite for infer_size, contiguity | pianpwk | closed | [
"ciflow/trunk",
"release notes: fx",
"fx",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,936,380,216 | [ca] use torch.compile ca API for benchmarks | xmfan | closed | [
"Merged",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 2 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149784
* #149773
* #149651
* #149709
* __->__ #149647
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,936,347,850 | [ONNX] Support running bfloat16 models with ONNX Runtime | justinchuby | closed | [
"module: onnx",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: onnx",
"topic: improvements"
] | 15 | COLLABORATOR | Use ORTValue objects to support bfloat16 and other dtypes as inputs. This only supports cuda as ort only implements bfloat16 on cuda.
| true |
2,936,311,289 | [torch/c10d] change class variable from private to protected (#149579) | GirasoleY | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 9 | CONTRIBUTOR | Summary:
Change class variable from private to protected in ProcessGroupNCCL
Test Plan: Existing UT Pass.
Reviewed By: kingchc, kwen2501
Differential Revision: D71373067
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,936,305,859 | op should NOT be static in aoti_torch_call_dispatcher | pytorchbot | closed | [
"open source",
"ciflow/inductor"
] | 1 | COLLABORATOR | aoti_torch_call_dispatcher is meant to call different ops, so the op must not be static. Otherwise, every call to this API will call the first op that was ever called, which is not the intended behavior of any human being.
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149230
* #14905... | true |
2,936,305,659 | Remove `torch.utils` from `MOD_SKIPLIST` | guilhermeleobas | open | [
"open source",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149748
* __->__ #149643
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,936,294,974 | [ca] torch.compile API comments and support older dynamic shapes API used in benchmarks | xmfan | closed | [
"module: dynamo",
"ciflow/inductor",
"module: compiled autograd"
] | 2 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149367
* #148516
* __->__ #149642
* #149641
* #149229
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,936,294,877 | [ca] use torch.compile ca API for benchmarks | xmfan | closed | [
"module: dynamo",
"ciflow/inductor"
] | 2 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149367
* #148516
* #149642
* __->__ #149641
* #149229
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,936,294,265 | torch.distributed.checkpoint CUDA OOM with broadcast_from_rank0 | nikonikolov | open | [
"oncall: distributed",
"module: cuda",
"triaged",
"module: fsdp"
] | 2 | CONTRIBUTOR | I am trying to load an FSDP checkpoint by broadcasting weights from rank 0. The model is already correctly set up on GPU on each rank. I use
```python
model_state_dict = torch.distributed.checkpoint.state_dict.set_model_state_dict(
model=self._model,
model_state_dict=model_state_dict,
options=torch.distrib... | true |
2,936,267,519 | Pylint error: ` torch.linalg.vector_norm is not callable` | adosar | open | [
"module: typing",
"module: lint",
"triaged",
"actionable"
] | 3 | NONE | ### 🐛 Describe the bug
```python
# test.py
import torch
if __name__ == "__main__":
t = torch.linalg.vector_norm(torch.randn(32, 4))
```
Pylint throws the following error:
```
************* Module test
test.py:1:0: C0114: Missing module docstring (missing-module-docstring)
test.py:4:8: E1102: torch.linalg.vector... | true |
2,936,265,115 | [Release/2.6] Pin requirements | ethanwee1 | closed | [
"oncall: distributed",
"module: rocm",
"module: cpu",
"release notes: releng",
"fx",
"module: inductor",
"module: dynamo"
] | 1 | CONTRIBUTOR |
Validation:
http://rocm-ci.amd.com/job/pytorch2.6-manylinux-wheels_rel-6.4-preview/15/
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd @jgong5 @mingfeima @Xiaobin... | true |
2,936,169,456 | Fix is_nonzero for more than one elem tensors | tugsbayasgalan | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: fx",
"fx",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149637
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv
Differential Revision: [D71560442](https://our.internmc.facebook.com/intern/diff/D71560442) | true |
2,936,155,389 | Remove custom kwargs before calling BuildExtension.__init__(...) | janeyx99 | open | [] | 6 | CONTRIBUTOR | Remove custom kwargs before calling `BuildExtension.__init__(...)`
This should fix what is going on in https://fb.workplace.com/chat/t/100068823519463#:~:text=https%3A//github.com/pytorch/rl/actions/runs/13974012630/job/39123001095
cc @vmoens
| true |
2,936,147,432 | avoid guarding on max() unnecessarily | bdhirsh | open | [
"triaged",
"oncall: pt2",
"module: dynamic shapes",
"vllm-compile"
] | 7 | CONTRIBUTOR | here's a repro. theoretically the code below should not require a recompile. We are conditionally padding, producing an output tensor of shape max(input_size, 16). Instead though, we specialize on the pad value, and produce separate graphs for the `size_16` and `size_greater_than_16` cases
```
import torch
@torch.comp... | true |
2,936,130,645 | [ONNX] Improve onnx ops docs | justinchuby | closed | [
"module: onnx",
"triaged"
] | 0 | COLLABORATOR | https://pytorch.org/docs/main/onnx_ops.html
Improve example to show the onnx op being used with torch ops. | true |
2,936,122,813 | DRAFT: HasData | rec | closed | [
"open source",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149633
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
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