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 |
|---|---|---|---|---|---|---|---|---|
2,858,279,205 | torch.isin does not support scalar `test_element` under torch.compile | meetmul | closed | [
"triaged",
"oncall: pt2",
"module: decompositions"
] | 3 | NONE | ### 🐛 Describe the bug
According to the doc: https://pytorch.org/docs/stable/generated/torch.isin.html, `test_element` can be either vector or scalar, but this API will directly raise exception when receiving scalar `test_element` under torch.compile.
Please run the following code to reproduce this issue:
```python
... | true |
2,858,207,623 | Comm reordering can make Inductor use variable before its definition | lw | open | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
When using PyTorch 2.6.0 with some code that uses tensor parallelism I encountered an issue that manifests as follows:
```
File "/tmp/torchinductor_lcw/5r/c5rh5j7ln7q5ww6b23zxiuv7bdxprxg7iwjsed32bcix7r7helem.py", line 2815, in call
buf40 = torch.ops._c10d_functional.all_gather_into_tensor... | true |
2,858,179,950 | Never ending compile | bhack | closed | [
"triaged",
"oncall: pt2",
"module: inductor",
"oncall: export",
"module: aotinductor"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
I'am forking this ticket from https://github.com/pytorch/pytorch/issues/147323
I supposed that it was just a long running compile session but it seems never going to end
`100.0 3.6 122:34.20 cc1plus`:
How we are going to debug these cases?
### Error logs
_No response_
### Versions
nig... | true |
2,858,112,087 | torch.export.export fails when one input is a class inheriting from torch.nn.Module | xadupre | open | [
"oncall: pt2",
"export-triaged",
"oncall: export"
] | 1 | COLLABORATOR | ### 🐛 Describe the bug
``transformers.cache_utils.DynamicCache`` inherits from ``torch.nn.Module``. It seems to confuse ``torch.export.export`` and gives the following error:
```text
File ".../site-packages/torch/export/_trace.py", line 1697, in _export_to_aten_ir_make_fx
raise UserError(UserErro... | true |
2,858,073,038 | [MPS] Fix incorrect size for uint3 arg | blawrence-ont | open | [
"triaged",
"open source",
"release notes: mps"
] | 3 | CONTRIBUTOR | With the metal validation layer enabled I get the following error:
> validateComputeFunctionArguments:844: failed assertion `Compute
> Function(naive_matmul_half): argument sizes[0] from buffer(4) with
> offset(0) and length(12) has space for 12 bytes, but argument has a
> length(16).'
The spec (https://de... | true |
2,858,071,944 | [MPS] Fix metallib embedding in static builds | blawrence-ont | open | [
"triaged",
"open source",
"release notes: mps"
] | 4 | CONTRIBUTOR | `-sectcreate` doesn't have any effect on static libraries, so when building as such we have to let the client do that part.
