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 |
|---|---|---|---|---|---|---|---|---|
2,901,822,822 | Re-enable tests | cyyever | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"test-config/asan"
] | 3 | COLLABORATOR | No UBSAN failures. | true |
2,901,805,467 | [WIP] backed_size_oblivious=True for export | pianpwk | open | [
"fx",
"module: inductor",
"ciflow/inductor",
"release notes: export"
] | 1 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv @voznesenskym @penguinwu @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,901,790,922 | [ONNX] Handle error in verification interpreter | justinchuby | closed | [
"module: onnx",
"open source",
"Merged",
"ciflow/trunk",
"release notes: onnx",
"topic: not user facing"
] | 6 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148707
* __->__ #148730
Use a simple try catch to handle onnx runtime errors in the verification interpreter when that happens. One example is ort will sometimes produce a list of None for some nodes. I am not sure how that happens yet.... | true |
2,901,784,521 | [inductor] Fix division by zero error in fractional max | isuruf | closed | [
"open source",
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 3 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148729
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov
Fixes https://github.co... | true |
2,901,780,983 | UNSTABLE trunk / libtorch-linux-focal-cuda12.6-py3.10-gcc9-debug / build | malfet | closed | [
"module: ci",
"triaged",
"unstable"
] | 1 | CONTRIBUTOR | Followup after https://github.com/pytorch/pytorch/issues/148495 (new one has been migrated to cuda12.6)
cc @seemethere @pytorch/pytorch-dev-infra | true |
2,901,777,242 | Add ccode for FloorDiv | kalpit-meta-1 | closed | [
"module: cpu",
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 13 | CONTRIBUTOR | Summary: Add ccode for FloorDiv
Test Plan: CIs
Differential Revision: D70749021
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 | true |
2,901,760,551 | Update the comment | Microve | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 10 | CONTRIBUTOR | Differential Revision: D70747931
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,901,757,013 | [Inductor] Missed block pointer for tiled + broadcast load | blaine-rister | closed | [
"oncall: pt2"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
On this example program:
```
import torch
import torch._inductor.config as config
config.triton.prefer_nd_tiling = True
config.triton.use_block_ptr = True
full_size = (114, 10, 160)
def get_input(view_size):
full = torch.rand(full_size, device="cuda")
view = torch.as_strided(full, vie... | true |
2,901,750,946 | Add ccode for FloorDiv | kalpit-meta-1 | closed | [
"module: cpu",
"fb-exported"
] | 8 | CONTRIBUTOR | Summary: Add ccode for FloorDiv
Test Plan: CIs
Differential Revision: D70746841
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 | true |
2,901,747,488 | Don't clear feedback_saver_fns after cache clear | exclamaforte | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Summary:
Since feedback_saver_fns are used for logging, I don't think it makes sense to clear them, and this resulted in weird behavior in user code where disabling caches caused logging code to break.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @ji... | true |
2,901,736,074 | Workaround no triton float8_e8m0fnu support in inductor | eellison | closed | [
"Merged",
"ciflow/trunk",
"release notes: fx",
"fx",
"module: inductor",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148722
Triton doesn't support actual float8_e8m0fnu yet, so we can't currently codegen any arithmetic on them. But we can support bitcasting, and view/memory operators and treat them as uint8 for now. Fix for https://github.com/py... | true |
2,901,724,871 | [cuSPARSE][B200] Bump tolerances for test_sparse_csr matvec | eqy | closed | [
"module: sparse",
"module: cuda",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | COLLABORATOR | Small tolerance bump for blackwell (appears to use same kernel as prev. arches)
cc @alexsamardzic @nikitaved @pearu @cpuhrsch @amjames @bhosmer @jcaip @ptrblck @msaroufim | true |
2,901,719,122 | replace usages of upload_graph in inductor with tlparse (v2) | bdhirsh | closed | [
"Merged",
"ciflow/trunk",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"release notes: inductor"
] | 19 | CONTRIBUTOR | Reland of https://github.com/pytorch/pytorch/pull/148703
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #133044
* #147561
* __->__ #148720
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenya... | true |
2,901,716,857 | [MPS][BE] Align bitshift behavior with CPU | malfet | closed | [
"Merged",
"ciflow/trunk",
"topic: improvements",
"release notes: mps",
"ciflow/mps",
"no-runner-experiments"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148719
* #148686
* #148685
By casting the argument to output type | true |
2,901,700,348 | [Inductor] Permuted memory access pattern for tiled pointwise kernels | blaine-rister | closed | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 11 | CONTRIBUTOR | ### 🐛 Describe the bug
On this example program:
```
import torch
import torch._inductor.config as config
config.triton.prefer_nd_tiling = True
config.triton.use_block_ptr = True
full_size = (21, 32)
view_size = (21, 19)
def get_input():
full = torch.rand(full_size, device="cuda")
view = torch.as_strided(ful... | true |
2,901,658,170 | Update win-vs2022-cuda12.1-py3 -> win-vs2022-cuda12.6-py3 | atalman | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Should have been migrated long ago | true |
2,901,632,664 | [mm_logs] enhance the printing for overview info | YUNQIUGUO | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 8 | CONTRIBUTOR | Summary:
previously the dynamo counters does not print the counts information automatically.
