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 |
|---|---|---|---|---|---|---|---|---|
2,830,972,735 | [Metal] Small speedup for `sum`/`prod` | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146436
* #146429
* __->__ #146428
* #146423
As they can not really be invoked over empty arrays | true |
2,830,957,369 | add the `torch.float8_e8m0fnu` dtype to PyTorch | vkuzo | closed | [
"module: cpu",
"release notes: quantization",
"module: float8"
] | 10 | CONTRIBUTOR | Summary:
Adds the `torch.float8_e8m0fnu` dtype to PyTorch, as detailed in
https://github.com/pytorch/pytorch/issues/146414 . Please see the issue for a detailed definition of the format. Example of basic functionality:
```python
import torch
# round trip
x0 = torch.randn(4, 4, dtype=torch.float32)
x1 = x0... | true |
2,830,956,700 | Test typing of arithmetic operators on Tensor (see #145838) | rec | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 12 | COLLABORATOR | See #145838
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146426
| true |
2,830,953,854 | [ONNX] Create deprecation warning on dynamo_export | justinchuby | closed | [
"module: onnx",
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: onnx",
"topic: deprecation",
"ci-no-td"
] | 28 | COLLABORATOR | Reland #146003
Deprecation of `torch.onnx.dynamo_export`:
* [`torch/onnx/_internal/_exporter_legacy.py`]: Added deprecation warnings to the `OnnxRegistry`, `ExportOptions`, `ONNXRuntimeOptions`, and `dynamo_export` functions, indicating that `torch.onnx.dynamo_export` is deprecated since version 2.6.0 and should ... | true |
2,830,951,751 | cpp_wrapper: fix test_torchinductor* tests | benjaminglass1 | closed | [
"module: cpu",
"open source",
"Merged",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 4 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147225
* #146706
* #147403
* #146991
* #147215
* __->__ #146424
* #146109
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @voznesenskym @penguinwu @EikanWang @Guobing-Chen @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @... | true |
2,830,948,781 | [Metal][BE] Add `#pragma once` to all headers | malfet | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/mps"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146436
* #146429
* #146428
* __->__ #146423
| true |
2,830,904,568 | [metal] Add a missing cast to make the call to copysign unambiguous. | dcci | closed | [
"Merged",
"topic: not user facing",
"module: mps",
"ciflow/mps"
] | 3 | MEMBER | cc @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen | true |
2,830,885,330 | experimental specialization logging | bobrenjc93 | closed | [
"Stale",
"ciflow/trunk",
"release notes: fx",
"fx",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146421
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv
Differential Revision: [D69163120](https://our.internmc.facebook.com/intern/diff/D69163120) | true |
2,830,866,031 | [ROCm] Optimize the stride one indexing backwards kernel | doru1004 | closed | [
"module: rocm",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/periodic",
"rocm",
"ciflow/rocm"
] | 28 | CONTRIBUTOR | This patch makes several changes to the stride 1 backwards indexing kernel as follows:
- enables the computation across the `sorted_indices` array to happen in parallel by all the lanes in the warp, this means that the accesses to `sorted_indices` are now fully coalesced.
- the duplicate counting now happens in paral... | true |
2,830,850,956 | The value of requires_grad is not set when creating the tensor using TensorMaker | irshadcc | closed | [
"module: internals",
"module: cpp",
"triaged"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
I was trying to create a weight tensor using at::from_blob and I set the requires_grad flag in tensor options. After I created the tensor and checked the requires_grad value, I found that the requires_grad flag is not set.