This fixes static builds since they currently trigger this exception: https://github.com/pytorch/pytorch/blob/71855a1cad1346a27a83984e245bbd16a7b56f53/aten/src/ATen/native/mps/OperationUtils.mm... | true |
2,858,009,026 | [compile] Modularize very long compilation | bhack | closed | [
"triaged",
"oncall: pt2",
"module: inductor",
"oncall: export",
"module: aotinductor"
] | 33 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
On a model export/compile I see that there is a very very long stage (more then 1 hours) compiling a single generated c++ final file that is more that 78K+ lines
```bash
g++ /tmp/torchinductor_root/<hash>/<hash>.cpp -D TORCH_INDUCTOR_CPP_WRAPPER -D STANDALONE_TORCH_HEADER -D C... | true |
2,857,924,311 | Add NEON implementation for 8 bit quantized embedding bag on aarch64 | annop-w | closed | [
"module: cpu",
"open source",
"module: arm",
"Merged",
"ciflow/trunk",
"release notes: quantization",
"topic: performance",
"ciflow/linux-aarch64",
"arm priority"
] | 6 | CONTRIBUTOR | This improves performance by ~5.5x on NeoverseV1 cores using the following benchmarking script:
```
import torch
import torch.nn as nn
import numpy as np
import torch.autograd.profiler as profiler
np.random.seed(0)
torch.manual_seed(0)
class SimpleEmbeddingBagModel(nn.Module):
def __init__(self, num_em... | true |
2,857,861,529 | [FSDP] Moving module's view tensor to device | mieshkiwrk | open | [
"oncall: distributed",
"triaged",
"module: fsdp"
] | 4 | NONE | ### 🐛 Describe the bug
When wrapping `nn.Module` with `FSDP` it's moving tensors to device using `tensor.data = tensor.to(device)` instead of `tensor = tensor.to(device)`, source: [torch/distributed/fsdp/_init_utils.py#L1025](https://github.com/pytorch/pytorch/blob/main/torch/distributed/fsdp/_init_utils.py#L1025)
D... | true |
2,857,806,740 | [TESTING] [NO MERGE] Testing new triton commit for release/2.7 | jataylo | closed | [
"open source",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"keep-going",
"ciflow/unstable",
"ciflow/rocm",
"ci-no-td",
"ciflow/inductor-rocm",
"ciflow/rocm-mi300",
"ciflow/inductor-perf-test-nightly-rocm"
] | 66 | COLLABORATOR | testing only
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,857,647,461 | Fix the tiny doc descriptions | FFFrog | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 5 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147319
As the title stated | true |
2,857,554,472 | Torch.export.export produces a graph with inplace operations | anzr299 | closed | [
"oncall: pt2",
"oncall: export"
] | 3 | NONE | ### 🐛 Describe the bug
Graphmodule produced by torch.export.export is supposed to produce no operations with in-place operators as far as my knowledge goes from the [docs](https://pytorch.org/docs/stable/export.html#an-example). I have the following code which can show an example of this case(Also applies to torchvis... | true |
2,857,536,338 | Tensorboard `add_video()` broken for `moviepy>=2.0` | araffin | open | [
"triaged",
"module: tensorboard"
] | 1 | NONE | ### 🐛 Describe the bug
Since https://github.com/Zulko/moviepy/pull/1340 (and [release 2.0](https://github.com/Zulko/moviepy/releases/tag/v2.0.0)), moviepy directly exposes imports.
So the current
https://github.com/pytorch/pytorch/blob/e8b20f6ef39e006e6da90de736ae85a1ba55c159/torch/utils/tensorboard/summary.py#L658-... | true |
2,857,435,790 | [ROCm][Windows] Fix unrecognized constexpr std::memcpy for HIP-clang | m-gallus | closed | [
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 15 | CONTRIBUTOR | Since in MSVC's 2019/2022 implementation of STL memcpy is not defined as a constexpr function, HIP clang compiler on Windows cannot evaluate the following memcopy as one that could be resolved during the compile time. To resolve this, a `__builtin_memcpy` is used instead which doesn't have this limitation.
cc @jeffd... | true |
2,857,375,810 | [ROCm] Introduce AMD specific inductor gemm tuning | jataylo | closed | [
"module: rocm",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: rocm",
"module: inductor",
"ciflow/inductor",
"ciflow/rocm",
"ciflow/inductor-rocm",
"ciflow/inductor-periodic"
] | 16 | COLLABORATOR | Replaces https://github.com/pytorch/pytorch/pull/143286
Adds ROCm specific MM configs for max-autotune incorporating ROCm specific triton tuning kernargs such as waves_per_eu, kpack, matrix_instr_nonkdim. This PR also introduces behavior to allow tuning for GROUP_M in triton gemm case.