explicitly added a log msg to print after lowering for overview info for inductor aten mms
it will look like:
the name is in `{aten_op_name}_{m}_{n}_{k}`
```
torch/_inductor/compile_fx.py:832] [0/0] Overview info ... | true |
2,901,619,425 | [FSDP2][doc] highlight equivalence of set_requires_gradient_sync and no_sync | weifengpy | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (fsdp)",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148715
we got asked a few times about FSDP2's equivalence of no_sync. highlight
set_requires_gradient_sync as the equivalence in docstring
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,901,611,365 | Fix too big to optimize in test, actually use O0 when aot_inductor.compile_wrapper_with_O0 is set | yushangdi | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 8 | CONTRIBUTOR | Summary:
1. Check against the "0" char instead
2. We got the following error when using anything other than O0 flag: `error: Function ZN5torch12aot_inductorL22__check_inputs_outputsEPP16AtenTensorOpaqueS3 is too big to optimize [-Werror,-Wignored-optimization-argument]` So we use O0 flag in wrapper code when `aot_i... | true |
2,901,610,947 | [torch.export] How to export with the model having *args and **kwargs as forward signature? | titaiwangms | closed | [
"oncall: pt2",
"oncall: export"
] | 3 | COLLABORATOR | This is the original model code:
```python
from diffusers.models import AutoencoderKL
import torch
model_name = "black-forest-labs/FLUX.1-dev"
hf_safetensor = True
model_opts = {'torch_dtype': torch.float16}
model = AutoencoderKL.from_pretrained(model_name, subfolder="vae", use_safetensors=hf_safetensor, force_downlo... | true |
2,901,603,849 | Fix calling torch.compile inside of a `__torch_dispatch__` | zou3519 | open | [
"Stale",
"module: dynamo",
"ciflow/inductor",
"keep-going"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148712
We should only bail out in the following situation:
```py
with torch_dispatch_mode()
torch.compile(f)(x)
```
However, before this PR, we are also bailing out in the following
situation:
```py
with torch_dispatch_mode()
n... | true |
2,901,578,447 | Flex attention significantly slower than SDPA | nikonikolov | closed | [
"triaged",
"oncall: pt2",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention",
"module: sdpa"
] | 5 | CONTRIBUTOR | I have been trying to use flex attention but it seems to be significantly slower than the SDPA attention. Reproduction script in https://gist.github.com/nikonikolov/4cf740b8f9268f4386a4394c7f663e12
With the script, the average time for a forward pass with flex attention is ~ `704` vs `319` with SDPA.
```
PyTorch vers... | true |
2,901,548,390 | [Just SCRTCH] no review | laithsakka | open | [
"Stale",
"release notes: fx",
"fx",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148710
* #148430
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,901,547,585 | fix lost input mutations with export_tracepoint | avikchaudhuri | closed | [
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: export"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148709
Preserving module call signatures in the presence of input mutation cause incorrect results. The root cause turned out to be that export tracepoints would unwrap / wrap functional args that would lose mutation info on those ar... | true |
2,901,532,470 | [DO NOT REVIEW, review 148124 instead] stable torch library draft | janeyx99 | closed | [
"ciflow/trunk",
"release notes: cpp",
"ciflow/inductor"
] | 8 | CONTRIBUTOR | importable copy of https://github.com/pytorch/pytorch/pull/148124
| true |
2,901,531,624 | [ONNX] Create documentation for ONNX verification tools | justinchuby | closed | [
"open source",
"release notes: onnx"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148707
* #148730
| true |
2,901,531,534 | [ONNX] Improve verify_onnx_program to use VerificationInterpreter | justinchuby | closed | [
"module: onnx",
"open source",
"Merged",
"ciflow/trunk",
"release notes: onnx",
"topic: not user facing"
] | 9 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148707
* __->__ #148706
I realized we can just extend `verify_onnx_program` to return intermediate values. There is no need for us to expose the VerificationInterpreter to users.
I added a `compare_intermediates` option to `verify_onnx_... | true |
2,901,515,590 | Bump triton pin. Add aarch64 triton build | atalman | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ciflow/rocm",
"ciflow/inductor-rocm"
] | 12 | CONTRIBUTOR | 1. Bumps pin for triton to release/3.3.x branch
2. Bump pin for triton-xpu
3. Remove ROCm xfail tests
4. Add aarch64 triton build:
* Depends on: https://github.com/pytorch/pytorch/pull/148768
* Fixes: https://github.com/pytorch/pytorch/issues/130558
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen... | true |
2,901,497,524 | cleanup JK for duplicate pt2 compile callbacks prevention | burak-turk | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 12 | CONTRIBUTOR | Summary: This diff cleans up the JK we used for enabling `add pt2 callbacks for backward pass and prevent duplicate callbacks` feature.
