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 |
|---|---|---|---|---|---|---|---|---|
2,854,745,573 | Iterate over dense dim first in split reduction reindexing | eellison | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 19 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147229
Fix for https://github.com/pytorch/pytorch/issues/144431.
Improves perf from 0.29963893827160504 -> 0.0396331632970453.
In split reductions, we view an input tensor as a single dimension, then reduce over it. When we ar... | true |
2,854,732,605 | Don't use '-e' when installing Triton | jayfurmanek | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor",
"ciflow/inductor-rocm"
] | 11 | CONTRIBUTOR | Currently the install_triton.sh script uses "pip install -e ." to install Triton.
Using the -e is sometimes appropriate for develop work but is less appropriate for delivery.
To make matters worse it seems the behavior of the -e various depending on the version of pip invovled.
This PR removes the -e and installs ... | true |
2,854,685,391 | [dynamo] fix error message when logging graph that contains hops | ydwu4 | 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):
* __->__ #147227
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,854,683,292 | Unable to use XPU device on PyTorch 2.6 | iori2333 | closed | [
"triaged",
"module: regression",
"module: xpu"
] | 13 | NONE | ### 🐛 Describe the bug
After installing PyTorch 2.6.0-XPU according to [documentation](https://pytorch.org/docs/stable/notes/get_start_xpu.html#Binaries), PyTorch could not detect any XPU devices:
```
>>> import torch
>>> torch.__version__
'2.6.0+xpu'
>>> torch.xpu.is_available()
/home/iori/.conda/envs/xpu/lib/python... | true |
2,854,675,581 | cpp_wrapper: Fix even more tests | benjaminglass1 | closed | [
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ciflow/rocm",
"ci-no-td",
"ciflow/rocm-mi300"
] | 20 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150673
* __->__ #147225
* #150672
* #150671
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,854,673,561 | [Cutlass] Restore search space for swizzle | mlazos | closed | [
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 9 | CONTRIBUTOR | This restores the previous search space, since swizzle is now a runtime parameter, there shouldn't be extra compile-time overhead from searching this now.
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147224
* #147223
* #147222
cc @voznesenskym @penguinwu @EikanWang @jg... | true |
2,854,673,496 | [Cutlass] Add support for runtime param choices, starting with swizzle | mlazos | closed | [
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 2 | CONTRIBUTOR | This PR adds support for swizzle as a runtime parameter choice. Future runtime parameter choices can be added to the [get_runtime_arg_info](https://github.com/pytorch/pytorch/blob/2d40f9fb525350ac55486714e5620548f53b2958/torch/_inductor/codegen/cuda/cuda_template.py#L282) list method and then possible choices can be [l... | true |
2,854,673,436 | [Inductor] Add autotuning artifact logging | mlazos | closed | [
"Merged",
"ciflow/trunk",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"release notes: inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147224
* #147223
* __->__ #147222
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,854,672,872 | [ROCm] Update inductor-perf-test-nightly-rocm.yml to use the correct labels & frequency | amdfaa | closed | [
"module: rocm",
"open source",
"Merged",
"topic: not user facing"
] | 4 | CONTRIBUTOR | This workflow takes around 75-80hrs on ROCm, so scaling down the frequency to once per week until we get more CI capacity.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,854,672,682 | Build TorchVision with USE_SYSTEM_NVTX=0 Flag Would Encounter Failure Due to the use of PROJECT_SRC_DIR | nWEIdia | open | [
"module: cpp-extensions",
"module: cuda",
"triaged"
] | 2 | COLLABORATOR | ### 🐛 Describe the bug
During internal testing, we encountered a failure related to the following piece of code:
https://github.com/pytorch/pytorch/blob/6f035d8462e43b1c678e5f334d52d9df0e00e6bf/cmake/public/cuda.cmake#L176
We were trying to build torchvision using the following, but we had to use -DUSE_SYSTEM_NVTX... | true |
2,854,611,570 | Allow mark_dynamic to mark parameters as dynamic instead of silence failing. | laithsakka | open | [
"triaged",
"oncall: pt2",
"module: dynamic shapes"
] | 0 | CONTRIBUTOR | One caveat is that parameters are forced to be static even when marked dynamic is used on them unless a [global flag](https://www.internalfb.com/code/fbsource/[8e8caf410d62dea512c2fe20b7008d1e72543cbf]/fbcode/caffe2/torch/_dynamo/config.py?lines=122) is switched. Switching that flag enable automatic dynamic globally on... | true |
2,854,610,156 | Periodic Activations Module | GulkoA | open | [
"triaged",
"open source",
"release notes: nn",
"topic: improvements"
] | 6 | NONE | Fixes #146708
| true |
2,854,602,335 | [dynamo][mappingproxy][inspect] Support existing types.MappingProxyType | 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):
* __->__ #147217
Fixes https://github.com/pytorch/pytorch/issues/147162
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,854,549,638 | [sigmoid] Test OSS model runner with test_export.py | zhxchen17 | closed | [
"fb-exported",
"release notes: export"
] | 5 | CONTRIBUTOR | Summary: There are ~260 tests for all the corner cases of export from test_export.py. utitlizing to test sigmoid in the OSS setting.