```C++
#include <iostream>
#include <ATen/ops/embedding.h>
#include ... | true |
2,830,786,352 | [BE]: Add TypeVarTuple to RNN Args for better type inference | Skylion007 | closed | [
"open source",
"Stale"
] | 3 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,830,728,419 | Only call triton in worker process, kick off worker processes earlier, during inductor codegen | jamesjwu | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 41 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146417
### Big idea
This PR extends https://github.com/pytorch/pytorch/pull/144288 by combining calling triton in worker processes with the future cache: we kick off triton compilation in the worker processes earlier, during induct... | true |
2,830,722,495 | Silent correctness bug in Inductor when fusing transpose into other ops | lw | closed | [
"high priority",
"triage review",
"oncall: distributed",
"triaged",
"module: correctness (silent)",
"oncall: pt2",
"module: inductor"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
```py
import os
import torch
import torch.distributed._functional_collectives as funcol
os.environ["RANK"] = "0"
os.environ["WORLD_SIZE"] = "1"
os.environ["MASTER_ADDR"] = "127.0.0.1"
os.environ["MASTER_PORT"] = "2743"
torch.distributed.init_process_group(backend="nccl")
def scale(t):
s... | true |
2,830,715,038 | Only call triton in worker process, ahead of time compile | jamesjwu | closed | [
"fb-exported",
"Stale",
"module: inductor",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146415
# Big idea
This PR extends https://github.com/pytorch/pytorch/pull/144288 by combining calling triton in worker processes with the future cache: we kick off triton compilation in the worker processes earlier, during inductor c... | true |
2,830,683,665 | MX basic dtypes in pytorch/pytorch | vkuzo | open | [
"triaged",
"enhancement",
"needs research",
"module: python frontend"
] | 10 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
# Overview
The Open Compute Project introduced the [MicroScaling formats (MX)](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf) in Sep 2023, defining block-scaled dtypes with E8M0 block scales and FP8|FP6|FP4|INT8 block elements. We propo... | true |
2,830,668,570 | add support for capturing provenance of unary operations | bobrenjc93 | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: fx",
"topic: not user facing",
"fx",
"ciflow/inductor",
"ci-no-td"
] | 17 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146413
* #145848
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,830,668,448 | use DTRACE_ENV_VAR as the trace logs directory of set | bobrenjc93 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 10 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146413
* __->__ #146412
* #145848
```
(/home/bobren/local/a/pytorch-env) [7:47] devgpu035:/home/bobren/local/a/pytorch TORCH_DTRACE=/tmp/bb python r1.py
``` | true |
2,830,650,321 | Enable ruff and other linters on ipynb notebooks in PyTorch too | Skylion007 | open | [
"module: lint",
"triaged"
] | 0 | COLLABORATOR | ### 🚀 The feature, motivation and pitch
Enable ruff linter on ipynb notebooks in the PyTorch repo. We also have various formatters that support ipynb notebooks in the repo and should considering enabling them. Might be relevant to @justinchuby @aorenste
### Alternatives
_No response_
### Additional context
_No r... | true |
2,830,643,881 | [BE][Ez]: Enable ruff rule E731. use `def` instead of anonymous lambda | Skylion007 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | COLLABORATOR | Not sure why this isn't enabled, only 1 fix is needed and it supports autofixes. | true |
2,830,642,787 | ROCM Infra failures during checkout of PyTorch | atalman | closed | [
"high priority",
"module: rocm",
"module: ci",
"triaged"
] | 4 | CONTRIBUTOR | ## Current Status
ongoing
## Error looks like
Error during checkout pytorch: https://github.com/pytorch/pytorch/actions/runs/13130864428/job/36636990502
```
6m 59s
Run pytorch/pytorch/.github/actions/checkout-pytorch@main
Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else ... | true |
2,830,559,499 | [BE][Ez]: ISC001 Auto concatenate implicit one line strings | Skylion007 | closed | [
"oncall: distributed",
"oncall: jit",
"open source",
"better-engineering",
"Merged",
"ciflow/trunk",
"release notes: distributed (fsdp)",
"fx",
"module: dynamo",
"ciflow/inductor"
] | 8 | COLLABORATOR | Apply ruff rule about implicit string concatenation, this autofixes strings that are all the same type and on the same line. These lines are broken up likely as the result of autoformatters in the past. All fixes are automated using the autofixes in ISC001.
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @... | true |
2,830,395,844 | [ROCm] Unskip std:bad_alloc failures | jataylo | closed | [
"module: rocm",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ciflow/rocm"
] | 6 | COLLABORATOR | Flakey MI300 issue related to memory usage should now be resolved after https://github.com/pytorch/pytorch/actions/runs/13007160888?pr=145829.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @hongxiayang @naromero77amd @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingS... | true |
2,830,284,433 | Only enable aotriton on x86_64 and aarch64 | Xeonacid | closed | [
"triaged",
"open source",
"Stale",
"topic: not user facing"
] | 5 | NONE | Make `USE_FLASH_ATTENTION` and `USE_MEM_EFF_ATTENTION` depend on `CPU_INTEL OR CPU_AARCH64`.
[aotriton pre-built](https://github.com/ROCm/aotriton/releases) is only available on x86_64.
Although `AOTRITON_INSTALL_FROM_SOURCE` can be specified to build from source, building aotriton requires CUDA, so on architectu... | true |
2,830,228,799 | Small improvements to NJT matrix multiplies | michael-diggin | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: bug fixes",
"release notes: nested tensor"
] | 6 | CONTRIBUTOR | Fixes #146404
Adds changes to the matmul and matmul_backward operation for nested jagged tensors, to support back propagation when the output is a regular strided tensor.
This required adding support for the nested matmul operation to work when the nested tensor wasn't 'self', i.e
`A@B` where `A` isn't nested but ... | true |
2,830,216,130 | Can't back prop through NJT matrix multiplication when output is strided tensor | michael-diggin | closed | [
"triaged",
"module: nestedtensor",
"actionable"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
When performing a matmul between two NJTs such that the output is strided, it currently fails on the backward pass.
This is on latest nightly.