Differential Revision: D70643543
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 ... | true |
2,901,476,303 | replace usages of upload_graph in inductor with tlparse | bdhirsh | closed | [
"fb-exported",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 7 | CONTRIBUTOR | Summary:
context here: https://fb.workplace.com/groups/1286739428954016/posts/1447200899574534/?comment_id=1447204456240845
we shouldn't be uploading graphs during compilation, which can be slow. Instead, we should be relying on tlparse everywhere we need to to dump intermediate artifacts to disk during compilation, s... | true |
2,901,467,764 | [torch.export] Dynamic shapes disappear after run_decompositions(decomp_table=None) | titaiwangms | closed | [
"triaged",
"oncall: pt2",
"export-triaged",
"oncall: export"
] | 11 | COLLABORATOR | ### 🐛 Describe the bug
The exported program does not keep sym_size_int after decompositions.
```python
import torchvision
import torchaudio
import torch
# define a pytorch model
class SpecMaker(torch.nn.Module):
def __init__(self):
super().__init__()
self.transforms = torchvision.transforms.Comp... | true |
2,901,426,451 | aot_eager produces wrong output with all_gather_tensor_autograd | eellison | open | [
"high priority",
"oncall: distributed",
"triaged",
"actionable",
"module: correctness (silent)",
"oncall: pt2",
"module: pt2-dispatcher"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
See repro here:
```
"""
torchrun --nproc-per-node 2 /home/dhaziza/work/scripts/aot_bug.py
"""
import torch
import torch.distributed as dist
import torch.distributed._functional_collectives as ft_c
def model(x):
x = ft_c.all_gather_tensor_autograd(x, gather_dim=0, group=dist.group.WORLD)
... | true |
2,901,406,012 | Add cpp wrapper skip to cudagraph logs | eellison | 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):
* __->__ #148700
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,901,403,626 | CUDA 12.6 Inductor perf test failures | atalman | open | [
"high priority",
"module: cuda",
"module: ci",
"triaged"
] | 6 | CONTRIBUTOR | We are working on PR to move our Inductor CUDA 12.4 -> 12.6
This is pull request:
https://github.com/pytorch/pytorch/pull/148612/
We do see 2 test failures and 1 pass
fail_accuracy:
timm_efficientnet,fail_accuracy,7
crossvit_9_240,fail_accuracy,7
pass:
tinynet_a,pass,6
Please note: this looks like an improvement, i... | true |
2,901,399,352 | Tell dmypy to ignore bad package | aorenste | closed | [
"topic: not user facing"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148698
| true |
2,901,376,234 | Remove redundant moves in kernel.cpp | justinchuby | closed | [
"oncall: jit",
"open source",
"NNC",
"release notes: jit",
"topic: not user facing"
] | 2 | COLLABORATOR | During compilation the gcc compiler suggests
> ```
> pytorch/torch/csrc/jit/tensorexpr/kernel.cpp: In member function ‘torch::jit::tensorexpr::ExprHandle torch::jit::tensorexpr::TensorExprKernel::getVarForShape(const c10::ShapeSymbol&)’:
> pytorch/torch/csrc/jit/tensorexpr/kernel.cpp:486:21: warning: redundant mov... | true |
2,901,360,190 | [dynamic shapes] add backed_size_oblivious option | pianpwk | closed | [
"Merged",
"ciflow/trunk",
"release notes: fx",
"fx",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Adds option `torch.fx.experimental._config.backed_size_oblivious = True` to allocate `[0, inf]` instead of `[2, inf]` ranges for size backed symbols, and opting into size-oblivious semantics for them.
Helps in a number of cases like
- Keeps `[0, inf]` bounds for unbacked symbols, when we make a unbacked -> backed r... | true |
2,901,354,580 | dynamo fakification errors with opaquetensorimpl | j4orz | open | [
"module: autograd",
"triaged",
"oncall: pt2",
"module: fakeTensor",
"module: pt2-dispatcher"
] | 2 | NONE | hacking away on the pytorch backend for tinygrad with the guidance of @albanD , and one of the early designs being explored is wrapping a tinygrad tensor in an `opaquetensorimpl` with `privateuse1` dispatch[0] [1], even though the former is for much older integrations (XLATensor) with no storage nor stride.