Test Plan: buck test mode/opt caffe2/test:test_export -- -r _sigmoid
Reviewed By: SherlockNoMad
Differential Revision: D69060784
| true |
2,854,517,617 | cpp_wrapper: Fixup output code indentation | benjaminglass1 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 9 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147225
* #146706
* #147403
* #146991
* __->__ #147215
* #146424
* #146109
Closes #142165.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 ... | true |
2,854,482,232 | DISABLED test_triton_kernel_multiple_out (__main__.AutogradFunctionTests) | pytorch-bot[bot] | closed | [
"module: rocm",
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamo"
] | 3 | NONE | Platforms: rocm
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_triton_kernel_multiple_out&suite=AutogradFunctionTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/37209672197).
Over the past 3 hours,... | true |
2,854,465,772 | export input dict mutation in strict mode | ydwu4 | open | [
"oncall: pt2",
"oncall: export"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
The following code fail to export in strict mode due to input mutation:
```python
import torch
inps2 = (
torch.ones([])*1,
{
"a": torch.ones([])*10,
"b": torch.ones([])*10,
},
# pyre-ignore
torch.Tensor([True])
)
class Foo2(torch.nn.Module):
def __init__... | true |
2,854,417,784 | dict_tag optimization leads to wrong results with relational guards | isuruf | open | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 1 | COLLABORATOR | ### 🐛 Describe the bug
`dict_tag` optimization checks if a dictionary that we guard on has changed or not and if all the values in it are immutable, no further processing is done on the dictionary if the tag is the same. This avoids running a lot of guards on the dictionary and its values and makes the C++ guards fas... | true |
2,854,350,225 | Skip unsupported types by MPS in `test_torchinductor.py` | 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):
* #147266
* #147205
* __->__ #147211
- Skip unsupported dtypes in `test_split_cumsum` (and manually skip int64 for MacOS13)
- Adapt `test_cat` to use `torch.half` instead of `torch.double` on MPS
- Skip `test_adaptive_avg_pool1d_argmax` is ... | true |
2,854,323,582 | Consider relaxing the in-place mutation restriction for torch.cond and torch.while_loop | tingyangk | open | [
"triaged",
"oncall: pt2",
"module: higher order operators",
"module: pt2-dispatcher"
] | 2 | NONE | ### 🐛 Describe the bug
`torch.while_loop` can be very beneficial for use cases such as LLM decoding. However, given the current in-place mutation limitations that `torch.cond` and `torch.while_loop` have, the real application for `torch.cond` and `torch.while_loop` is restricted.
Use the implementation of [EMMA](htt... | true |
2,854,278,470 | Passing `src_key_padding_mask` as `bool` vs `float` causes different outputs from `nn.TransformerEncoderLayer` | petercall | closed | [
"module: nn",
"triaged"
] | 3 | NONE | ### 🐛 Describe the bug
The module `nn.TransformerEncoderLayer` is outputting different values from the `forward` function based on the data type of the `src_key_padding_mask` argument. The documentation says that either boolean data type or float data type should be accepted, so the difference in output is puzzling.
... | true |
2,854,186,170 | [CPU][Quantization] `torch.flip` on `torch.quint4x2` quantized tensor causes memory corruption (invalid free/malloc) | WLFJ | closed | [
"module: crash",
"oncall: quantization",
"bug",
"topic: fuzzer"
] | 9 | NONE | ### 🐛 Describe the bug
When executing the following test case on CPU, applying `torch.flip` to a quantized tensor can result in memory corruption errors. The issue is non-deterministic and can produce different errors across multiple runs.
example:
```python
import torch
def f(*args):
sym_5, sym_6, sym_7 = arg... | true |
2,854,181,597 | [torch] Make amdsmi cdll hook private | danzimm | closed | [
"module: cuda",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: cuda"
] | 6 | CONTRIBUTOR | Summary: https://github.com/pytorch/pytorch/actions/runs/13314282597/job/37186177974 yelled at me for landing a seemingly public API that's not exported. It's a private API, so lets prepend `_` to make that clear
Test Plan: CI
Differential Revision: D69665234
cc @ptrblck @msaroufim @eqy | true |
2,854,127,295 | [Inductor] SIGILL instead of `ZeroDivisionError` in `torch.remainder` when using `@torch.compile` (Nightly Regression) | WLFJ | open | [
"triaged",
"oncall: pt2",
"module: inductor",
"module: empty tensor",
"topic: fuzzer"
] | 0 | NONE | ### 🐛 Describe the bug
# Issue Description:
When executing the following test case with `@torch.compile` using Inductor on the PyTorch Nightly build, the process crashes with a SIGILL signal instead of raising the expected `ZeroDivisionError`.
