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 |
|---|---|---|---|---|---|---|---|---|
2,866,854,688 | [ONNX] Add draft_export as a strategy | justinchuby | closed | [
"module: onnx",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: onnx",
"topic: new features"
] | 14 | COLLABORATOR | Create draft_export strategy.
The strategy is added before jit and after strict=True, as the third fallback. Since it is specializing tensors it should not be less robust than the jit trace strategy.
| true |
2,866,841,603 | Package API for torch.compile | zhxchen17 | open | [
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"release notes: AO frontend"
] | 5 | CONTRIBUTOR |
Package API for torch.compile
Following up PR https://github.com/pytorch/pytorch/pull/145381, we implement
a new API for compiling models using the cpp wrapper, and save/load
compiled artifacts to disk.
Package is now designed to be a per-torch.compile() object living with
the compilation context. Each time ... | true |
2,866,757,583 | [ROCm] Input vectorization in elementwise kernels for tensors with heterogeneous types | carlobertolli | closed | [
"module: rocm",
"triaged",
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: rocm",
"rocm",
"ciflow/rocm",
"ci-no-td"
] | 14 | CONTRIBUTOR | This patch exemplifies its use for input tensors with types (float,bfloat16) when functor type is float(float,float).
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd @gujinghui @PenghuiCheng @XiaobingSuper @jianyuh @jgong5 @mingfeima @sanchitinte... | true |
2,866,682,656 | [MPS] faster integer matmul for mps | Isalia20 | closed | [
"open source",
"Merged",
"topic: improvements",
"release notes: mps",
"ciflow/mps"
] | 4 | COLLABORATOR | There is a naive matmul kernel written for MPS matmul which is used when input types are integer(and some other cases for older MacOSes). The old version of matmul is naive with global memory accesses which really tanks the performance especially when matrix is sufficiently large.
This PR optimizes it (even though ... | true |
2,866,469,409 | Raise KeyError when class attr is overwritten by a Module | vmoens | open | [
"Stale",
"release notes: nn"
] | 3 | CONTRIBUTOR | Currently, this code doesn't raise an exception though the result of the `setattr` is erroneous
```python
import torch
from torch.nn import Module
from torch.nn.parameter import Parameter
class MyModule(Module):
class_attr = "some value"
c = MyModule()
print(c.class_attr) # prints "some value"
c.... | true |
2,866,403,489 | Update pybind11 submodule to 3.0.0-dev test | Skylion007 | open | [
"open source",
"Stale",
"topic: not user facing"
] | 2 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,866,352,594 | [Inductor] optimize the heuristics of outer loop fusion | jiayisunx | closed | [
"open source",
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 7 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144020
* __->__ #147523
Summary:
Optimize the heuristics of outer loop fusion: When the range of the first inner loop is much larger than the range of all outer loops, do not fuse the outer loops and fallback to standard codegen.
c... | true |
2,866,228,726 | Fix the shape check inside gnll loss | KohakuBlueleaf | open | [
"module: nn",
"triaged",
"open source",
"topic: not user facing",
"bug"
] | 5 | NONE | Fixes #147521
This modification allow user to put any size of var in GaussianNLLLoss if the var is broadcastable (to input/target's size)
Therefore, the demo code in #147521 will result in expected behaviour and correct output.
This allow all input size that match:
`input.size = (..., n, ...), var.size = (..., ... | true |
2,866,222,575 | Unexpected incorrect size error in GaussianNLLLoss | KohakuBlueleaf | open | [
"module: nn",
"module: loss",
"triaged"
] | 0 | NONE | ### 🐛 Describe the bug
When using the `nn.GaussianNLLLoss`, we allow `var` to have one dimension to be 1 and other size match. But based on the behavior and source code. We actually only allow the "final dimension" to be one.
```python
import torch
import torch.nn as nn
loss = nn.GaussianNLLLoss()
test_inp = torch.... | true |
2,866,155,044 | L-BFGS-B | MicheleBellomo | closed | [
"module: optimizer",
"triaged"
] | 2 | NONE | ### 🚀 The feature, motivation and pitch
Good morning everyone, I would like to know if there is any interest in implementing the L-BFGS-B algorithm in PyTorch. Currently, PyTorch provides an implementation of L-BFGS, but without support for box constraints.
There have been some independent attempts to develop L-BFGS... | true |
2,865,947,296 | [Inductor] Fix the decompositions of torch isin | leslie-fang-intel | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147519
**Summary**
Fixed two decomposition issues in `torch.isin`:
- Issue 1: As reported in [#147329](https://github.com/pytorch/pytorch/issues/147329), the current decomposition does not support cases where test_element is a s... | true |
2,865,945,462 | Enable FSDP tests on XPU device | zhangxiaoli73 | closed | [
"oncall: distributed",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: distributed (fsdp)"
] | 11 | CONTRIBUTOR | **Motivation:**
Enable FSDP tests on XPU device
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @gujinghui @guangyey | true |
2,865,655,105 | [dtensor][cp] experiment: register flex_attention to a custom fn on DTensor within a custom dispatch mode | XilunWu | open | [
"oncall: distributed",
"topic: not user facing"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147603
* __->__ #147517
* #147516
* #147515
* #147514
* #145353
### Summary
Successfully dispatch `flex_attention` within a given context to the custom CP `flex_attention`.