Repro:
```python
import torch
nt0 = torch.nested.nested_tensor([torch.rand(2, 6), torch.rand(3, 6)], layout=torch.jagged, requires_grad=True)
nt1 = t... | true |
2,830,185,748 | [1/N] Use std::string_view in torchgen | cyyever | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/periodic",
"module: aotinductor"
] | 21 | COLLABORATOR | Moves remaining c10::sv to std::sv
cc @desertfire @chenyang78 @penguinwu @yushangdi @benjaminglass1 | true |
2,830,052,032 | [2/N] Remove NOLINT suppressions | cyyever | closed | [
"oncall: jit",
"triaged",
"open source",
"Merged",
"release notes: cpp"
] | 3 | COLLABORATOR | Fixes #ISSUE_NUMBER
cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel | true |
2,829,945,230 | [ARM] Unit test failure - FreezingCpuTests.test_linear_binary_folding_cpu | robert-hardwick | open | [
"module: tests",
"triaged",
"module: arm"
] | 0 | COLLABORATOR | ### 🐛 Describe the bug
This test is not currently been enabled in ci and has been failing for an unknown period of time.
```
Traceback (most recent call last):
File "/var/lib/jenkins/workspace/test/inductor/test_binary_folding.py", line 302, in test_linear_binary_folding
test_linear_fusion(
File "/opt/conda... | true |
2,829,624,297 | DISABLED test_ddp_comm_hook_sparse_gradients (__main__.DistributedDataParallelTest) | pytorch-bot[bot] | open | [
"oncall: distributed",
"module: flaky-tests",
"skipped"
] | 1 | NONE | Platforms: linux
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_ddp_comm_hook_sparse_gradients&suite=DistributedDataParallelTest&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/36629187366).
Over the pa... | true |
2,829,608,019 | Input ignore not execute in `torch.addr()` | ILCSFNO | closed | [
"triaged",
"module: linear algebra",
"module: python frontend"
] | 6 | CONTRIBUTOR | ### 🐛 Describe the bug
The doc of [`torch.addr()`](https://pytorch.org/docs/stable/generated/torch.addr.html#torch-addr) shows its description as below:
https://github.com/pytorch/pytorch/blob/1c16cf70c37652dde7950ca174278b425af03611/torch/_torch_docs.py#L702-L703
It shows that when `beta` is set to 0, the `input` ... | true |
2,829,557,136 | [TEST][Sparse] Force CUTLASS backend in TestSparseSemiStructuredCUTLASS | Aidyn-A | closed | [
"module: sparse",
"module: cuda",
"module: tests",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | COLLABORATOR | We have noticed some discrepancy between the ways the `test_sparse_semi_structured.py` was called. And in some ways, the test falsely fails, because it was attempting to run on a wrong backend. All because `SparseSemiStructuredTensor._FORCE_CUTLASS = True` was never set in the setup of `TestSparseSemiStructuredCUTLASS`... | true |
2,829,459,447 | [2/N][cp][example] flex attention in context parallel (backward pass) | XilunWu | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor",
"module: context parallel"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146397
* #145896
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,829,244,133 | [MPSInductor] Implement `prod` reduction | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146396
* #146389
* #146380
Mostly reusing `sum` reduction logic
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @am... | true |
2,829,183,378 | [dynamo][builtin-skipfile-cleanup] Remove random | anijain2305 | closed | [
"Stale",
"module: dynamo",
"ciflow/inductor",
"keep-going"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146395
* #146339
* #146116
* #146322
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,829,116,864 | [cpp_builder] refactor to reduce libcudart_static logs | henrylhtsang | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 7 | CONTRIBUTOR | Want to reduce logs from `log_msg = f'"libcudart_static.a" not found under {path}'`, which was added in https://github.com/pytorch/pytorch/pull/142175
Differential Revision: D69096354
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipis... | true |
2,829,086,969 | PEP585: More fixes 2 | aorenste | closed | [
"oncall: distributed",
"oncall: jit",
"release notes: quantization",
"fx",
"ciflow/inductor",
"release notes: AO frontend"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146393
* #146392
* #146391
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @EikanWang @jgong5 @wenzhe-nrv @sanchitintel @ezyang @SherlockNoMad | true |
2,829,086,893 | PEP585: More UP006 fixes | aorenste | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: onnx",
"release notes: quantization",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | This should be the final PR before we can enable RUFF UP006.