because of... | true |
2,901,285,556 | [ca] use torch.compile ca API for benchmarks and fix API to allow specifying dynamic via configs instead of torch.compile kwargs | xmfan | open | [
"module: dynamo",
"ciflow/inductor"
] | 2 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148516
* #149420
* #149367
* __->__ #148694
* #149229
* #149336
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,901,207,934 | [logging] Set compile_id in the CachingAutotuner during compilation so we have it for dynamo_timed logging | masnesral | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"ci-no-td"
] | 8 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148693
Summary: This is a simpler alternative to https://github.com/pytorch/pytorch/pull/146455, where we can stick the compileId (and forward/backward bool) in the CachingAutotuner so that we have it for logging `benchmark_all_conf... | true |
2,901,184,195 | [RFC][cutlass backend] Reduce precompile error to log.info level | henrylhtsang | closed | [
"fb-exported",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148692
Differential Revision: [D70719832](https://our.internmc.facebook.com/intern/diff/D70719832/)
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf22... | true |
2,901,144,918 | codecache.py: use str.format rather than % formatting | benjaminglass1 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 5 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148691
Additionally, swaps over a fixed length `std::vector` used by `cpp_wrapper` for a `std::array`.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipis... | true |
2,901,128,158 | [RFC] Make Functional Collectives Differentiable | wconstab | closed | [
"oncall: distributed",
"triaged"
] | 3 | CONTRIBUTOR | Currently, functional collectives are by default not differentiable, which can lead to surprises.
@d4l3k added support for differentiable functional collectives in a stack of PRs beginning with https://github.com/pytorch/pytorch/pull/123599. For now, these are 'separate' so users have to opt into them. Furthermore,... | true |
2,901,102,071 | NUMA Binding Integration with torchrun | raghavhrishi | open | [
"oncall: distributed",
"triaged"
] | 14 | NONE | ### 🚀 The feature, motivation and pitch
The feature request involves adding NUMA (Non-Uniform Memory Access) binding capabilities to torchrun as an option to optimize distributed training performance. This feature will automatically manage process-to-CPU core binding based on GPU-CPU topology, improving resource uti... | true |
2,901,091,999 | Fix _del_library | zou3519 | closed | [] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148104
* __->__ #148688
* #148092
* #148091
* #148063
* #148046
On library deletion, we need to clear fx's schema cache.
Test Plan:
- next PR up in the stack | true |
2,901,076,969 | torch.onnx.export with torchaudio Spectrogram doesn't support dynamic batch size | sammlapp | closed | [
"module: onnx",
"triaged"
] | 11 | NONE | ### 🐛 Describe the bug
It is now possible to include torchaudio.transforms.Spectrogram in a model and successfully export the model to an onnx program. However, when loading the model I cannot use a batch size besides the one used in the saved model. I've tried several approaches. Here is an example based on https://... | true |
2,901,025,795 | [MPS] Fix scalar to tensors bitshifts | malfet | closed | [
"Merged",
"topic: bug fixes",
"release notes: mps",
"ciflow/mps"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148719
* __->__ #148686
* #148685
By introducing a concept of non-commutative binary op and renaming all op templates from `bitwise_foo_tensor` and `bitwise_foo_scalar` to `bitwise_foo_tensor_tensor` and `bitwise_foo_tensor_scalar`
Add ... | true |
2,901,025,686 | [BE][MPS] Remove redundant `handle_tensor_scalar_binary_op` | 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):
* #148686
* __->__ #148685
After https://github.com/pytorch/pytorch/pull/143934 `mtl_setBuffer` can handle scalar tensors correctly, so no need to have a specialized function here | true |
2,900,997,675 | [triton 3.3] perf run, Mar 5 | davidberard98 | open | [
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148684
| true |
2,900,989,147 | [dynamo] Remove dead code path around `functools.partial` objects | StrongerXi | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148683
This removes the code paths added in #98120, which has then been
superceded by #108846.
More importantly, it makes `EQUALS_MATCH`'s `ok_mutable_types` (added in #134016)
easier to reason about, i.e., no need to worry about `d... | true |
2,900,917,171 | can we make inductor create faster fusions for tiled reductions across dim0 and dim1? | vkuzo | open | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
Can we make fusions of reductions across Mx1 and 1xM tiles fast in inductor? The key use case for this is scaling tensors to MX across both dim0 and dim1 at the same time, which is important for microscaling (MX) training. Here is an example snippet to demonstrate a simplified version of the ... | true |
2,900,898,265 | [CUDA 12.6 CI] Update of cuda 12.6 eager tests failing on test_pointwise_op_with_tensor_of_scalarlist_overload__foreach_addcmul_is_fastpath_True_cuda_bfloat16 | atalman | open | [
"module: cuda",
"triaged"
] | 0 | CONTRIBUTOR | While updating to CUDA 12.6 eager test, PR: https://github.com/pytorch/pytorch/pull/148602
Failing workflow: https://github.com/pytorch/pytorch/actions/runs/13690790469/job/38285054097#step:22:4164
We see following test failure:
```
_ TestForeachCUDA.test_pointwise_op_with_tensor_of_scalarlist_overload__foreach_addcmu... | true |
2,900,770,678 | Torch Windows nightly installation with torchvision/audio broken by dependencies conflict | chuanqi129 | closed | [
"module: binaries",
"module: windows",
"triaged",
"module: xpu"
] | 5 | COLLABORATOR | Recently, the torch xpu Windows nightly wheel can't install with torchvision/audio, the failure as below
```
(nightly) C:\Users\chuanqiw>pip install --pre torch torchvision torchaudio --index-url=https://download.pytorch.org/whl/
nightly/xpu
Looking in indexes: https://download.pytorch.org/whl/nightly/xpu
Collecting t... | true |
2,900,758,922 | [inductor] Fix block ptr store if input is constant | kundaMwiza | open | [
"triaged",
"open source",
"topic: not user facing",
"module: inductor"
] | 2 | CONTRIBUTOR | Since block ptr stores require explicit broadcasts, the input to `tl.store` needs to be reshaped and broadcasted. Currently, it is assumed that the input to be stored is in block form (e.g. `XBLOCK`), however it is possible for the input to be a scalar, and so special handling is required to reshape + broadcast the sca... | true |
2,900,723,490 | Improve Pareto frontier plot for AutoAC | lw | closed | [
"Merged",
"ciflow/trunk",
"release notes: autograd",
"module: functorch",
"release notes: torch.func",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148678
This was added in https://github.com/pytorch/pytorch/pull/126320. It's a very nice feature, which can be used to predict memory usage for different budget values.