```python
import torch
@torch.compile
def f(*args):
sym_0, sym_1, s... | true |
2,854,120,847 | [MPSInductor] Adjust check_bounds | 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):
* #147266
* __->__ #147205
* #147211
To make upper bound inclusive, which fixes `test_vectorized_ops_masked` and results in the following code
```python
mps_lib_0 = compile_mps_shader("""
#include <c10/metal/random.h>
#include <c1... | true |
2,854,053,905 | Fix the AOTI compile failure with ARM CPU for Meta internal | hl475 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Summary: Fix the AOTI compile failure with ARM CPU for Meta internal
Differential Revision: D69642211
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,854,011,968 | Fix rms_norm in fp16/bf16 | riccardofelluga | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 22 | CONTRIBUTOR | Fixes #134106. This PR moves the `upcasted_result` down-casting after all computation is done.
Since the multiplication with the weight_opt input is not done in half precision, the current code path is doing the following: fp16 -> fp32 -> fp16 -> fp32 -> fp16. What we want tho is to avoid down-casting and this PR pr... | true |
2,853,870,900 | [inductor][refactor] Move _compile_file to cpp_builder | desertfire | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Summary: To further conslidate cpp build logic into cpp_builder
Test Plan: CI
Differential Revision: D69595327
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,853,866,494 | torch._dynamo.exc.Unsupported: 'skip function getfullargspec | bhack | open | [
"triaged",
"oncall: pt2",
"module: dynamo",
"dynamo-triage-jan2025"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
Exporting a model that is using popular mmengine crashed.
That root cause seems this line in the engine:
https://github.com/open-mmlab/mmengine/blob/main/mmengine/utils/misc.py#L362
### Error logs
Relevant part of the error
```python
with `torch._dynamo.exc.Unsupported: 'skip function get... | true |
2,853,636,359 | DISABLED test_max_autotune (__main__.TestFlexAttention) | pytorch-bot[bot] | closed | [
"module: rocm",
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: inductor"
] | 4 | NONE | Platforms: rocm
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_max_autotune&suite=TestFlexAttention&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/37196534734).
Over the past 3 hours, it has been deter... | true |
2,853,213,795 | [Inductor] Fix Inplace Buffer inner name conflict | 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):
* __->__ #147199
**Summary**
Fix issue: https://github.com/pytorch/pytorch/issues/146975, when create `InplacedBuffer` inner name, we only count the number of unique `InplacedBuffer` or `RemovedArg`. The name may have conflict, for example... | true |
2,853,116,943 | add PrivateUse1 backend in fsdp collecitves | zqwenn | closed | [
"oncall: distributed",
"open source",
"release notes: distributed (fsdp)"
] | 2 | CONTRIBUTOR | add PrivateUse1 backend in fsdp collecitves
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,853,082,604 | Unify all sympy versions to avoid conflicts within PyTorch | FFFrog | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 11 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147197
As the title stated.
There are some tiny diffrences between 1.13.1 and 1.13.3:
1.13.1:
https://github.com/sympy/sympy/blob/2e489cf4b1438ae134ba98a44a80cc9add1306b0/sympy/core/numbers.py#L1591
1.13.3:
https://github.com/sympy... | true |
2,853,026,211 | [Draft][Inductor][CPP] Adopt block sparse for FlexAttention CPU | jianan-gu | open | [
"open source",
"module: inductor"
] | 2 | CONTRIBUTOR |
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,852,986,127 | update kineto submodule to include fix for windows build | briancoutinho | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 8 | CONTRIBUTOR | Fixes an issue causing windows builds to fail
https://github.com/pytorch/kineto/pull/1039 | true |
2,852,971,060 | Add ppc64le wheel build support | sandeepgupta12 | open | [
"triaged",
"open source",
"topic: not user facing"
] | 6 | NONE | This PR adds support for building ppc64le wheels as part of the CI/CD pipeline. The goal is to enable ppc64le architecture compatibility for wheel builds, ensuring that TensorFlow/PyArrow (or any related package) can be distributed for Power architecture users.