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146392
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSupe... | true |
2,829,086,816 | PEP585: Add noqa to necessary tests | aorenste | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146391
| true |
2,829,072,165 | CPU-specific Inductor Error with `view` on `torch.nn.Embedding` output | cw-tan | closed | [
"triaged",
"oncall: pt2",
"oncall: cpu inductor"
] | 4 | NONE | ### 🐛 Describe the bug
The following minimal example runs with `device="cuda"` but fails with `device="cpu"` with latest torch 2.6. The error is specific to doing an operation on the `view` of the output of `torch.nn.Embedding` (error does not appear if we just do elementwise multiplication on the `torch.nn.Embedding... | true |
2,829,017,152 | [MPSInductor] Implement `min` and `max` reductions | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146396
* __->__ #146389
* #146380
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @desertfire @chauhang @aakhund... | true |
2,829,002,299 | [WIP][CUDA][cuDNN] Experimental `cudnn_rms_norm` | eqy | open | [
"module: cudnn",
"module: cuda",
"open source",
"module: norms and normalization",
"Stale",
"topic: not user facing"
] | 6 | COLLABORATOR | opt-in for now behind two new native functions---the plan would be to eventually add it as the `CUDA:` backend to `rms_norm`
Initial experiments show forward ~4-5x speed, up fwd+bwd ~3x speedup
cc @csarofeen @ptrblck @xwang233 @msaroufim | true |
2,828,995,143 | [ROCm] TopK optimizations for AMD GPUs | apakbin | closed | [
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"rocm",
"rocm priority",
"ciflow/rocm",
"ciflow/inductor-rocm"
] | 25 | CONTRIBUTOR | TopK performance on ROCm performs better on the test suite with the default config.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,828,987,917 | [ca] refactor compile reasons and log to tlparse | xmfan | closed | [
"Merged",
"ciflow/trunk",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo",
"module: compiled autograd"
] | 3 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146386
* #146229
This PR accumulates comple reasons inside each CacheNode, and logs them to tlparse on each CA compile. This defines a compile as an autograd structure change, and a recompile as a dynamic shape change.
sample tlp... | true |
2,828,974,284 | [WIP] Confirm XPU Regression | EikanWang | closed | [
"triaged",
"open source",
"topic: not user facing",
"ciflow/xpu"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146385
| true |
2,828,954,346 | [Experiment] Fix an unaligned memory access issue in mm_template | desertfire | closed | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor-rocm"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146384
Summary:
Fixes a corner case in the Triton MM template, where the dimension M (dynamic size) can be smaller than BLOCK_M (similarly for the N dimenstion) can trigger unaligned memory access error.
cc @voznesenskym @penguinwu... | true |
2,828,937,589 | Error about vscode c++ configuration libtorch | mikeallen39 | closed | [
"module: windows",
"module: cpp"
] | 1 | NONE | ### 🐛 Describe the bug
<pre>
{
"configurations": [
{
"name": "Win32",
"includePath": [
"${workspaceFolder}/**",
"D:/dependencies/libtorchcu116/libtorch/include",
"D:/dependencies/libtorchcu116/libtorch/include/torch/csrc/api/include"
... | true |
2,828,934,871 | [Metal][BE] Fix the arguments of `polygamma` | dcci | closed | [
"Merged",
"topic: not user facing",
"module: mps",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | MEMBER | In the public API, order comes before input, while here they're
reversed. Match for consistency (and make this less error prone).
cc @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf... | true |
2,828,933,966 | [dynamic shapes][real tensor tracing] propagate unbacked hint when creating mod replacement | pianpwk | closed | [
"Merged",
"ciflow/trunk",
"release notes: fx",
"fx",
"ciflow/inductor"
] | 10 | CONTRIBUTOR | Fixes data-dependent errors for 2 PT2I models in draft export
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,828,923,299 | [MPSInductor] Add support for `sum` reduction | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146396
* #146389
* __->__ #146380
- Add `threadgroup_sum` template to `c10/metal/reduction_utils.h` that so far uses barrier to compute the reductions
TODOs:
- Implement efficient reduction using cooperative functions such as `simd_sh... | true |
2,828,891,387 | Re-add stft option to align window for center = false | jackzhxng | closed | [
"Merged",
"ciflow/trunk",
"release notes: onnx",
"ciflow/slow"
] | 19 | CONTRIBUTOR | Skips advancing the fc window on https://github.com/pytorch/pytorch/pull/145437, since I just found that there were non-trivial efforts to do so a while ago that eventually was reverted: https://github.com/pytorch/pytorch/pull/73434
Works around the issue by keeping the stft sans center overload
| true |
2,828,887,519 | [aarch64] CUDA 12.8 aarch64 builds to nightly binaries | tinglvv | closed | [
"open source",
"Merged",
"ciflow/binaries",
"ciflow/trunk",
"topic: not user facing"
] | 9 | COLLABORATOR | https://github.com/pytorch/pytorch/issues/145570
Adding Cuda 12.8 and keeping 12.6 for the sbsa build, supported CUDA_ARCH: 9.0, 10.0, 12.0
Refactor the binaries matrix for cuda sbsa build. Previously cuda-aarch64 was hardcoded to cuda 12.6. Now reads 12.6 and 12.8, new build naming example [manywheel-py3_9-cuda-... | true |
2,828,873,926 | FlexAttention compiled backward gives garbage data in certain stride situations for K.grad | leijurv | closed | [
"high priority",
"triaged",
"module: correctness (silent)",
"oncall: pt2",
"module: dynamic shapes",
"module: inductor",
"module: flex attention"
] | 11 | NONE | ### 🐛 Describe the bug
```python
import torch
import torch.nn.attention.flex_attention
torch.set_default_device("cuda")
print(torch.__version__)
flex_compiled = torch.compile(torch.nn.attention.flex_attention.flex_attention)
for fix_issue in [False, True]:
for i in range(10):
torch.manual_seed(0)
shape = (1, 16,... | true |
2,828,872,624 | PyWork: preserve Python reference counting when used in functional collectives | d4l3k | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 27 | MEMBER | @fegin found an issue where torchft is not compatible with functional collectives.