However, it had some limitations, notably in terms of resoluti... | true |
2,900,707,061 | xpu: update filter out of dg2 AOT target | dvrogozh | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"release notes: xpu"
] | 13 | CONTRIBUTOR | torch-xpu-ops has updated list of AOT targets to use and used `dg2` instead of `dg2-g10`. This requires an update in cpp_extension.py which currently filters out `dg2-` prefixed AOT targets.
CC: @gujinghui @EikanWang @fengyuan14 @guangyey @jgong5 | true |
2,900,704,307 | [dynamo] allow global import `from collections import deque` in user code | XuehaiPan | closed | [
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"ci-no-td",
"ciflow/inductor-rocm"
] | 9 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #138214
* #113258
* #148569
* __->__ #148676
See https://github.com/pytorch/pytorch/pull/148669#discussion_r1983462218 for more details.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wen... | true |
2,900,693,003 | Fix static functions when using module in MSVC | taras-janea | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: releng"
] | 12 | COLLABORATOR | If you try to use torch in c++ using modules then it will not compile due to static function not being supported in MSVC when using modules https://developercommunity.visualstudio.com/t/10323558.
It's also aligned with [C++20 standard](https://www.open-std.org/jtc1/sc22/wg21/docs/papers/2020/n4849.pdf) (ISO/IEC 1488... | true |
2,900,676,729 | Set /NODEFAULTLIB:vcomp for MSVC when linking caffe2::mkl with libiomp5md.lib | taras-janea | open | [
"module: build",
"module: windows",
"triaged",
"open source",
"release notes: build",
"topic: bug fixes"
] | 8 | COLLABORATOR | Fixes:
- https://github.com/pytorch/pytorch/issues/113490
When using the Microsoft Visual C++ Compiler with Intel® OpenMP, it's needed to avoid linking the Microsoft OpenMP runtime library (vcomp) and explicitly pass the name of the Intel® OpenMP compatibility library as linker options.
More details: https://www... | true |
2,900,668,259 | Remove shebang line from easy_install generated python scripts on Windows only | taras-janea | open | [
"module: windows",
"triaged",
"open source",
"topic: bug fixes",
"topic: not user facing"
] | 9 | COLLABORATOR | Fixes
- #108602
On windows only, for install step: remove shebang line from python scripts generated by `easy_install`.
cc @peterjc123 @mszhanyi @skyline75489 @nbcsm @iremyux @Blackhex | true |
2,900,656,691 | [ROCm] Enable max_autotune run on inductor perf dashboard | jataylo | open | [
"module: rocm",
"open source",
"topic: not user facing",
"ciflow/rocm",
"ciflow/inductor-perf-test-nightly-rocm"
] | 5 | COLLABORATOR | Enables max_autotune on ROCm inductor dashboard
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @hongxiayang @naromero77amd | true |
2,900,640,504 | Improve error handling when checking CUDA version in case nvcc is not found | taras-janea | closed | [
"module: windows",
"triaged",
"open source",
"Merged",
"release notes: fx"
] | 9 | COLLABORATOR | Fixes:
- https://github.com/pytorch/pytorch/issues/101138
**Description**
The PR enhances error handling in `_check_cuda_version` by verifying the existence of the `nvcc` executable before invoking `subprocess.check_output`. If `nvcc` is missing, a `FileNotFoundError` is raised with a clear message, guiding users ... | true |
2,900,610,160 | MPS Backend Error: ComplexDouble (complex128) Conversion Fails When Diffusers Transformer Creates 64‐bit Complex Tensors | mozzipa | open | [
"feature",
"triaged",
"module: complex",
"module: mps"
] | 0 | NONE | ### 🐛 Describe the bug
When running a diffusers-based transformer pipeline (e.g., the WanPipeline from diffusers) on Apple’s MPS device, an error is raised because a tensor is being converted to a ComplexDouble (i.e. torch.complex128) type. The error message is:
`TypeError: Trying to convert ComplexDouble to the MPS... | true |
2,900,566,224 | [pytree] fix previously failed dynamo tests | XuehaiPan | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: pytree",
"module: dynamo"
] | 3 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148676
* #148569
* __->__ #148669
cc @zou3519 @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,900,495,357 | Fix CUPTI lookup to include target directory | mgorny | open | [
"triaged",
"open source",
"Stale"
] | 3 | CONTRIBUTOR | CUPTI library and headers are installed to the target subdirectory rather than the top-level prefix in conda-forge. Include `CUDAToolkit_TARGET_DIR` subdirectories in CUPTI search paths to fix finding it in that environment.