**Changes Introduced**
✅ Enabled ppc64le architecture ... | true |
2,852,942,436 | [inductor][triton] Ignore block ptr advances for removed buffers | kundaMwiza | closed | [
"triaged",
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ci-no-td"
] | 13 | CONTRIBUTOR | block ptr advancements should also be deferrered conditional on the associated buffer not being removed. For example, if `FusedSchedulerNode(op0-op1)` has a store in `SchedulerNode` `op0` that is read in `op1`, the store and associated block ptr that would be created for `op0` in isolation is no longer needed.
Fix... | true |
2,852,914,379 | Add Torch Logs for ir_pre_fusion and ir_post_fusion | zeshengzong | closed | [
"open source",
"module: inductor"
] | 2 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,852,906,115 | [Inductor] Add input value checking to randint meta function | DDEle | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 10 | CONTRIBUTOR | Fixes #147070
Adding value checking for the range to the meta function, similar to which in the CUDA/CPU aten op.
Test with
```
PYTORCH_TEST_WITH_DYNAMO=1 pytest test/test_tensor_creation_ops.py -k test_randint_inference
``` | true |
2,852,859,513 | Wheel v1 | sandeepgupta12 | closed | [
"topic: not user facing"
] | 3 | NONE | Fixes #ISSUE_NUMBER
| true |
2,852,819,568 | [StaticRuntime] Support a new pattern (aten::to with 5 inputs) for ClipRangesToGatherToOffsets | coufon | closed | [
"oncall: jit",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: jit"
] | 7 | CONTRIBUTOR | Summary:
Support the following new pattern for ClipRangesToGatherToOffsets:
Before optimization:
```
%11175 : Tensor, %11176 : Tensor = fb::clip_ranges_gather(%int_66.1, %getitem_1784.1, %347)
%getattr_256.1 : int = prim::dtype(%11175)
%to_298.1 : Tensor = aten::to(%11176, %getattr_256.1, %13, %13, %12)
%lengths_to_of... | true |
2,852,802,071 | Fix torch.mean out dtype check | FFFrog | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 10 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147188
**For CPU**:
Type promotion is supported for torch.mean
**For Meta**:
Not supported for torch.mean
ISSUE related:
https://github.com/pytorch/pytorch/issues/138399 | true |
2,852,775,600 | [torch.export] How to export a model with kv cache | exeex | open | [
"oncall: pt2",
"oncall: export"
] | 6 | NONE | ### 🐛 Describe the bug
In an attention layer, kv cache needs a variable number "start_pos" from outside.
(may related to https://github.com/pytorch/pytorch/issues/146990)
Here is a simplified model for reproducing the issue:
```python
import torch
from torch import nn
class Cache(nn.Module):
def __init__(self... | true |
2,852,759,908 | ROCm: Remove static specifier for allow_tf32 variable. | jagadish-amd | closed | [
"module: rocm",
"triaged",
"open source",
"Merged",
"topic: not user facing",
"ciflow/rocm"
] | 6 | CONTRIBUTOR | Since the env variable HIPBLASLT_ALLOW_TF32 can change, remove static type for allow_tf32 variable so that it captures the current value of env variable HIPBLASLT_ALLOW_TF32.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,852,696,898 | [cutlass backend] forward fix of #146877 | henrylhtsang | closed | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147185
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,852,679,749 | [MPS][BE] Migrate polar to use functor | malfet | closed | [
"Merged",
"topic: not user facing",
"release notes: mps",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147184
* #147183
* #147182
| true |
2,852,679,684 | [MPS][BE] Add copysign integral flavors as functor | malfet | closed | [
"Merged",
"topic: not user facing",
"release notes: mps",
"ciflow/mps"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147184
* __->__ #147183
* #147182
| true |
2,852,679,619 | [BE][MPS] Infer results of functor | malfet | closed | [
"Merged",
"topic: not user facing",
"release notes: mps",
"ciflow/mps"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147184
* #147183
* __->__ #147182
Do not assume that functor will return the same results as its arguments, but rather dynamically infer it using `decltype` and `::metal::declval`
This is a no-op that prepares for migration of `copysign` of... | true |
2,852,662,973 | Remove code for Python < 3.9 | cyyever | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 9 | COLLABORATOR | Fixes #ISSUE_NUMBER
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,852,657,421 | [DTensor] `Partial(sum)` reductions are wrongly cached (?) | main-horse | open | [
"oncall: distributed",
"triaged",
"module: dtensor"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
First of all, a very simple motivating example:
```python
# OMP_NUM_THREADS=1 torchrun --nproc-per-node 2 what.py
import os
import torch
from torch.distributed.tensor import DTensor, Partial, init_device_mesh
# Create mesh
mesh = init_device_mesh('cuda', (int(os.environ.get("WORLD_SIZE", "1")... | true |
2,852,644,010 | [FSDP2] OOM when use integer `reshard_after_forward` that smaller than DP size | FindDefinition | open | [
"oncall: distributed",
"triaged",
"module: fsdp"
] | 4 | NONE | ### 🐛 Describe the bug
When we use fsdp2 module to do inference only with `reshard_after_forward` set, we found that if we use `reshard_after_forward=True` or `reshard_after_forward=False`, fsdp2 works fine, but if we use a integer `reshard_after_forward=4` with `world_size=8`, OOM happens in second step of inference... | true |
2,852,580,305 | Add numerical tests for speciality ops | henrylhtsang | closed | [
"Stale",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147178
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,852,543,882 | Fix `torch.max` optional args `dim`, `keepdim` description | zeshengzong | closed | [
"open source",
"Merged",
"ciflow/trunk",
"release notes: python_frontend"
] | 9 | CONTRIBUTOR | [`torch.max`](https://pytorch.org/docs/stable/generated/torch.max.html#torch.max) optional args `dim`, `keepdim` not described in document, but users can ignore them.