Found in https://github.com/pytorch/torchtitan/pull/806
The root cause is because PyProcessGroup/PyWork are not compatible with functional collectives due to a nasty ownership bug.
PyWork relies on a pybind trampoline to pro... | true |
2,828,866,216 | [inductor] Remove SimplifyIndexing pass in codegen | jansel | closed | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146375
I'm not convinced this does anything since we simplify again later on.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng ... | true |
2,828,864,225 | [Dynamo] Fix spammy optimizer warning | mlazos | closed | [
"Merged",
"ciflow/trunk",
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo"
] | 6 | CONTRIBUTOR | Fixes https://discuss.pytorch.org/t/torch-compile-optimizer-step-generates-excessive-warning-messages/216067/7
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,828,861,684 | [inductor] Pre-populate cache for simplify_with_ranges return value | jansel | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 14 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146373
* #146297
* #146282
* #146257
* #146255
* #146254
* #146252
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8... | true |
2,828,853,898 | [Submodule] Turning flash-attention integration into 3rd party submod (#144120) | drisspg | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"skip-pr-sanity-checks",
"ciflow/inductor",
"suppress-bc-linter",
"ci-no-td",
"module: sdpa"
] | 29 | CONTRIBUTOR | Summary:
# Summary
### Sticky points
Cuda-graph rng handling has changed / deviated from original implementation. We will be left with a dangling 'offset' val and confusing naming due to BC
## Dependencies
- Flash PR: https://github.com/Dao-AILab/flash-attention/pull/1419
### Other Points
- The BC linter is compla... | true |
2,828,852,412 | Torchrun does not handle worker failure gracefully | shravan-achar | open | [
"oncall: distributed"
] | 1 | NONE | ### 🐛 Describe the bug
```
def train_func():
import os
import torch.distributed as dist
import time
import sys
dist.init_process_group(backend="nccl")
ws = dist.get_world_size()
rank = dist.get_rank()
endpoint = os.getenv("PET_RDZV_ENDPOINT")
print(f"WS: {ws}, RANK: {rank}")
... | true |
2,828,847,555 | [MPSInductor] Add support for any reduction | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146380
* __->__ #146370
* #146369
- Add `_new_accvar` function that creates a threadgroup variable
- As threadgroup variables can not be initialized in place, add explicit initialization for reduction var
cc @voznesenskym @pengui... | true |
2,828,847,470 | [MPSInductor] Prep change for reduction support | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146380
* #146370
* __->__ #146369
Add `group_pos` parameter as well as set `group_size` when invoking reduction kernels
Separates loads and stores and insert threadgroup barrier if reduction is in place
Should be a no-op right now
c... | true |
2,828,837,568 | DeepSeek: block quantization | ngimel | open | [
"oncall: quantization"
] | 2 | COLLABORATOR | DeepSeek is using 128x1 and 128x128 quantization. Currently _scaled_mm supports row-wise quantization (although for some sizes performance of `fast_accum=False` leaves a lot to be desired), but there's no support for 128x1 and 128x128. There's some work for block quantization support for mx format for blackwell, but it... | true |
2,828,819,891 | [dynamo] Initial support for `nonstrict_trace` | StrongerXi | closed | [
"Merged",
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147572
* #147571
* #146950
* __->__ #146367
* #146714
## Context
> **Note:** `mark_traceable` got renamed to `nonstrict_trace` after
> offline discussion. The reasons are (1) it aligns with `torch.export`'s
> `nonstrict` notion, and (... | true |
2,828,790,426 | [mps/inductor] Adjust more tests that expect float64 as input. | dcci | closed | [
"Merged",
"topic: not user facing",
"module: mps",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | MEMBER | cc @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @desertfire @chauhang @aakhundov | true |
2,828,779,624 | [RFC][LOGS] Add options to show cutlass logs | henrylhtsang | closed | [
"fb-exported",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 10 | CONTRIBUTOR | Summary:
Use `os.environ["TORCH_LOGS"] = "+cutlass"` to see CUTLASS backend logs.