Dynamo huggingface inferen... | true |
2,857,196,966 | [Inductor][CPP] Eliminate the overhead of BRGEMM fetching for Half micro gemm on CPU Inductor | CaoE | open | [
"open source",
"Stale",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 2 | COLLABORATOR | Split `brgemm` method into `brgemm_create` and `brgemm_execute` to avoid the overhead of key hashing and fetching for oneDNN BRGEMM object. Such overhead is not negligible when the gemm is very fast, e.g., on small shapes.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzhe... | true |
2,857,099,062 | [pt2-benchmarks] Compiler reset on every run | anijain2305 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 7 | CONTRIBUTOR | Internal benchmarks call `run` in a loop. Compiler reset gives a clean env
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,857,090,118 | return value of Work.exception() can't be used in Python | sanshang-nv | open | [
"oncall: distributed",
"triaged"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
Can't get error message in python code with function `Work.exception()`. [link](https://github.com/pytorch/pytorch/blob/2b30e94fc04878066b60554682368ee0b92d0128/torch/_C/_distributed_c10d.pyi#L263)
Or what's the right way to use this API?
```
def do_comm_test(group):
rank = torch.distribu... | true |
2,857,069,718 | Fix test_device_memory_allocated | Stonepia | closed | [
"open source",
"Merged",
"module: testing",
"ciflow/trunk",
"topic: not user facing",
"ciflow/xpu",
"module: xpu"
] | 5 | CONTRIBUTOR | Fixes #147310
The `torch.ones` allocates memory and is released immediately, thus the following assertion will fail.
This PR stores it into a temp variable to fix it.
cc @gujinghui @EikanWang @fengyuan14 @guangyey | true |
2,857,060,067 | [XPU] test_device_memory_allocated failed | Stonepia | closed | [
"triaged",
"module: testing",
"module: xpu"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
The following test failed:
```Bash
pytest -k test_device_memory_allocated test/test_xpu.py
```
Failure message:
```Bash
File "/home/pytorch/test/test_xpu.py", line 483, in test_device_memory_allocated
self.assertGreater(torch.xpu.memory_allocated(0), current_alloc[0])
AssertionError: ... | true |
2,857,035,354 | Why doesn't work.wait work? | FieeFlip | closed | [
"oncall: distributed"
] | 4 | NONE | ### 🐛 Describe the bug
```Python
import os
import torch
import torch.distributed as dist
import time
def main():
# 初始化进程组（后端使用NCCL优化GPU通信）
dist.init_process_group(backend="nccl")
# 获取当前进程信息
rank = dist.get_rank()
local_rank = int(os.environ["LOCAL_RANK"])
world_size = dist.get_world_size()
... | true |
2,857,029,125 | Update slow tests | pytorchupdatebot | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/slow",
"ci-no-td"
] | 3 | COLLABORATOR | This PR is auto-generated weekly by [this action](https://github.com/pytorch/pytorch/blob/main/.github/workflows/weekly.yml).
Update the list of slow tests. | true |
2,857,015,371 | Match view node and _unsafe_view node, as they have same schema | pralay-das | closed | [
"triaged",
"open source",
"topic: not user facing",
"module: inductor"
] | 7 | CONTRIBUTOR | **Description:** we have observed that in some cases in the pattern it is creating a `view` node but in the original model that replace with `_unsafe_view` node. Because of both of the schemas are same, so we will match these nodes and proceed further.