```python
>>> import torch
>>> a = torch.randn(3,1,3)
>>> a.max()
tensor(1.9145)
>>> a.max(dim=1)
torch.return_types.max(
values=tensor([[ 1.14... | true |
2,852,528,699 | Fix failing export of DTensor toy model | tousif-anwar | open | [
"triaged",
"open source",
"Stale",
"release notes: export"
] | 3 | NONE | Fixes #147172
Address the issue of strict-mode export failing at AOTAutograd when exporting a model with DTensors.
* **torch/_export/__init__.py**
- Modify `aot_compile` function to handle DTensors correctly.
- Add `_handle_dtensor` function to process DTensors during the export process.
* **torch/_export/conver... | true |
2,852,506,663 | UserWarning with Compiled Autograd | cora-codes | closed | [
"triaged",
"oncall: pt2",
"module: compiled autograd"
] | 1 | NONE | ### 🐛 Describe the bug
I've came across the following when using compiled autograd: `UserWarning: Trying to prepend a node to itself. This behavior has no effect on the graph"` I don't see any behavior to indicate that this is causing issues, but it is a bit annoying to see the error per a worker. I'd be happy to pro... | true |
2,852,505,962 | Release/2.5: [ROCm] TopK optimizations for AMD GPUs | apakbin | closed | [
"oncall: distributed",
"module: rocm",
"open source",
"release notes: nn",
"fx",
"module: inductor"
] | 2 | CONTRIBUTOR | Mirroring the PR: https://github.com/pytorch/pytorch/pull/146387 for the release/2.5 branch
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd @ezyang @SherlockNoMad @E... | true |
2,852,501,652 | [cutlass backend] add subproc tests | henrylhtsang | 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):
* #147485
* __->__ #147173
* #147169
I want to separate subproc autotuning from the main tests. And I observed that for addmm, it can work without subproc.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuh... | true |
2,852,496,821 | [export] failing to export DTensor toy model | pianpwk | closed | [
"oncall: pt2",
"module: dtensor",
"oncall: export"
] | 7 | CONTRIBUTOR | ### 🐛 Describe the bug
Testing out https://github.com/kwen2501/export-playground/blob/main/dist_pre_export.py
Strict-mode export is failing at AOTAutograd, when exporting a model with DTensors (`parallelize_module` has been called). Not sure what's going on, `_dynamo.export` is able to produce a graph without collec... | true |
2,852,495,363 | torch.compile not DCEing unused rand calls | eellison | closed | [
"triaged",
"oncall: pt2",
"module: inductor",
"internal ramp-up task"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
Running the following with `TORCH_LOGS="post_grad_graphs" `
```
import torch
@torch.compile()
def foo(y):
x = torch.rand([10])
return y + 2
foo(torch.rand([4], device="cuda"))
```
Gives:
```
def forward(self, arg0_1: "f32[4][1]cuda:0"):
# No stacktrace found for followi... | true |
2,852,474,867 | [BUG][PyTorch 2.0 Export][quant]:get_source_partitions() may return different matches with same input graph | GodHforever | open | [
"oncall: quantization",
"good first issue",
"oncall: pt2",
"oncall: export"
] | 7 | NONE | ### 🐛 Describe the bug
I am attempting to extend the quantization backend based on PyTorch 2.0 export. The operator I have chosen is `torch.gather` .