```
Traceback (most recent call last):
File "/data/users/... | true |
2,865,654,819 | [dtensor][cp] experiment: register flex_attention to a custom fn within a custom dispatch mode | XilunWu | open | [
"oncall: distributed",
"topic: not user facing"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147603
* #147517
* __->__ #147516
* #147515
* #147514
* #145353
### Summary
Attempt to dispatch `flex_attention` within a given context to the custom CP `flex_attention`. Got the below error:
```
Traceback (most recent call last):
Fi... | true |
2,865,654,591 | [dtensor][cp] experiment: register flex_attention to a custom fn on DTensor | XilunWu | open | [
"oncall: distributed",
"topic: not user facing"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147603
* #147517
* #147516
* __->__ #147515
* #147514
* #145353
### Summary
Attempt to dispatch flex_attention on DTensor to a custom CP flex_attention function but got the error below. This error should be identical to #146994 .
```
... | true |
2,865,654,394 | [DTensor] add aten.as_strided.default op | XilunWu | open | [
"oncall: distributed",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147603
* #147517
* #147516
* #147515
* __->__ #147514
* #145353
###Summary
This PR solves the FakeTensor propagation error encountered in #145353 but actually this is not needed for CP flex_attention. #147517 shows a way to avoid flex_att... | true |
2,865,576,831 | [RFC] Request for Feedback and Review on PRs Adding RISC-V and RVV Support | zhangfeiv0 | open | [
"module: build",
"module: cpu",
"triaged",
"enhancement",
"module: risc-v"
] | 5 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
# Motivation
I am writing to provide an update and request feedback regarding the progress of two PRs that I have submitted, which aim to enhance PyTorch's support for RISC-V architecture and the RVV (RISC-V Vector Extension).
# Current Progress
As mentioned in #129553, we h... | true |
2,865,534,762 | Enable ASAN in CUDA tests | cyyever | closed | [
"oncall: distributed",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/periodic",
"ciflow/slow"
] | 3 | COLLABORATOR | It should work.
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,865,504,347 | Enable UBSAN test | cyyever | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: quantization",
"topic: not user facing"
] | 6 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,865,428,403 | `clamp_` and `clamp` behave differently on MPS device. | ornew | open | [
"triaged",
"module: correctness (silent)",
"module: mps"
] | 4 | NONE | ### 🐛 Describe the bug
On the MPS device, `clamp_` and `clamp` operations on tensors produce inconsistent results, unlike on the CPU device where they behave as expected. Specifically, `clamp_` appears to not correctly modify the tensor in-place on MPS, leading to unexpected values in the output tensor. This issue h... | true |
2,865,425,111 | Open pynvml related test in test_cuda.py | cdzhan | closed | [
"open source",
"topic: not user facing"
] | 1 | CONTRIBUTOR | As stated in the title. | true |
2,865,412,743 | Precision drop after exporting PyTorch.sigmoid to ONNX.sigmoid | blingbling22 | closed | [
"module: onnx",
"triaged"
] | 3 | NONE | ### 🐛 Describe the bug
I have noticed a significant precision drop when exporting a PyTorch.sigmoid model to the ONNX format. The model performs well in PyTorch, but after converting it to ONNX and running inference, the output accuracy or results are not the same.
result of sigmoid([[1.0, -17.0, -20.0]]))
onnx ou... | true |
2,865,324,971 | Document poison fork note for accelerator APIs | guangyey | closed | [
"oncall: distributed",
"open source",
"Merged",
"ciflow/trunk",
"release notes: distributed (rpc)",
"topic: not user facing",
"ciflow/xpu",
"module: accelerator"
] | 14 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149924
* __->__ #147507
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @albanD @EikanWang | true |
2,865,318,220 | Unintuitive behavior and errors in F.pad | FabianIsensee | open | [
"module: nn",
"triaged",
"module: padding"
] | 0 | NONE | ### 🐛 Describe the bug
When padding arrays with F.pad I consistently run into errors that seem unintuitive. F.pad seems to expect batch and channel dimensions even though this is not documented:
https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
```python
import torch
import torch.nn.functional a... | true |
2,865,116,576 | [export] Enforce ordering for graph inputs of ExportedProgram | yiming0416 | closed | [
"fb-exported",
"ciflow/inductor",
"release notes: export"
] | 14 | CONTRIBUTOR | Summary:
As title. The graph inputs of have the following order:
token -> parameter -> buffer (persistent) -> buffer (non-persistent) -> tensor_constant -> custom_obj -> user_inputs
Verifier is also updated to check this order.