Fixes #ISSUE_NUMBER
cc @voznesenskym @penguinwu @EikanWang @j... | true |
2,856,957,670 | Allow XPU device for validating the arguments to sparse compressed tensor factory functions | xytintel | closed | [
"open source",
"Merged",
"ciflow/trunk",
"release notes: sparse",
"ciflow/xpu",
"release notes: xpu"
] | 3 | CONTRIBUTOR | During Sparse tensor conversion, a validity check is performed. We need to allow XPU to pass this check. | true |
2,856,934,034 | [Dynamo] Allow dynamo to handle 'or' operator between two dicts | shink | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo"
] | 22 | CONTRIBUTOR | Fixes #146538
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,856,910,042 | Optimize `Sequential` methods description | zeshengzong | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: nn",
"topic: docs"
] | 10 | CONTRIBUTOR | Fixes #146892
Add methods description and examples for [`Sequential` document](https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html)
## Test Result
### Before
### After
![image](https://github.com... | true |
2,856,808,094 | fp16 channels_last created Nan in batchnorm backward | jthakurH | closed | [
"module: cpu",
"triaged",
"bug"
] | 1 | NONE | ### 🐛 Describe the bug
If input is set with channels_last then there is Nan in output of backward
```
import torch
op = torch.nn.BatchNorm2d(num_features=3, affine=True, track_running_stats=True)
ifm = torch.empty(size=[16, 3, 224, 224]).uniform_(0, 1).to(dtype=torch.float16)
ifm = ifm.contiguous(memory_format=tor... | true |
2,856,804,638 | Update torch-xpu-ops commit pin | xytintel | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"keep-going",
"ciflow/xpu"
] | 6 | CONTRIBUTOR | Update the torch-xpu-ops commit to [b421032c8fed40df5eaee395c2e7f5f8a7bcc815](https://github.com/intel/torch-xpu-ops/commit/b421032c8fed40df5eaee395c2e7f5f8a7bcc815), includes:
- Correct int4 weight pack implementation
- Enhance build system: only build one shared library for the user
| true |
2,856,628,135 | Remove deprecate method and attirbute in `LRScheduler` | zeshengzong | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"suppress-bc-linter",
"release notes: optim"
] | 14 | CONTRIBUTOR | Following [#99270 suggestion](https://github.com/pytorch/pytorch/issues/99270#issuecomment-1511656408), remove deprecate method `LRScheduler.print_lr`
_____
# BC-breaking note
**`LRScheduler.print_lr()` along with the `verbose` kwarg to the LRScheduler constructor has been deprecated since release 2.2. Please us... | true |
2,856,610,589 | switch from deprecated `find_package(CUDA)` to `find_package(CUDAToolkit)` | h-vetinari | open | [
"module: mkldnn",
"open source"
] | 2 | CONTRIBUTOR | Towards #76082
In conda-forge we've recently started running into hard problems related to #76082; the vast majority of our builds (pytorch itself excluded now, but true for most of its dependents, e.g. `torch{vision,audio,...}`) get built on agents without a physical GPU. It's sufficient to have a complete toolchai... | true |
2,856,571,238 | [MPS][BE] Turn `exec_unary_kernel` as MetalShaderLibrary method | malfet | closed | [
"Merged",
"release notes: mps",
"ciflow/mps"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147299
* #147297
* #147296
And delete duplicate implementations from SpecialOps and UnaryKernel.
Change input and output arguments order for SpecialOps kernels to match those of UnaryOps
Fixes https://github.com/pytorch/pyto... | true |
2,856,544,973 | [Inductor][CPP] Add the legalize low fp support for index expr | leslie-fang-intel | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 4 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147298
**Summary**
Fix issue: https://github.com/pytorch/pytorch/issues/147279. The test case produced a low-precision floating-point value using `ops.index_expr`, but the CPP backend did not handle its legalization. This PR adds s... | true |
2,856,543,455 | [BE] Make `exec_unary_kernel` take TensorIterator as argument | 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):
* #147299
* __->__ #147297
* #147296
| true |
2,856,543,329 | [BE] Switch all structured funcs to stubs | malfet | closed | [
"Merged",
"topic: not user facing",
"release notes: mps",
"ciflow/mps"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147299
* #147297
* __->__ #147296
No need to have separate foobar_out_mps when registering a dispatch to foobar_stub will do
And this makes `exec_unary_kernel` defined in UnaryKernel.mm and
SpecialOps.mm look very similar | true |
2,856,475,051 | Hi, want to know pytorch 2.6 for libtorch what new features have contain in cpp? | mullerhai | closed | [] | 1 | NONE | ### 🚀 The feature, motivation and pitch
HI,
I use libtorch for cpp code ,pytorch 2.6 release ,so for libtorch 2.6 what new features contains?