For example,
```
cutlass-3/python/cutlass_library/manifest.py:731] [0/0] Culled cutlass_tensorop_i168256xorgemm_b1_256x64_1024x4_tn_align128 from manifest
...
cutlass_library/generator.py:58] [0/0] *** CreateConvOperator3x
cutlass_libr... | true |
2,828,735,413 | [DeviceMesh] Add some documentation for `from_group` API and add a 2D test | wz337 | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"module: dtensor",
"release notes: distributed (dtensor)"
] | 9 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wconstab @d4l3k @c-p-i-o @tianyu-l @XilunWu | true |
2,828,729,573 | print out partial fx graph for all data-dependent errors | bobrenjc93 | closed | [
"Merged",
"ciflow/trunk",
"release notes: fx",
"fx",
"module: dynamo",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146363
* #146296
* #146298
The previous implementation didn't catch the following type of errors
```
torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode: Could not extract specialized integer from data-dependent ex... | true |
2,828,715,688 | Simple tensor parallel example forces all inputs/outputs to be replicated | bdhirsh | closed | [
"oncall: distributed",
"module: dtensor"
] | 5 | CONTRIBUTOR | Creating a fresh issue from the comment [here](https://github.com/pytorch/pytorch/issues/108840#issuecomment-2631806300):
Running this repro and printing all inputs/outputs, they all appear to have `device_mesh=DeviceMesh('cuda', [0, 1]), placements=(Replicate(),))`. Is that expected?
```
import os
import torch
impor... | true |
2,828,699,083 | Jz/test old stft | jackzhxng | closed | [
"release notes: onnx",
"ciflow/slow"
] | 2 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
| true |
2,828,668,514 | Dynamo Unsupported: call_method UserDefinedObjectVariable(zip) __next__ [] {} | zou3519 | open | [
"triaged",
"oncall: pt2",
"module: dynamo",
"module: graph breaks"
] | 1 | CONTRIBUTOR | Repro:
```py
@torch.compile(backend="eager", fullgraph=True)
def f(x, z):
next(z)
return x.sin()
x = torch.randn(3)
z = zip([0, 1], [2, 3])
f(x, z)
```
cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @amjames | true |
2,828,668,457 | Dynamo Unsupported: call_method UserDefinedObjectVariable(zip) __iter__ () {} | zou3519 | open | [
"triaged",
"oncall: pt2",
"module: dynamo",
"module: graph breaks"
] | 0 | CONTRIBUTOR | Repro:
```py
@torch.compile(backend="eager", fullgraph=True)
def f(x, z):
iter(z)
return x.sin()
x = torch.randn(3)
z = zip([0, 1], [2, 3])
f(x, z)
```
cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @amjames | true |
2,828,660,091 | [ROCm][TunableOp] Support leading dimensions in TunableOp signature. | naromero77amd | closed | [
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/rocm"
] | 3 | COLLABORATOR | This is a feature enhancement that:
- May improve performance by distinguishing GEMMs with different leading dimensions.
- Fix correctness issues reported by users.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang | true |
2,828,648,030 | [Dynamo] Better unsupported message for Fake Tensor Exception | zou3519 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146357
I cannot repro this. But this line shows up in internal logs, and I want
to know what the exception is and the context inside it. All of the
exceptions_allowed_to_be_fallback are dataclasses, so they should print
nicely.
Test... | true |
2,828,633,280 | [cutlass backend] fix bug for accuminator dtype | henrylhtsang | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 12 | CONTRIBUTOR | Will add unit tests for accuracy.
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146743
* __->__ #146356
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames... | true |
2,828,629,889 | [dynamo] replace hardcoded eval frame control flags skip_code_recursive_flag/cache_limit_hit_flag | williamwen42 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 9 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146355
* #145603
This PR and the previous:
- Moves parts of `eval_frame.c` to C++.
- Reduces code duplication in `dynamo__custom_eval_frame` and makes the control flow more clear.
- Enables `convert_frame` to signal to `eval_fram... | true |
2,828,624,396 | Remove fp16 accumulation default from inductor cutlass backend | Chillee | closed | [
"module: inductor",
"ciflow/inductor"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146354
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @desertfire @chauhang @aakhundov | true |
2,828,593,875 | Fix assertion failure in gemm template lowering | dmpots | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 15 | CONTRIBUTOR | Summary:
This commit fixes a crash in the gemm template lowering caused by hitting an [assert](https://github.com/pytorch/pytorch/blob/fd515e4f59bfa0ac9faa5185b7a02f3222c4cd08/torch/_inductor/codegen/common.py#L1181) that a buffer was previously removed.
The assert triggers because in the first gemm lowering we use... | true |
2,828,551,389 | Build a storage reader/writer to write checkpoints in HF format | ankitageorge | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: new features",
"topic: not user facing",
"ci-no-td"
] | 18 | CONTRIBUTOR | Summary: Title - we want to write checkpoints in HF format with DCP, this diff allows this for the non-distributed use case.