Test Plan: buck2 run @mode/dev-nosan caffe2/test:test_export -- -r test_enforcing_... | true |
2,865,076,554 | [Inductor][ROCm][CK] Unhardedcoded kernel shapes for ck_conv_template codegen | AviralGoelAMD | closed | [
"module: rocm",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 7 | CONTRIBUTOR | ## [Inductor][ROCm][CK] Parameterize `ck_conv_template` Codegen
### Description
Previously, ROCm CK kernel codegen templates were hardcoded with fixed values for convolution parameters:
- `index_t GroupCount`
- `index_t NBatch`
- `index_t NOutChannels`
- `index_t NInChannels`
- `vector<index_t>... | true |
2,865,051,770 | [Inductor][ROCm][CK] Parameterize ck_conv_template Codegen | AviralGoelAMD | closed | [
"module: rocm",
"topic: not user facing",
"module: inductor"
] | 3 | CONTRIBUTOR | ### Description
Previously, ROCm CK kernel codegen templates were hardcoded with fixed values for convolution parameters:
- index_t GroupCount
- index_t NBatch
- index_t NOutChannels
- index_t NInChannels
- vector<index_t> FilterSize
- vector<index_t> InputSize
- vector<index_t> ConvolutionStrides
- vector<i... | true |
2,865,003,743 | Slow first time torch.distributed op with ray | grimoire | open | [
"oncall: distributed",
"triaged",
"module: c10d"
] | 0 | NONE | ### 🐛 Describe the bug
I am working on deploying a distributed model with [ray](https://github.com/ray-project/ray). Everything is good before I change `CUDA_VISIBLE_DEVICES`. First time `all_reduce` take longer time (30s+).
### Reproduction script
`ray_dist.py`
```python
import os
import time
import ray
import to... | true |
2,864,996,744 | removed zero dim cpu logic from fake_tensor.py | zero000064 | open | [
"oncall: distributed",
"module: cpu",
"triaged",
"open source",
"module: amp (automated mixed precision)",
"NNC",
"release notes: quantization",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"release notes: distributed (checkpoint)",
"module: compiled autograd"
] | 29 | CONTRIBUTOR | Fixes #144748
In #144748, the inconsistency between the eager mode and the inductor mode is reported as a bug.
The root cause is fake_tenosr.py's find-common-device method, https://github.com/pytorch/pytorch/blob/0b0da81021e061c021e515bc35d7dc0dbbb05941/torch/_subclasses/fake_tensor.py#L833, takes zero dim cpu tensor... | true |
2,864,995,945 | Support serialization for uintx/intx in weights_only | jerryzh168 | closed | [
"Merged",
"ciflow/trunk",
"release notes: python_frontend"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147500
Summary:
Fixing the issue reported by huggingface
Test Plan:
python test/test_serialization.py -k test_serialization_uintx_intx
Reviewers:
Subscribers:
Tasks:
Tags: | true |
2,864,984,457 | Optimize `dynamo` typing | zeshengzong | open | [
"triaged",
"open source",
"topic: not user facing",
"module: dynamo"
] | 7 | CONTRIBUTOR | Optimize dynamo methods type annotation.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,864,948,051 | Upgrade submodule oneDNN to v3.7 | yanbing-j | closed | [
"module: mkldnn",
"open source",
"module: arm",
"Merged",
"Reverted",
"Stale",
"ciflow/binaries",
"ciflow/trunk",
"topic: not user facing",
"intel",
"module: inductor",
"ciflow/inductor",
"ciflow/xpu",
"ci-no-td",
"ciflow/linux-aarch64"
] | 23 | COLLABORATOR | This PR is to upgrade submodule oneDNN to v3.7.
## Improvements
- Improved performance of convolution and matmul primitives on Intel Xeon processors with Intel AMX instruction set support (formerly Sapphire Rapids and Granite Rapids).
- Improved performance of int8 and fp32 forward convolution primitive on proce... | true |
2,864,936,952 | [PrivateUse1] Improve error message after we deprecated the `REGISTER_GENERATOR_PRIVATEUSE1` micro | shink | closed | [
"open source",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
cc: @albanD @FFFrog | true |
2,864,849,943 | [codemod] Fix unused-value issue in caffe2/aten/src/ATen/native/miopen/Conv_miopen.cpp +1 | r-barnes | closed | [
"module: cpp",
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: improvements",
"topic: not user facing"
] | 4 | CONTRIBUTOR | Summary:
LLVM has a warning `-Wunused-value` which we treat as an error because it's so often diagnostic of a code issue. Unused values often indicate a programming mistake, but can also just be unnecessary cruft that harms readability and performance.
For questions/comments, contact r-barnes.
- If you approve of th... | true |
2,864,814,515 | Native Node Sleep/Wake Functionality for Neural Network Layers | MikeyBeez | open | [
"module: nn",
"triaged"
] | 0 | NONE | ### 🚀 The feature, motivation and pitch
# Feature Request: Native Node Sleep/Wake Functionality for Neural Network Layers
## Overview
This feature request proposes adding native functionality to temporarily deactivate ("sleep") and reactivate ("wake") specific nodes and weights within neural network layers. This cap... | true |
2,864,777,665 | [dynamo] more better error messages [3/N] | williamwen42 | closed | [
"Merged",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"module: compile ux"
] | 6 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147912
* #147872
* __->__ #147494
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,864,742,886 | [CUDA] Replace deprecated usages of cub iterators and thread operators | xwang233 | open | [
"module: cuda",
"triaged",
"open source",
"topic: not user facing"
] | 8 | COLLABORATOR | Several cub iterators have been deprecated and removed in the latest CCCL (cub) development https://github.com/NVIDIA/cccl/pull/3831. This PR replaced the usage of those cub iterators with thrust iterators.