| true |
2,900,349,201 | Enable FSDP2 on HPU device | AnantGulati | closed | [
"oncall: distributed",
"open source",
"Merged",
"ciflow/trunk",
"release notes: distributed (fsdp)"
] | 6 | CONTRIBUTOR | The motivation of this PR is to enable FSDP2 collectives for HPU
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,900,336,111 | [HPU] Add hpu to fused kernels supported devices | Nitin-x2a | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: foreach_frontend"
] | 3 | CONTRIBUTOR | This change adds "hpu" to the list of device types that support fused kernels in the optimizer, ensuring
compatibility with HPU backend.
Without this change, when `test_all_gather_extension_outer_size_stride` of `pytorch/test/distributed/_composable/fsdp/test_fully_shard_extensions.py` is run on 'hpu' backend, it f... | true |
2,900,332,600 | DISABLED test_wrap_all_kwarg_dynamic_shapes (__main__.DynamicShapesHigherOrderOpTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamo"
] | 7 | NONE | Platforms: asan, linux, rocm, mac, macos
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_wrap_all_kwarg_dynamic_shapes&suite=DynamicShapesHigherOrderOpTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorc... | true |
2,900,224,309 | [AOTInductor] Codegen fix | Dinesh-Mareedu | open | [
"triaged",
"open source",
"module: inductor"
] | 5 | NONE | -- Added CPP Codegen support for the List[Optional[Tensor]] data type during function argument conversion from Python, as the existing codegen did not support it.
-- Modified std::vector to c10::ArrayRef to solve below runtime issues due to signature mismatch.
**Error Message:**
correct signature: std::vector<at:... | true |
2,900,152,727 | [Profiler][HPU] Fix incorrect availabilities for HPU | wdziurdz | closed | [
"triaged",
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"keep-going",
"ci-no-td"
] | 22 | CONTRIBUTOR | Fixes #148661
| true |
2,900,152,577 | [Triton 3.3] Remove ROCm specific mm gemm template | AmdSampsa | closed | [
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 11 | COLLABORATOR | Fixes: https://github.com/pytorch/pytorch/issues/147121
Since triton 3.3.x fixes the problem
Needs to be handled in none BC breaking way, so we will conditionalise this change on triton version.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd... | true |
2,900,149,450 | [Profiler][HPU] Incorrect availabilities for the HPU device | wdziurdz | closed | [
"triaged",
"module: hpu"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
This commit does not contain the complete availabilities for HPU devices (https://github.com/pytorch/pytorch/pull/148182).
We need to add the complete availabilities for HPU devices.
### Versions
Collecting environment information...
PyTorch version: 2.6.0+hpu.git99dbd97
Is debug build: Fals... | true |
2,900,117,493 | Remove deprecated std::aligned_storage_t | cyyever | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 7 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,900,101,536 | [HPU] Add HPU as a supported device for NestedTensor | Nitin-x2a | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: hpu"
] | 9 | CONTRIBUTOR | This change enables basic NestedTensor operations on HPU,
fixing the runtime error when creating a NestedTensor on HPU.
- Extended `NestedTensorImpl` to recognize `hpu` as a valid storage device.
- Added `NestedTensorHPU` to `DispatchKey` parsing in `DispatchKey.cpp`.
- Updated `torchgen/model... | true |
2,900,027,217 | [docs] fix autograd description on convex function case | dw61 | closed | [
"triaged",
"open source",
"Merged",
"topic: not user facing"
] | 5 | CONTRIBUTOR | The sub-gradient of minimum norm is the least steep descent direction.
```python
import torch
x = torch.tensor([-2, -1, 0, 1, 2.], requires_grad=True)
torch.relu(x).sum().backward()
print(x.grad) # tensor([0., 0., 0., 1., 1.])
y = torch.tensor([-2, -1, 0, 1, 2.], requires_grad=True)
torch.abs(y).sum().back... | true |
2,900,025,097 | [associative_scan] Refactoring of input checking and dynamo invocation | bohnstingl | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo"
] | 10 | COLLABORATOR | This PR is the counterpart of https://github.com/pytorch/pytorch/pull/142125 for the associative_scan operation. The way the input checks are performed and the combine_fn is not invoked in the frontend to check the output trees, but rather dynamo is used for that.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guob... | true |
2,899,909,032 | Allow to run flex_attention on HPU | m-a-nowak | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: flex attention"
] | 11 | CONTRIBUTOR | HPU specific implementation details are to be located in out-of-tree HPU library.