The input code I tested is as follows
```python
class GatherLayer(nn.Module):
def forward(self, x):
assert x.shape == (2,2)
x = tor... | true |
2,852,468,365 | [cutlass backend] remove triton from most tests and add an integration test | henrylhtsang | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147485
* #147173
* __->__ #147169
Removing aten and triton from the list of backends for the tests that have it. Instead, add a small integration test to make sure autotuning works fine.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @G... | true |
2,852,459,957 | [FSDP2] The evil `record_stream` in c10d causes FSDP2 to over-allocate GPU memory | leonardo0lyj | closed | [
"oncall: distributed",
"module: c10d",
"module: fsdp"
] | 17 | NONE | Hey Andrew @awgu,
As a big fan of FSDP2, I find an potential bug 😄
## Demand:
- No inter-stream memory fragmentation (incurred by copy in streams)
- Explicit Prefetch
- CPU runs a head of GPU by a lot
## `_set_unshard_async_op(True)`
To satisfy these demands, FSDP2 has to turn on [`_set_unshard_async_op(True)`](ht... | true |
2,852,459,837 | [PT2]: allow empty dict to pass type check | kqfu | closed | [
"oncall: jit",
"fb-exported",
"release notes: jit"
] | 7 | CONTRIBUTOR | Summary:
Seeing errors like when testing sigmoid for some 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]'.
```
Let empty dict... | true |
2,852,426,972 | [ONNX] Consolidate constants to a single location | justinchuby | closed | [
"module: onnx",
"open source",
"Merged",
"ciflow/trunk",
"release notes: onnx",
"topic: not user facing"
] | 6 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147166
* #147165
* #147164
| true |
2,852,426,916 | [ONNX] Set warning stacklevel so it appears at the torch.onnx call site | justinchuby | closed | [
"module: onnx",
"open source",
"Merged",
"ciflow/trunk",
"release notes: onnx",
"topic: improvements"
] | 6 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147166
* __->__ #147165
* #147164
| true |
2,852,426,860 | [ONNX] Handle number of outputs in builder | justinchuby | closed | [
"module: onnx",
"open source",
"Merged",
"ciflow/trunk",
"release notes: onnx",
"topic: bug fixes"
] | 6 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147166
* #147165
* __->__ #147164
| true |
2,852,424,516 | [BE] Use `c10::multiply_integers` in cholesky_impl | malfet | closed | [
"Merged",
"topic: not user facing",
"release notes: mps",
"ciflow/mps"
] | 3 | CONTRIBUTOR | That replaces explicit for loop
| true |
2,852,418,092 | [dynamo][inspect] Graph break on mappingproxy | anijain2305 | closed | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
With `inspect` getting inlined, we are seeing new graph breaks on `signature.bind` using `mappingproxy`
```
import inspect
import torch
def greet(greeting, name, punctuation='!'):
"""Simple function to greet a person."""
print(f"{greeting}, {name}{punctuation}")
# Obtain the signatu... | true |
2,852,415,485 | Record the XPU and XCCL build settings in the compiled binary | pkourdis | open | [
"caffe2",
"open source",
"Stale",
"topic: not user facing",
"release notes: xpu"
] | 17 | NONE | Fixes #ISSUE_NUMBER
Currently the XPU and XCCL build settings are not recorded in the compiled binary and are not shown using the `torch.__config__.show()` which is a quick way to check if the binary has been built with such support.
Below is the output adding them (see end of last line):
```
Python 3.12.8 | ... | true |
2,852,415,091 | [inductor] add lowering for repeat_interleave.Tensor with output size specified | eellison | open | [
"triaged",
"oncall: pt2",
"module: inductor",
"internal ramp-up task"
] | 0 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
Repro, and [internal workplace post](https://fb.workplace.com/groups/1075192433118967/posts/1599399114031627):
```
import torch
@torch.compile()
def f(input, repeats):
return torch.repeat_interleave(input, repeats, dim=0, output_size=3) + 1
f = torch.compile(f)
input = t... | true |
2,852,413,173 | [MPS] Fix cholesky_ex for empty inputs | malfet | closed | [
"Merged",
"topic: bug fixes",
"release notes: mps",
"ciflow/mps"
] | 3 | CONTRIBUTOR | By making sure that `info` is actually initialized if input is empty(but no need to do anything about `out`, is it's guaranteed to be an empty tensor)
Also move output resizing logic before `input.numel()` check
Fixes https://github.com/pytorch/pytorch/issues/147128
| true |
2,852,412,403 | [cutlass backend] forward fix of standalone runner for fbcode | henrylhtsang | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147158
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,852,374,633 | [executorch hash update] update the pinned executorch hash | pytorchupdatebot | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 3 | COLLABORATOR | This PR is auto-generated nightly by [this action](https://github.com/pytorch/pytorch/blob/main/.github/workflows/nightly.yml).