Some cub thread operators were also deprecated and removed in https://github.com/NVIDIA/cccl/pull/3918. This P... | true |
2,864,742,437 | Fix RuntimeError: value cannot be converted to type int64_t without overflow | drisspg | 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):
* __->__ #147492
The exact call is coming from here:
https://github.com/pytorch/pytorch/blob/78a94c911435bf9b1bb45888033a29081e406ec2/torch/_inductor/memory.py#L161
I have no idea why this error is being thrown and what mode/modes mig... | true |
2,864,739,495 | Fix pxtas warnings on sm_120 | sclarkson | open | [
"triaged",
"open source",
"Stale",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Per the feature specification table on the CUDA C programming guide, sm_120 also has maximum of 1536 threads per SM. This silences many ptxas warnings during build.
See Table 23 here: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#features-and-technical-specifications-technical-specifications-per-c... | true |
2,864,737,450 | Documentation: fix RNN example for multiple layers | jibril-b-coulibaly | open | [
"triaged",
"open source",
"Stale"
] | 4 | NONE |
Hi,
The documentation on RNN provides the following snippet of code that I think is incorrect.
```
# Efficient implementation equivalent to the following with bidirectional=False
def forward(x, hx=None):
if batch_first:
x = x.transpose(0, 1)
seq_len, batch_size, _ = x.size()
if hx is Non... | true |
2,864,658,922 | type `fully_shard` so that the return value can be chained with typing enabled | xunnanxu | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (fsdp)",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147489
* #147488
This allows for
```
fsdped = fully_shard(model)
fsdped.set_xyz()
```
same applies if `model` is actually a list of modules
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o
Dif... | true |
2,864,658,844 | capture the return value in the contract typing | xunnanxu | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (composable)"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147489
* __->__ #147488
----
* the existing typing makes the return type `Optional[nn.Module]`
* this doesn't seem to be what the decorator actually does as it does
not alter the original return type
* This PR aims to fix the typing
cc ... | true |
2,864,603,987 | [CI] Build sm89 with more procs experiment | clee2000 | closed | [
"Merged",
"release notes: releng"
] | 7 | CONTRIBUTOR | Add a build that uses 4 out of the 8 processes available on a linux.2xlarge/c5.2xlarge. Currently it's set to 2 because it would oom, but I'm curious as to how often people's builds oom. I can't test this on my own because of caching, so it has to run on pull request
This might result in a failing job on may peopl... | true |
2,864,590,596 | remote_cache.py loggings cause a logging error after pytest exit | henrylhtsang | open | [
"module: logging",
"triaged",
"module: testing",
"oncall: pt2"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
Basically, it seems like pytest cannot expect loggings after it closes, but remote_cache prints is using decorator `atexit.register`
repro (test expects A100, but you can comment that out):
```
TORCH_LOGS="inductor" pytest test/inductor/test_cutlass_backend.py -k test_get_max_alignment
```
e... | true |
2,864,568,050 | [cutlass backend] Add main tests for mm, addmm, bmm | henrylhtsang | closed | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147485
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,864,551,068 | [CI] Do not overwrite return code of test file when fails for rerun disabled tests | clee2000 | closed | [
"Merged",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Do not overwrite the return code of a single file when it fails. This will allow the log to be printed to stdout and the gha logs
| true |
2,864,536,391 | Can we have Dim.AUTO/Dim.DYNAMIC with an optional min & max? | ColinPeppler | open | [
"oncall: pt2",
"export-triaged",
"oncall: export"
] | 1 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
**What?**
- `Dim.AUTO` is the dynamic shapes feature that automatically handles dynamic dim relations and can specialize.
- `Dim.DYNAMIC` is the same as `Dim.AUTO` but can't specialize IIUC.
- The `Dim` API allows you to specify symbol name, min and max.
- Can we add this min &... | true |
2,864,520,089 | [experimental] delayed compile | bobrenjc93 | closed | [
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147482
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,864,519,390 | [dynamo][guard] Guard on the cuda device index | anijain2305 | open | [
"Stale",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147481
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,864,490,079 | [PT2]: allow empty dict to pass type check (#147167) | kqfu | closed | [
"oncall: jit",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: jit"
] | 7 | CONTRIBUTOR | Summary:
Seeing errors like when testing sigmoid for inline_cvr and perevent_cvr models.
```
terminate called after throwing an instance of 'c10::Error'
what(): forward() Expected a value of type 'Dict[int, Tuple[Tensor, Tensor, Tensor]]' for argument 'event_based_features' but instead found type 'Dict[Any, Any]'.