cc @Chillee @drisspg @yanboliang @BoyuanFeng | true |
2,899,882,270 | Extra onnx::Neg_2 input after torch.onnx.export | xeasonx | open | [
"module: onnx",
"triaged",
"OSS contribution wanted"
] | 2 | NONE | ### 🐛 Describe the bug
Convert huggingface model meta-llama/Llama-3.2-1B to ONNX
```python
input_ids = torch.ones((1, 256), dtype=torch.long)
attention_mask = torch.ones((1, 256), dtype=torch.long)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="cpu"
)
mod... | true |
2,899,827,571 | Enable ruff check for `torch/utils/data/*.ipynb` | zeshengzong | open | [
"triaged",
"open source",
"release notes: dataloader"
] | 3 | CONTRIBUTOR | Fixes part of #146411
Enable ruff check for `torch/utils/data/*.ipynb` files
## Test Result
```bash
lintrunner -a --take RUFF torch/utils/data/*.ipynb
```
cc @Skylion007 | true |
2,899,784,759 | Enable qint8 and quint8 add for AArch64 using ACL directly | fadara01 | closed | [
"module: cpu",
"open source",
"module: arm",
"Merged",
"release notes: quantization",
"ciflow/linux-aarch64",
"arm priority"
] | 7 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148653
* #148585
This enables qint8 and quint8 add for AArch64 through Arm Compute Library (ACL) directly.
Relative performance improvement using OMP_NUM_THREADS=1 is ~15x, using OMP_NUM_THREADS=32 it’s ~5.4x.
Co-authored-by: Da... | true |
2,899,737,968 | [Intel GPU] Fix SDPA dummy LSE output to match meta function | DDEle | closed | [
"module: cpu",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"keep-going",
"ciflow/xpu"
] | 6 | CONTRIBUTOR | To fix XPU patched UTs including
```bash
pytest -vs third_party/torch-xpu-ops/test/xpu/test_meta_xpu.py::TestMetaXPU::test_dispatch_symbolic_meta_outplace_nn_functional_scaled_dot_product_attention_xpu_bfloat16
```
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 | true |
2,899,700,103 | Avoid fork for TORCHINDUCTOR_COMPILE_THREADS > 1 | AmdSampsa | open | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 0 | COLLABORATOR | For inductor-related unit tests, setting the env variable `TORCHINDUCTOR_COMPILE_THREADS` to a bigger value than 1, leads many times to flaky behaviour.
I think this is because inductor combines multithreading with forking (multiprocessing) : this is a notorious pitfall in concurrent programming, and is known to creat... | true |
2,899,696,184 | [Inductor-CPU] With cpp-wrapper, some ATen ops don't get profiled with PyTorch profiler | sanchitintel | closed | [
"oncall: pt2",
"oncall: cpu inductor"
] | 0 | COLLABORATOR | ### 🐛 Describe the bug
With cpp-wrapper, some ATen ops don't get profiled with PyTorch profiler
Here's a code example (needs torchao), in which `_weight_int8pack_mm` ATen op doesn't appear in PyTorch profiling results.
<details>
```python
# Most of the code has been adapted from a script authored by leslie-fang-in... | true |
2,899,692,643 | Use oneDNN v3.7.1 for Intel GPU | ZhiweiYan-96 | closed | [
"module: mkldnn",
"open source",
"ciflow/linux-aarch64"
] | 3 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148649
cc @gujinghui @PenghuiCheng @XiaobingSuper @jianyuh @jgong5 @mingfeima @sanchitintel @ashokei @jingxu10 @min-jean-cho @yanbing-j @Guobing-Chen @Xia-Weiwen @snadampal | true |
2,899,659,313 | Bump Clang-tidy to 19.1.4 | cyyever | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 3 | COLLABORATOR | Because Clang-tidy 19 has more powerful clang-analyzer checks to detect subtle bugs. New checks such as misc-use-internal-linkage can help identify potential static variables or functions, thus reducing binary sizes.
Some new checks are disabled temporarily for later enabling. Additional warnings have been fixed or ... | true |
2,899,634,288 | [SGD] Add SGD capturable API and tests | zeshengzong | open | [
"open source",
"Stale",
"release notes: optim"
] | 3 | CONTRIBUTOR | Fixes #118018
| true |
2,899,618,156 | [XPU] Add an implict conversion from XPUStream to sycl::queue* | zhiweij1 | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: xpu"
] | 19 | CONTRIBUTOR | # Motivation
Currently, in Pytorch XPU, `cudaStream_t` is mapped to `sycl::queue&`, so an implicit cast from `XPUStream` to `sycl::queue&` is provided just like `CUDAStream` has an implicit cast to `cudaStream_t`.
But on the SYCLomatic side, we migrate `cudaStream_t` to `sycl::queue*` but not `sycl::queue&` (One ... | true |
2,899,597,898 | [inductor][torchbench][CI] timm models got obvious performance drop with --ci flag | LifengWang | closed | [
"module: ci",
"triaged",
"oncall: pt2",
"module: dynamic shapes"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
I observed that the accuracy test for timm_models with max_autotune experienced a performance degradation by multiples when the `--ci` flag was enabled. For example, the e2e time of spnasnet_100 increased from 42s to 4m52.451s after adding `--ci` flag.