### Alternatives
_No response_
### Additional context
_No response_ | true |
2,856,435,153 | [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 |
2,856,383,844 | Fix overflow in checkInBoundsForStorage | mikaylagawarecki | closed | [] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147293
| true |
2,856,335,055 | Fix arvr macOS buck pytorch builds | stepanhruda | closed | [
"module: cpu",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: quantization"
] | 7 | CONTRIBUTOR | Summary:
X-link: https://github.com/ctrl-labs/src2/pull/42453
buck arvr macOS builds had a few issues that needed fixing.
Test Plan: build with buck
Differential Revision: D69722372
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 | true |
2,856,320,479 | Do not use username for inductor default_cache_dir | cocktailpeanut | open | [
"triaged",
"open source",
"Stale",
"topic: not user facing",
"module: inductor"
] | 4 | NONE | The existing approach of using the session username fails when:
1. There is no username for the current session
2. The username includes one or more spaces (compile fails)
Unless there's an important reason why this needs to be based on username, it seems much cleaner to just use "torchinductor_cache_dir", which... | true |
2,856,201,558 | [BE]: Enable ruff rule SIM113 | Skylion007 | closed | [
"oncall: distributed",
"open source",
"better-engineering",
"Merged",
"ciflow/trunk",
"release notes: onnx",
"release notes: quantization",
"topic: not user facing",
"fx",
"module: dynamo",
"ciflow/inductor"
] | 6 | COLLABORATOR | Lint rules that tells the user to avoid keeping track of their own counter and use the builtin enumerate when possible.
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv @voznesenskym @penguinwu @Guobing-Chen @XiaobingSuper @zhuh... | true |
2,856,195,976 | fixed optimizer load_state_dict | egg-west | open | [
"triaged",
"open source",
"release notes: optim"
] | 2 | CONTRIBUTOR | Fixes #147288
| true |
2,856,195,820 | Calling optimizer.load_state_dict causes Tensors lost | egg-west | open | [
"module: optimizer",
"triaged"
] | 5 | CONTRIBUTOR | # Description
`Optimizer.load_state_dict(state_dict: StateDict)` will overwrite its own Tensors with the provided `state_dict` instead of copying the Tensor values from `state_dict` to the existing tensors.
Here is an example code to show the issue
```python
import numpy as np
import torch
from torch import nn
"""Tes... | true |
2,856,132,762 | [inductor][fuzzer] legacy `torch.native_batch_norm` should be removed | WLFJ | open | [
"triaged",
"oncall: pt2",
"module: decompositions"
] | 4 | NONE | ### 🐛 Describe the bug
Eager Mode works fine, but Inductor hits assertion error.
Reproduce Example:
```python
import torch
@torch.compile
def f(*args):
sym_0, sym_1, sym_2, sym_3, sym_4, sym_5, sym_6, sym_7 = args
var_442 = torch.empty_permuted(size=sym_0, physical_layout=sym_1)
var_647 = torch.triu_i... | true |
2,856,038,349 | [MPS][BE] Use stubs for floor/ceil/round/trunc | malfet | closed | [
"Merged",
"topic: not user facing",
"release notes: mps",
"ciflow/mps"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147266
* __->__ #147286
To avoid duplicating logic that those ops are no-ops for integral dtypes
(And in preparation of adding `round_decimals` that calls round_stub if decimals are 0)
Tested for the corner cases by manually invokin... | true |
2,856,037,907 | [MPS][BE] Use stubs for floor/ceil/round/trunc | malfet | closed | [
"topic: not user facing",
"release notes: mps",
"ciflow/mps"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* (to be filled)
To avoid duplicating logic that those ops are no-ops for integral dtypes
(And in preparation of adding `round_decimals` that calls round_stub if decimals are 0) | true |
2,855,994,427 | torch.softmax gives wierd result under specific condition on cuda double tensors with multiple rows and 781 columns. | Xenadon | open | [
"module: nn",
"module: cuda",
"triaged"
] | 4 | NONE | ### 🐛 Describe the bug
`torch.softmax(torch.zeros(c,781, dtype=torch.float64, device='cuda'),dim=1)` gives wrong answer for `c > 1`.