... | true |
2,864,435,802 | [RFC] Deprecate silent fallback to aten logic in Inductor | henrylhtsang | open | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 5 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
The proposal is suggested by @eellison. See more context in https://github.com/pytorch/pytorch/pull/147148
**tldr**
This proposal isn’t going to affect the majority of users. You only need to worry about it if **both** of the following are true:
You set your custom max_autotun... | true |
2,864,383,871 | Blackwell Only Bugs | drisspg | open | [
"module: cuda",
"module: tests",
"triaged",
"oncall: pt2",
"module: inductor",
"oncall: export",
"Blackwell"
] | 1 | CONTRIBUTOR | # Blackwell specific failures
1. Out-of-range shared or local address on blackwell for second MM in flex-attention | full log: https://www.internalfb.com/intern/paste/P1749623099/ + repro: https://gist.github.com/drisspg/807e3e4bfbfbc76f85bde154e00850c6
3. https://github.com/pytorch/ao/issues/1799
cc @ptrblck @msaro... | true |
2,864,345,798 | [Cutlass] Add test verifying number of precompiles | mlazos | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | As title
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,864,289,782 | Remove mention of magma-cuda in readme.md, refactor magma_conda install | atalman | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 9 | CONTRIBUTOR | Related to: https://github.com/pytorch/pytorch/issues/138506 we migrated magma-cuda build from anaconda to aws
Last version of magma-cuda published was 12.6 https://anaconda.org/pytorch/magma-cuda126
Here is the PR that moved from anaconda to tarball: https://github.com/pytorch/pytorch/pull/140417
cc @malfet @a... | true |
2,864,284,422 | [Export AOTI] dynamic_shapes export and compile degraded output | bhack | open | [
"triaged",
"oncall: pt2",
"export-triaged",
"oncall: export",
"module: aotinductor"
] | 22 | CONTRIBUTOR | ### 🐛 Describe the bug
I cannot share the code as it is a whole model export and compile.
With exactly the same code exporting W and H at fixed resolution is going to give the correct output.
When instead I am going to use dynamic W and H in a given range
```python
h_dim = torch.export.Dim("H", min=min_h, max=max_h... | true |
2,864,268,296 | [cutlass backend] enable mixed mm test (cutlass2x) for H100 | henrylhtsang | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 8 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147474
I am okay with not landing this as well. The motivation is to make developing on H100 smoother.
The reason the current test works on A100 but not H100 is because of alignment issue. Which was caused by arch specific filt... | true |
2,864,258,678 | [ROCm] Update inductor-periodic.yml to use the correct label | amdfaa | closed | [
"module: rocm",
"open source",
"Merged",
"topic: not user facing",
"ciflow/inductor-periodic"
] | 3 | CONTRIBUTOR | cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,864,241,709 | [ONNX] Implement sym_not | justinchuby | closed | [
"module: onnx",
"open source",
"ciflow/trunk",
"release notes: onnx",
"topic: improvements"
] | 7 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147472
* #147469
Fix https://github.com/pytorch/pytorch/issues/136572
| true |
2,864,237,527 | Add type hints to cuda kernel | mlazos | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Missed this in a previous PR
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,864,195,740 | [test] sccache log | clee2000 | open | [
"ciflow/trunk",
"release notes: releng",
"topic: not user facing"
] | 1 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
| true |
2,864,185,350 | [ONNX] Migrate onnx ops decomp functions | justinchuby | open | [
"module: onnx",
"open source",
"release notes: onnx",
"topic: new features"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147472
* __->__ #147469
This is the main PR that adds the onnx decomp functions from onnxscript to pytorch to decouple torch.onnx from implementations in onnxscript. Details of this migration, including how the logic is tested are descr... | true |
2,864,159,828 | [Triton] [Upstream] FlexDecoding Test Failures opToStageAndCluster[&op].first < numStages && "Op with invalid stage! | drisspg | closed | [
"triaged",
"oncall: pt2",
"upstream triton",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention"
] | 6 | CONTRIBUTOR | # Summary
Setup: checkout and install Triton: f73cf3268ef04d862493e0fc1cca5257f2a09346
Checkout recent main of PyTorch
Script to reproducer
```py
"""
Standalone test file for max_autotune with captured buffers.
This file brings together the test along with all supporting functions,
decorators, and helper classes so ... | true |
2,864,067,105 | not for land: just testing | vkuzo | closed | [
"release notes: quantization",
"topic: not user facing"
] | 1 | CONTRIBUTOR | Summary:
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Fixes #ISSUE_NUMBER
| true |
2,864,050,383 | add the `torch.float8_e8m0fnu` dtype to PyTorch | vkuzo | closed | [
"module: cpu",
"Merged",
"ciflow/trunk",
"release notes: quantization"
] | 8 | CONTRIBUTOR | Summary:
Continuing the work from https://github.com/pytorch/pytorch/pull/146427
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 ... | true |
2,864,046,480 | TCPStore: soft fail bind when agent store active | d4l3k | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 3 | MEMBER | This makes it easier to roll out `TORCHELASTIC_USE_AGENT_STORE` by opportunistically swallowing bind errors when the agent store is enabled and the port matches `MASTER_PORT`.
This should be very safe as if the store is somehow not up and the envs are set, the TCPStore client connections will fail to connect so we e... | true |
2,864,026,073 | Add current cuda device index to FXGraphCache key | jamesjwu | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147464
This PR intends to fix the cache related issues from https://github.com/pytorch/pytorch/issues/147405.
It does *not* handle the dynamo recompile case in process, because it does not introduce any extra guards. For FXGraphCa... | true |
2,863,941,403 | Performance Regression nightly 2025/02/08→02/09, on nanogpt speedrun | YouJiacheng | closed | [
"triaged",
"oncall: pt2",
"module: inductor",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention"
] | 17 | CONTRIBUTOR | ### 🐛 Describe the bug
On 3.28 track: torch-2.7.0.dev20250209 is 2 seconds slower than torch-2.7.0.dev20250208 .