Update the pinned executorch hash. | true |
2,852,362,217 | require_exact_stride better handling of expanded dims | eellison | closed | [
"triaged",
"oncall: pt2",
"module: inductor",
"internal ramp-up task"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
We had a previous perf bug https://github.com/pytorch/pytorch/issues/145760 because [require_exact_strides](https://github.com/pytorch/pytorch/blob/057bcd3a454464340025c8d1b698829e2db110e3/torch/_inductor/ir.py#L5275-L5279) did not handle expanded dims well. An expanded dims is a singleton dime... | true |
2,852,360,963 | [dynamo][not ready] Handle builtin methods as f_locals | 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):
* __->__ #147155
```
import torch
def gn(x):
return torch.sin(x)
def fn(method, x, a):
# method is a.append
method(gn(x))
return a
opt_fn = torch.compile(fn, backend="eager", fullgraph=True)
x = torch.ra... | true |
2,852,339,909 | [CI] Use job name to index into test times json | clee2000 | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | When the test times are generated, it doesn't know what the build environment is because it's an environment variable. But when we index into the test times, we (previously) didn't know what the job name is. These are usually the same but sometimes they're different and when they're different it ends up using default... | true |
2,852,320,528 | Re-land exclude upsample_bilinear2d.vec and nearest2d.vec from default export decomposition table | GregoryComer | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: export"
] | 6 | MEMBER | Note: This is a re-land of https://github.com/pytorch/pytorch/pull/141791, which I reverted due to breaking some Meta-internal tests - an internal ET delegate did not handle the non-decomposed upsample_nearest2d, and it was not caught in CI. I've resolved that issue and should be ready to safely re-land.
Summary:
A... | true |
2,852,303,435 | [dynamo][fx] Don't emit `call_function` node to construct dataclass instances for Dynamo and `make_fx` tracing | StrongerXi | closed | [
"release notes: fx",
"fx",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146950
* #146367
* #146714
* #146713
* __->__ #147152
* #147145
As title. The behavior change is limited to Dynamo and `make_fx` tracing
for backward compatibility reasons with `symbolic_trace`.
It heps enforce the invariant that Dynamo an... | true |
2,852,299,723 | Delete Mixed MM Special Casing | eellison | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"keep-going",
"ci-no-td"
] | 31 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147151
Now that torchinductor supports prologue fusion we can delete all the mixed mm code. When I benchmarked int8 weight only mm in the new path compared to int8mm in the old path in the [following benchmark](https://gist.github.c... | true |
2,852,292,244 | [AOTInductor] Guard RAII_cpuMalloc with macro | muchulee8 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Summary: Silence RAII_cpuMalloc(size_t) defined but not used [-Wunused-function]
Test Plan: Existing tests
Differential Revision: D69623481
| true |
2,852,291,084 | dynamo: Count number of opcodes processes | c00w | 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):
* __->__ #147149
This gives us a decent proxy for how big of a graph we functionally had to parse.
Note that this is a cummulative counter. If people feel strongly, I can either write into the dynamo_timed datasets with metrics contexts, o... | true |
2,852,275,307 | For addmm and bmm, check if config.autotune_fallback_to_aten before using aten as a fallback. Also fix bmm cutlass backend | 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):
* __->__ #147148
This PR also fixes BMM, which was silently failing for a while.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @m... | true |
2,852,230,125 | [dynamo] Remove unintended lru_cache | anijain2305 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | I forgot to remove it while add frozenset __contains__ method in this PR
- https://github.com/pytorch/pytorch/pull/146062?fbclid=IwZXh0bgNhZW0CMTEAAR3S_qq8bYxO7pDuHqpr2X-vqkXQrY0KtT14z46bfuRDYikjJBet3uKF2dE_aem_o1c7I4eawKyaEsfiWhnTmw
This is causing memory leak
Fixes #ISSUE_NUMBER
cc @voznesenskym @penguinwu ... | true |
2,852,184,763 | [fsdp] add an experimental allocator hook for buffers that participate in collective communication | yifuwang | open | [
"oncall: distributed",
"open source",
"Stale",
"release notes: distributed (fsdp)",
"ciflow/inductor"
] | 3 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147146
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,852,184,615 | [dynamo][fx] Don't emit `call_function` node to construct `NamedTuple` instances for Dynamo and `make_fx` tracing | StrongerXi | closed | [
"release notes: fx",
"fx",
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146950
* #146367
* #146714
* #146713
* #147152
* __->__ #147145
As title. This effectively undoes #49553, for Dynamo and `make_fx`
tracing only (for `symbolic_trace` backward compatibility reasons).
It heps enforce the invariant that Dynam... | true |
2,852,174,667 | lintrunner and requirements.txt have different versions for sympy | henrylhtsang | closed | [
"module: lint",
"triaged",
"actionable"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
as tiled
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
torch 2.7.0a0+git48e0300 requires sympy==1.13.1; python_version >= "3.9", but you have sympy 1.13.0 which is i... | true |
2,852,140,971 | torch.compile failed with FSDP model with `ignore_states` or `ignore_modules` | YurongYou | open | [
"triaged",
"module: fsdp",
"oncall: pt2",
"module: dynamo",
"dynamo-triage-jan2025"
] | 1 | NONE | ### 🐛 Describe the bug
I have a model that has a submodule must be run in fp32 while the rest of it is run in bf16, thus I need to exclude the submodule from FSDP otherwise FSDP will complain model weights are not in the same dtype. But looks like this option is not compatible with torch.compile.