This issue seems to be caused by the belo... | true |
2,899,508,439 | DISABLED test_set_stance_aot_eager_then_compile (__main__.DecoratorTests) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamo"
] | 2 | NONE | Platforms: linux, mac, macos, rocm, asan
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_set_stance_aot_eager_then_compile&suite=DecoratorTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/3828259245... | true |
2,899,508,355 | DISABLED test_symint_in_slice_dynamic_shapes (__main__.DynamicShapesHigherOrderOpTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamo"
] | 6 | NONE | Platforms: asan, linux, rocm, mac, macos
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_symint_in_slice_dynamic_shapes&suite=DynamicShapesHigherOrderOpTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch... | true |
2,899,501,732 | Feature request: throw `torch.cuda.OutOfMemoryError` for TorchScript OOM | njzjz | open | [
"oncall: jit"
] | 0 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
Currently, `torch.cuda.OutOfMemoryError` is used to throw OOM errors, which is easy to catch. However, TorchScript throws `RuntimeError` instead of `OutOfMemoryError`, as shown below. Thus, when one wants to catch a specific OOM error, it's not easy to do so.
```py
import torc... | true |
2,899,457,460 | Fix torch.utils.checkpoint import error | alphahui | closed | [
"open source"
] | 4 | NONE | # Problem
We were trying to use the torch.utils.checkpoint.checkpoint function directly with only the import of torch and without importing torch.utils.checkpoint in our script. However, we would encounter an import error "AttributeError: module 'torch.utils' has no attribute 'checkpoint'". We would like to propose a ... | true |
2,899,427,750 | [Intel GPU][quant] Refine zero-point memory creation | ZhiweiYan-96 | closed | [
"module: cpu",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor",
"keep-going",
"ciflow/xpu"
] | 10 | COLLABORATOR | # Motivation
This PR skips zero-point GPU memory creation when zero-point=0, as it would not be used by oneDNN library. This could help save the 1~3 H2D copy overhead per QLinear/QConv kernel.
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148640
cc @jgong5 @mingfeima @... | true |
2,899,419,292 | [inductor][cpu] poolformer_m36 AMP static shape multiple thread performance regression in 2025-03-03 nightly release | zxd1997066 | closed | [
"oncall: pt2",
"oncall: cpu inductor"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
<p>AMP static shape default wrapper</p><table border="1" class="dataframe table">
<thead>
<tr style="text-align: right;">
<th>suite</th>
<th>name</th>
<th>thread</th>
<th>batch_size_new</th>
<th>speed_up_new</th>
<th>inductor_new</th>
<th>eager_ne... | true |
2,899,408,026 | Remove cppcoreguidelines-pro-type-member-init_fix suppression | cyyever | closed | [
"oncall: jit",
"module: cpu",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: jit",
"module: dynamo",
"ciflow/inductor"
] | 6 | COLLABORATOR | Fixes #ISSUE_NUMBER
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @voznesenskym @penguinwu @EikanWang @Guobing-Chen @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,899,375,844 | [cutlass backend] switch host optimizer to O3 | henrylhtsang | closed | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148637
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,899,373,916 | [inductor][cpu] basic_gnn_gin and basic_gnn_sage AMP single thread performance regression in 2025-03-03 nightly release | zxd1997066 | closed | [
"oncall: cpu inductor"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
<p>amp static shape cpp wrapper</p><table border="1" class="dataframe table">
<thead>
<tr style="text-align: right;">
<th>suite</th>
<th>name</th>
<th>thread</th>
<th>batch_size_new</th>
<th>speed_up_new</th>
<th>inductor_new</th>
<th>eager_new</t... | true |
2,899,320,267 | Subprocess compile (attempt 2) | aorenste | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"fx",
"module: inductor",
"ciflow/inductor",
"ciflow/slow"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148635
Add a mode to fx_codegen_and_compile() to compile in a separate process. This is to prepare for async compile where we'll compile and run eager in parallel (and also be able to move the compile phase to a remote computer).
Ad... | true |
2,899,305,596 | README doesn't explain how to run tests in the "Test PyTorch" section | yurivict | closed | [] | 3 | NONE | ### 📚 The doc issue
README needs to have the "Test PyTorch" section after the [Install PyTorch](https://github.com/pytorch/pytorch#install-pytorch) section in the README.
Testing is the next step after building PyTorch.
### Suggest a potential alternative/fix
_No response_ | true |
2,899,265,329 | update torch.nn.RelicationPad{1,2,3}d deternimistic documentation | 1274085042 | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 23 | CONTRIBUTOR | https://github.com/pytorch/pytorch/issues/115395
This issue mentioned that when deterministic mode is turned on, added a decomp for replication_pad_{1,2,3}d
to make the backward function deterministic.
@malfet | true |
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