On 2.92 track:
02/08: ~1470s
02/09: ~1483s
### Versions
Collecting environment information...
PyTorch version: 2.7.0.dev20250209+cu126
Is debug build: False
CUDA used to build PyTorch: 12.6
ROCM used to ... | true |
2,863,800,946 | add the torch.float8_e8m0fnu` dtype to PyTorch | vkuzo | closed | [
"module: cpu",
"ciflow/trunk",
"release notes: quantization"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147462
Summary:
Continuing the work from https://github.com/pytorch/pytorch/pull/146427.
Adds the `torch.float8_e8m0fnu` dtype to PyTorch, as detailed in
https://github.com/pytorch/pytorch/issues/146414 . Please see the issue... | true |
2,863,778,374 | [Easy] Add Delimeter To Show Where Allocation Addr Begins | sraikund16 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: profiler",
"topic: improvements"
] | 7 | CONTRIBUTOR | Summary: When we print the addr we append an "s" or a "b" to the beginning of an addr. Since the addr is in hex, a user might be confused and think the "b" is part of the address. Added an approstrophe to clear this up
Test Plan: CI
Differential Revision: D69828538
| true |
2,863,754,153 | [ROCm] scaled_dot_product_attention using mem-efficient backend (aotriton) produces wrong outputs with custom attn_mask on torch 2.6.0+rocm6.2.4 | fxmarty-amd | closed | [
"module: rocm",
"triaged",
"module: sdpa"
] | 3 | NONE | ### 🐛 Describe the bug
Hi,
As discussed on slack and on https://github.com/huggingface/transformers/issues/30056#issuecomment-2657390613, SDPA with custom attn_mask using mem-efficient backend (aotriton 0.8.0) produces wrong outputs on torch 2.6.0 stable ROCm release. This is fixed on torch nightly that uses aotrito... | true |
2,863,662,746 | [ROCm] Fix sort for non-standard bool | pragupta | closed | [
"module: rocm",
"open source",
"Merged",
"topic: not user facing",
"ciflow/periodic",
"rocm",
"ciflow/rocm"
] | 16 | CONTRIBUTOR | When converting from uint8 to bool using `view` op, we get a bool that has 0 for false and a non-zero value for true. However, these kinds of bool have undefined behavior. We only read the last bit as 0 or 1 to convert to false or true.
In this fix, we convert bools to uint8, which will convert false to 0 and non-ze... | true |
2,863,556,129 | `torch.nn.functional.conv1d` can cause a `Floating point exception (core dumped)` | cybersupersoap | closed | [
"module: crash",
"module: nn",
"triaged",
"module: mkldnn",
"topic: fuzzer"
] | 2 | NONE | ### 🐛 Describe the bug
A `Floating point exception` will be raised when using `torch.nn.functional.conv1d`
```python
import torch
arg_1_tensor = torch.rand([20, 16, 50], dtype=torch.float32)
arg_1 = arg_1_tensor.clone()
arg_2_tensor = torch.rand([33, 16, 3], dtype=torch.float32)
arg_2 = arg_2_tensor.clone()
arg_3_te... | true |
2,863,539,565 | `torch.svd` can cause an `INTERNAL ASSERT FAILED` | cybersupersoap | closed | [
"module: crash",
"triaged",
"module: mkl",
"module: third_party",
"module: linear algebra",
"topic: fuzzer"
] | 3 | NONE | ### 🐛 Describe the bug
An INTERNAL ASSERT error will be raised when using `torch.svd`
```python
import torch
arg_1_tensor = torch.rand([2, 2**31], dtype=torch.float32) # Setting the size of the input data to a large value
arg_1 = arg_1_tensor.clone()
arg_2 = False
res = torch.svd(arg_1, compute_uv=arg_2)
```
Error... | true |
2,863,529,315 | `torch.cholesky_solve` can cause an "INTERNAL ASSERT FAILED" | cybersupersoap | closed | [
"module: crash",
"module: mkl",
"module: linear algebra",
"topic: fuzzer"
] | 2 | NONE | ### 🐛 Describe the bug
An INTERNAL ASSERT error will be raised when using `torch.cholesky_solve`
```python
import torch
arg_1_tensor = torch.rand([3, 2**31], dtype=torch.float32)
arg_1 = arg_1_tensor.clone()
arg_2_tensor = torch.rand([3, 3], dtype=torch.float32)
arg_2 = arg_2_tensor.clone()
res = torch.cholesky_solve... | true |
2,863,288,583 | DRAFT Revert "[ATen][CUDA] Implement 128 bit vectorization v2 (#145746)" | atalman | open | [
"Stale",
"ciflow/binaries_wheel"
] | 3 | CONTRIBUTOR | This reverts commit e84bf88dde509d44175a0a1c00cec13c9926843e.