Minimal reproducibl... | true |
2,852,078,077 | ROCm F8 Datatype Selector | petrex | closed | [
"module: rocm",
"triaged",
"open source",
"Stale",
"release notes: linalg_frontend"
] | 3 | CONTRIBUTOR | TLDR: This PR address https://github.com/pytorch/ao/issues/1066. Adding logic to override FP8 datatype selection based on GPU archs
--------------------
This pull request introduces support for ROCm (Radeon Open Compute) in the CUDA data type handling within the `aten` module. The changes include adding ROCm-sp... | true |
2,852,047,138 | NCCL Update 2.25.1 with CUDA 12.4 build is failing in CI | atalman | open | [
"oncall: distributed",
"triaged",
"module: nccl"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
This is an error:
```
__________ DynamicShapesReproTests.test_ddp_checkpoint_dynamic_shapes __________
Traceback (most recent call last):
File "/var/lib/jenkins/workspace/test/dynamo/test_repros.py", line 6423, in test_ddp_checkpoint
model = nn.parallel.DistributedDataParallel(model)
Fi... | true |
2,851,986,201 | Mask in MaskedTensor does not change device | sandeep-189 | open | [
"triaged",
"module: masked operators"
] | 1 | NONE | ### 🐛 Describe the bug
When you create a MaskedTensor and change it to cuda, the data is the only one that change to cuda. When we use a reduction function on cuda MaskedTensor (sum, to_tensor, etc), it will always fail since the mask in on another device.
```
import torch
from torch.masked import as_masked_tensor
d... | true |
2,851,963,113 | test2 | henrylhtsang | closed | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147139
* #147138
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,851,961,023 | test 1 | henrylhtsang | closed | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147139
* __->__ #147138
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,851,935,820 | [inductor] remove hardcoded mapping to resolve ops from ExternKernelSchedulerNode | xmfan | open | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 2 | MEMBER | ### 🐛 Describe the bug
https://github.com/pytorch/pytorch/pull/146992/files#r1953070064
during runtime estimation, we use this reverse map to lookup ops contained in ExternKernelSchedulerNode
https://github.com/pytorch/pytorch/blob/b0553cee6bbb0c3cfb7896d8f585f4ea32f1d254/torch/_inductor/scheduler.py#L924-L930
i've... | true |
2,851,930,990 | make subproc tests | henrylhtsang | closed | [
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147136
* #146743
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,851,930,418 | Don't print fw_metadata in "Found a graph input that requires gradients" | ezyang | open | [
"triaged",
"oncall: pt2",
"module: aotdispatch",
"module: pt2-dispatcher"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
It's extremely long, and I don't think it's useful for users.
Sample:
```
File /data/users/ezyang/a/pytorch/torch/_functorch/aot_autograd.py:570, in create_aot_dispatcher_function(flat_fn, fake_flat_args, aot_config, fake_mode, shape_env)
562 def create_aot_dispatcher_function(
563 ... | true |
2,851,918,240 | `torch.mul` uses `OpMathType` for computation. | ysiraichi | open | [
"module: cuda",
"triaged",
"module: bfloat16",
"module: half",
"module: python frontend"
] | 5 | COLLABORATOR | The element-wise multiplication implementation for CUDA is currently making use of `OpMathType`, which upcast the inputs if they are of `fp16` or `bf16` data-type for actually running the operation. In summary:
```python
>>> a = torch.rand(5, dtype=torch.bfloat16, device="cuda")
>>> b = torch.rand(5, dtype=torch.bfloa... | true |
2,851,888,685 | all reduce non strict | avikchaudhuri | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: export"
] | 11 | CONTRIBUTOR | Summary:
Some distributed collectives like `all_reduce` have special handling in Dynamo, where they are mapped to functional collectives. Non-strict was previously blind to such mappings, which means using them would fail to trace. Here we show how intercepting them in non-strict's torch function mode can mimic this re... | true |
2,851,888,009 | test 2 ghstack | henrylhtsang | closed | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147132
* #147131
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,851,886,763 | test ghstack | henrylhtsang | closed | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147132
* __->__ #147131
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,851,843,713 | [cond] support output sizes mismatch in front end | ydwu4 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147567
* #147649
* __->__ #147130
This PR finishes https://github.com/pytorch/pytorch/pull/137615 by addressing the TODOs and comments left there.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @b... | true |
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