Test Plan:
buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/distributed/checkpoint:test_hf_torchtune_storage
N6476188 --> able to save and load tensor in hf format
Differential R... | true |
2,828,539,503 | [export] Fix requires_grad deserialization | angelayi | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: export"
] | 6 | CONTRIBUTOR | Test Plan: CI
Differential Revision: D69072095
| true |
2,828,529,070 | Dynamo Unsupported: call_method BuiltinVariable(str) isalnum [LazyVariableTracker()] {} | zou3519 | closed | [
"triaged",
"oncall: pt2",
"module: dynamo",
"module: graph breaks"
] | 0 | CONTRIBUTOR | Repro:
```py
@torch.compile(backend="eager", fullgraph=True)
def f(x, c):
str.isalnum(c)
return x.sin()
x = torch.randn(3)
f(x, "foobar")
```
Kinda weird, but OK. Should also just support all the str methods while we're at it.
cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSup... | true |
2,828,526,138 | Dynamo Unsupported: call_method UserDefinedObjectVariable(dict_itemiterator) __next__ [] {} | zou3519 | open | [
"triaged",
"oncall: pt2",
"module: dynamo",
"module: graph breaks"
] | 0 | CONTRIBUTOR | Repro:
```py
@torch.compile(backend="eager", fullgraph=True)
def f(x, it):
next(it)
return x.sin()
x = torch.randn(3)
dct = {'a': 3, 'b': 3}
f(x, iter(dct.items()))
```
cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 ... | true |
2,828,520,472 | Dynamo Unsupported call_method UserDefinedObjectVariable(generator) __iter__ () {} | zou3519 | open | [
"triaged",
"oncall: pt2",
"module: dynamo",
"module: graph breaks"
] | 0 | CONTRIBUTOR | Repro:
```py
@torch.compile(backend="eager", fullgraph=True)
def f(x, it):
iter(it)
return x.sin()
def get_gen(i):
for i in range(10):
yield i
x = torch.randn(3)
gen = get_gen(10)
f(x, gen)
```
Not clear to me how supportable this is. `__next__` is also an issue.
cc @chauhang @penguinwu @voznesen... | true |
2,828,514,678 | Dynamo Unsupported call_method UserDefinedObjectVariable(enumerate) __iter__ () {} | zou3519 | open | [
"triaged",
"oncall: pt2",
"module: dynamo",
"module: graph breaks"
] | 0 | CONTRIBUTOR | Repro:
```py
@torch.compile(backend="eager", fullgraph=True)
def f(x, it):
iter(it)
return x.sin()
x = torch.randn(3)
f(x, enumerate(range(0, 3)))
```
cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @amjames | true |
2,828,511,553 | Dynamo Unsupported: call_method UserDefinedObjectVariable(enumerate) __next__ [] {} | zou3519 | open | [
"triaged",
"oncall: pt2",
"module: m1",
"module: dynamo",
"module: graph breaks"
] | 0 | CONTRIBUTOR | Repro:
```py
@torch.compile(backend="eager", fullgraph=True)
def f(x, it):
next(it)
return x.sin() * c['a']
x = torch.randn(3)
f(x, enumerate(range(0, 3)))
```
cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @... | true |
2,828,505,302 | Only call triton in worker process, ahead of time compile | jamesjwu | closed | [
"fb-exported",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Summary:
### Big idea
This PR extends https://github.com/pytorch/pytorch/pull/144288 by combining calling triton in worker processes with the future cache: we kick off triton compilation in the worker processes earlier, during inductor codegen. Basically instead of calling async_compile.triton for the first time only... | true |
2,828,504,634 | Dynamo Unsupported: call_method UserDefinedObjectVariable(dict_valueiterator) __next__ [] {} | zou3519 | open | [
"triaged",
"oncall: pt2",
"module: dynamo",
"module: graph breaks"
] | 0 | CONTRIBUTOR | Repro:
```py
@torch.compile(backend="eager", fullgraph=True)
def f(x, it):
next(it)
return x.sin()
x = torch.randn(3)
dct = {"a": 3, "b": 3}
f(x, iter(dct.values()))
```
cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78... | true |
2,828,501,499 | Only call triton in worker process, ahead of time compile | jamesjwu | closed | [
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* (to be filled)
# Big idea
This PR extends https://github.com/pytorch/pytorch/pull/144288 by combining calling triton in worker processes with the future cache: we kick off triton compilation in the worker processes earlier, during inductor c... | true |
2,828,494,844 | Only call triton in worker process; run async_compile.triton ahead of time in Scheduler.codegen | jamesjwu | closed | [
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* (to be filled)
### Big idea
This PR extends https://github.com/pytorch/pytorch/pull/144288 by combining calling triton in worker processes with the future cache: we kick off triton compilation in the worker processes earlier, during inductor... | true |
2,828,492,191 | Only call triton in worker process; run async_compile.triton ahead of time in Scheduler.codegen | jamesjwu | closed | [
"Stale",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* (to be filled)
### Big idea
This PR extends https://github.com/pytorch/pytorch/pull/144288 by combining calling triton in worker processes with the future cache: we kick off triton compilation in the worker processes earlier, during inductor... | true |