Fixes #ISSUE_NUMBER
| true |
2,863,265,662 | Update Arm Compute Library (ACL) to v25.02 | fadara01 | closed | [
"open source",
"module: arm",
"Merged",
"ciflow/trunk",
"release notes: releng",
"ciflow/linux-aarch64"
] | 9 | COLLABORATOR | Among many things, this version of ACL fixes the redundant declaration warning that we're blocked on in (#145942, #146620, #147337) and introduces better scheduling heuristics for GEMMs
Fixes #ISSUE_NUMBER
cc @malfet @snadampal @milpuz01 @annop-w | true |
2,862,839,728 | [Inductor] Fix `torch.polygamma()` when n == 1 | shink | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 24 | CONTRIBUTOR | Fixes #147450
Be consistent with cpu kernel:
https://github.com/pytorch/pytorch/blob/77dbd2853599a0f8245975746df9b2ff31f86d25/aten/src/ATen/native/cpu/UnaryOpsKernel.cpp#L433-L444
Got this in the case:
```
Eager: tensor([1.2914e+15]), dtype: torch.float32
Compile: tensor([1.2914e+15]), dtype: torch.float3... | true |
2,862,813,188 | Reland "Introduce new template heuristic for triton autotune configs" | jataylo | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ciflow/rocm",
"ci-no-td",
"ciflow/inductor-rocm"
] | 56 | COLLABORATOR | This change was reverted in https://github.com/pytorch/pytorch/pull/147388 for regressing an internal workload.
I have removed the additional ir.device_type calls in mm_scaled and unpack_mixed_mm.py which could be contributing to the additional compile time.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-... | true |
2,862,757,509 | sdfasdf | AnnaTrainingG | closed | [] | 0 | NONE | ### 🐛 Describe the bug
0.0778 | 0.6107 | 0.6592 | 0.3743
-- | -- | -- | --
0.0068 | 0.1044 | 0.0728 | 0.05
452700000 | 141926 | 18216000 | 70436
-0.0029126 | -0.0003828 | 0.00516129 | 0.00228267
</span>
### Versions
asdf | true |
2,862,745,855 | polygamma is less precise on torch.compile when n == 1 | maybeLee | closed | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
This is similar to my previous issue https://github.com/pytorch/pytorch/issues/143648.
When `n==1`, polygamma will execute `trigamma_kernel` under eager, but execute `cal_polygamma` under torch.compile. It seems that executing `trigamma_kernel` instead of `cal_polygamma` is more precise (pleas... | true |
2,862,527,535 | gradient checkpointing with use_reentrant=False cannot reduce peak memory | KK666-AI | open | [
"module: activation checkpointing",
"triaged"
] | 1 | NONE | ### 🐛 Describe the bug
I am comparing the memory cost between `use_reentrant=False` and `use_reentrant=True` when using gradient checkpointing. When set `use_reentrant=False`, i find the peak memory is exactly the same with the one without using gradient checkpointing.
The following is my script, suppose the script... | true |
2,862,468,364 | [XPU][DO NOT MERGE]Let XPU use VS2019 | Stonepia | closed | [
"oncall: jit",
"module: windows",
"open source",
"ciflow/binaries",
"ciflow/trunk",
"release notes: releng",
"topic: not user facing",
"ciflow/binaries_wheel",
"ciflow/xpu",
"module: xpu"
] | 2 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
This PR tries to let XPU use VS2019 to build
cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel @peterjc123 @mszhanyi @skyline75489 @nbcsm @iremyux @Blackhex @gujinghui @fengyuan14 @guangyey | true |
2,862,457,025 | [Export][AO] Include another batch_norm op in supported batch_norms | anzr299 | closed | [
"open source",
"release notes: quantization",
"release notes: AO frontend"
] | 2 | NONE | Running export with decomposition in PT export produces a graph with `aten._native_batch_norm_legit_functional.default` instead of other identified batch_norm ops in torch ao. This PR aims to include this op into torch AO utils.
```
class SimpleModel(torch.nn.Module):
def __init__(self, input_dim=10, hidden_di... | true |
2,862,455,933 | [XPU] [DO NOT MERGE] Test for permissive flag with vs2019 | Stonepia | closed | [
"oncall: jit",
"module: rocm",
"release notes: releng",
"module: xpu"
] | 2 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd @gujinghui @fengyuan14 @guangyey | true |
2,862,444,836 | [5/N] Remove unnecessary once flag usage | cyyever | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"module: compiled autograd"
] | 13 | COLLABORATOR | Fixes #ISSUE_NUMBER
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames @xmfan | true |
2,862,426,018 | NaN values originating from the normalize_weight_jit function during the backward pass | Sz520zS | open | [
"oncall: jit"
] | 0 | NONE | ### 🐛 Describe the bug
```
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.cuda.amp import autocast
from thirdparty.swish import Swish as SwishFN
from thirdparty.inplaced_sync_batchnorm import SyncBatchNormSwish
from thirdparty.checkpoint import checkpoint
from util.utils import avera... | true |
2,862,418,655 | MPS: Scaled Product Attention Passes Tensors with Inadmissible Size to `MetalPerformanceShadersGraph`, leading to Crashes | FabianSchuetze | closed | [
"high priority",
"triaged",
"module: regression",
"module: mps",
"module: sdpa"
] | 6 | CONTRIBUTOR | ### 🐛 Describe the bug
The following code leads to a crash on MPS:
```python
import torch
import torch.nn.functional as F
device = torch.device('mps')
## To provoke the error, an non-continguous tensor needs to be created
q = torch.rand(3, 592, 4, 49, 32).to(device)
k = torch.rand(3, 592, 4, 49, 32).to(device)
v =... | true |
2,862,386,038 | Use has_triton_package in _inductor.runtime.hints | dilililiwhy | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 4 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
Use existing method for triton check
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,862,306,475 | Split test_transformers.py | Zhenbin-8 | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Split test_transformers.py into test_transformers.py and test_transformers_privateuser1.py. Currently the privateuse1 test cases in test_transformers.py are skipped since they conflict with cuda test cases.