2,828,490,066 | Dynamo graph break on `call_method UserDefinedObjectVariable(list_iterator) __next__ [] {}` | zou3519 | open | [
"triaged",
"oncall: pt2",
"module: dynamo",
"module: graph breaks"
] | 0 | CONTRIBUTOR | Repro:
```py
@torch.compile(backend="eager", fullgraph=True)
def f(x, it):
next(it)
return x.sin()
x = torch.randn(3)
it = iter([1, 2, 3])
f(x, it)
```
cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @amjames | true |
2,828,458,016 | [dynamo] Support functools.partial variables through inspect.signature | anijain2305 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146339
* #146116
* #146322
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,828,452,937 | __pow__ operator on cfloat z=0 on mps produces `nan` | jcampbell | open | [
"triaged",
"module: complex",
"module: correctness (silent)",
"module: mps"
] | 4 | NONE | ### 🐛 Describe the bug
Using the `**` operator on a complex dtype that has value zero returns nan when using the `mps` device
```
import torch
device = torch.device("mps")
t = torch.tensor(0 + 0j, dtype=torch.cfloat).to(device) # only the scalar zero appears to cause this issue
t = t ** 2
print(t)
```
Returns:
``... | true |
2,828,449,213 | Honor Dr.CI classification results on auto commit hash update | huydhn | closed | [
"Merged",
"topic: not user facing",
"test-config/default"
] | 3 | CONTRIBUTOR | Disable `ignore_flaky_failures` was a safer choice, but it seems that this option doesn't work with the current state of the CI. For example, https://github.com/pytorch/pytorch/pull/125806 hasn't been merged since May because there would always be a failure in one type or another. This effectively disables the automa... | true |
2,828,405,077 | [ONNX] Fix torchlib function errors | justinchuby | open | [
"module: onnx",
"triaged"
] | 4 | COLLABORATOR | Tracking issue for new function errors from the torchlib migration.
- [x] unflatten (https://github.com/microsoft/onnxscript/pull/2070)
- [ ] embedding bag
- [ ] as_strided
- [x] unfold (https://github.com/microsoft/onnxscript/pull/2067) | true |
2,828,398,757 | [WIP][dynamic shapes] mark backed size symbols as size-like | pianpwk | open | [
"Stale",
"ciflow/trunk",
"release notes: fx",
"fx",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | experimental, to apply upper-bound / maxsize size-oblivious semantics to backed symbols
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv @voznesenskym @penguinwu @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,828,392,136 | Only call triton in worker process, ahead of time compile | jamesjwu | closed | [
"fb-exported",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146334
This PR extends https://github.com/pytorch/pytorch/pull/144288 by combining calling triton in worker processes with the future cache: we kick off triton compilation in the worker processes earlier, during inductor codegen. Bas... | true |
2,828,383,088 | Add optional generator to distribution sampler/rsample methods. | vladoovtcharov | open | [
"module: distributions",
"triaged",
"open source",
"topic: not user facing"
] | 5 | NONE | Fixes part of #45115 and #11340
Adds a generator parameter to all the sample/rsample methods of torch distribution classes
cc @fritzo @neerajprad @alicanb @nikitaved | true |
2,828,378,673 | DeepSeek: fine-grained overlap | ngimel | open | [
"oncall: distributed"
] | 3 | COLLABORATOR | DeepSeek implements fine-grain concurrency using computation of one batch to hide communication of another (see picture). Currently we have no convenient mechanism to express this kind of parallelism, given that model code usually specifies the series of computations and communications for a single microbatch and puts ... | true |
2,828,377,716 | DeepSeek: hierarchical a2a | ngimel | open | [
"oncall: distributed"
] | 0 | COLLABORATOR | Deepseek implements limited-node hierarchical routing to reduce cross-node traffic, where each token is sent to a preset number of nodes. The routing is done in 2 pipelined stages - first token is sent to peer GPU on a node, and then that GPU routes it to correct GPUs within a node in the dispatch stage. For DeepSeek p... | true |
2,828,376,655 | DeepSeek: MLA attention | ngimel | open | [
"triaged",
"module: sdpa"
] | 6 | COLLABORATOR | DeepSeek uses MLA attention which currently doesn't have efficient implementation in pytorch. cc @drisspg | true |
2,828,375,627 | DeepSeek: a2a communication with metadata on the GPU | ngimel | open | [
"oncall: distributed"
] | 4 | COLLABORATOR | For e2e routing data is computed on the GPU and to avoid CPU synchronization the all2all op itself should be implemented in such a way that if would read the necessary metadata from the GPU. The wrinkle here is that with splits data on the GPU we won't know what size output we need to allocate, the typical way to get a... | true |
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