| true |
2,862,304,237 | Dynamo Unsupported: call_method UserDefinedObjectVariable(dict_items) __iter__ () {} | shink | closed | [
"triaged",
"oncall: pt2",
"module: dynamo",
"dynamo-triage-jan2025"
] | 8 | CONTRIBUTOR |
Repro:
```python
@torch.compile(backend="eager", fullgraph=True)
def f(x, items ):
it = iter(items)
return next(it), x.sin()
x = torch.randn(3)
dct = {'a': 3, 'b': 3}
res = f(x, dct.items())
print(res)
```
### Versions
main
cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSu... | true |
2,862,261,014 | [fr][fix] Split MatchState and dynamic info for fr analysis downstream | fduwjj | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"suppress-api-compatibility-check",
"suppress-bc-linter"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147439
The original MatchState type was declared as a python Enum. Although we did make it callable but we consume it right away. There are downstream cases when we need it to be a python class which is not supported in Python enu... | true |
2,862,212,443 | About the trunk workflow triggered by the pull request | swgu98 | closed | [] | 0 | NONE | Why does a pull request (e.g. 147434) trigger a workflow trunk based on a push event? | true |
2,862,166,247 | To avoid default eps ineffective in some precision(float16), dynamically set the default value of eps in optimizers such as Adam based on precision. | 1994cxy | open | [
"module: optimizer",
"triaged",
"needs design"
] | 4 | NONE | ### 🚀 The feature, motivation and pitch
Recently, I have been developing my own model based on the BLIP-2 model, attempting to incorporate some new features into the input of the Q-former. Since the feature extraction network for the new features consumes GPU memory, and my GPU only has 80GB of memory, I used float16... | true |
2,862,145,953 | Fix c++ implementation of strip_function_call | aorenste | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | #143063 was missing handling a couple UCS cases as well as had some bugs in the way it dealt with errors.
- Fix all the UCS handling (and make some of the common code more common)
- Make sure all the error paths return `nullptr`
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->_... | true |
2,862,096,260 | [FlexAttention] Fix weird generate stride call in flex decode | drisspg | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147435
# Summary
Seems like we had a redundant tuple unpack and that doesn't appear to be supported in new triton
Fixes https://github.com/pytorch/pytorch/issues/147373
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobin... | true |
2,862,091,192 | [reland][cutlass backend] Do not change dtype of GEMM template for cutlass 3x | henrylhtsang | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 11 | CONTRIBUTOR | Reland of https://github.com/pytorch/pytorch/pull/146877
incorporate forward fix (didn't land): https://github.com/pytorch/pytorch/pull/147185
Summary:
I think this is a change in the right direction.
Right now, when we try to find a cutlass gemm, we generate bunch of gemm templates, and filter out those that d... | true |
2,862,081,557 | [Inductor] Avoid tensor slice overflow for large step | DDEle | open | [
"triaged",
"open source",
"Merged",
"Reverted",
"Stale",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ci-no-td"
] | 11 | CONTRIBUTOR | Fixes #147071
Currently, if step is a value very close to INT64_MAX, the calculation of slice output length will overflow. This PR tries to fix this problem and thus fix #147071.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @ch... | true |
2,862,064,372 | Expose is_available API for torch.backends.mkldnn | FFFrog | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 13 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147432
As the title stated.
Like torch.backends.mkl, torch.backends.openmp and so on, they all expose
is_available API for users. | true |
2,862,048,491 | xpu: torch.xpu.get_arch_list() to return [] if xpu not compiled | dvrogozh | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: improvements",
"topic: not user facing",
"ciflow/xpu",
"release notes: xpu"
] | 4 | CONTRIBUTOR | Initially discussed here: https://github.com/pytorch/pytorch/pull/132945#discussion_r1957366131
Previously `torch.xpu.get_arch_list()` got relaxed to work even if XPU device is not available. However, we overlooked the case when pytorch is not compiled with XPU support. In such a case function throws an exception. T... | true |
2,862,016,908 | [Quant] flip: throw runtime error for QUInt4x2 and QUInt2x4 input | Xia-Weiwen | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"intel"
] | 6 | COLLABORATOR | Fixes #147208
**Summary**
The `flip` op causes memory corruption for `torch.quint4x2` and `torch.quint2x4` inputs. It is because the TensorIterator-based implementation does not support multiple elements per byte. And `torch.quint4x2` and `torch.quint2x4` are deprecated in PyTorch. So, we add a check here to throw ... | true |
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