```python
import torch
print(torch.softmax(torch.zeros(1,781, dtype=torch.float64, device='cuda'),dim=1)) # correct
print(torch.softmax(torch.zeros(2,781, dtype=torch.float64, device='... | true |
2,855,993,524 | SerializeError for ScriptObject in AOTInductor | vbharadwaj-bk | closed | [
"triaged",
"oncall: pt2",
"oncall: export",
"module: aotinductor"
] | 5 | NONE | ### 🐛 Describe the bug
I am trying to export a model that uses a custom C++ class (a `_C.ScriptObject`) as a component of the model state. The export runs fine, but I get an SerializeError with `aoti_compile_and_package`. Minimal code below (the C++ class is called `Test` and holds a single integer as its state).
Th... | true |
2,855,992,412 | assert fails to trigger inside torch.compile | ad8e | open | [
"triaged",
"oncall: pt2",
"module: decompositions",
"module: inductor",
"internal ramp-up task"
] | 4 | CONTRIBUTOR | ### 🐛 Describe the bug
```
import torch
@torch.compile
def func():
a = torch.tensor([1.0, -2.0])
test = torch.all(a > 0)
print(test)
assert test, "should throw"
print("should not run")
func()
```
Output:
```
tensor(False)
should not run
```
Removing the torch.compile causes the assert to work.
Searching find... | true |
2,855,983,879 | [inductor][dynamo][fuzzer] `torch.bucketize` causes dynamo maximum recursion depth exceeded error | WLFJ | open | [
"triaged",
"bug",
"oncall: pt2",
"module: dynamo"
] | 0 | NONE | ### 🐛 Describe the bug
Reproduce Example:
```python
import torch
@torch.compile
def f(*args):
var_92, sym_1 = args
return torch.bucketize(sym_1, var_92)
var_92 = torch.randn(size=(1000,))
res = f(var_92, 1)
print(res)
```
This example works fine in Eager Mode, but causes maximum recursion depth exceeded w... | true |
2,855,930,735 | [inductor][fuzzer] Inconsistent Behavior with `aten.linalg_tensorinv` | WLFJ | open | [
"triaged",
"oncall: pt2",
"topic: fuzzer"
] | 3 | NONE | ### 🐛 Describe the bug
# Description
When executing the following PyTorch program using `torch.compile`, the result is inconsistent with the expected mathematical behavior.
```python
import torch
@torch.compile
def f(*args):
sym_5, sym_6, sym_7, sym_8 = args
var_714 = torch.ops.aten.fft_fftfreq(n=sym_5, d... | true |
2,855,915,051 | [inductor][fuzzer] Compilation Error in `torch.arange` + `torch.sum` with `torch.float16` | WLFJ | closed | [
"oncall: pt2",
"oncall: cpu inductor"
] | 2 | NONE | ### 🐛 Describe the bug
Reproduce example:
```python
import torch
def f(*args):
sym_0, sym_1, sym_2, sym_3 = args
var_228 = torch.arange(start=sym_0, end=sym_1, dtype=sym_2)
return torch.sum(var_228, dim=sym_3)
res = f(300, 1024, torch.float16, (0,))
print(res)
res = torch.compile(f)(300, 1024, torch.... | true |
2,855,905,611 | [inductor][fuzzer] `ZeroDivisionError` in `torch.unsafe_split` when input empty size tensor with zero `split_size` | WLFJ | open | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 0 | NONE | ### 🐛 Describe the bug
Reproduce example:
```python
import torch
def f(*args):
sym_0, sym_1, sym_2 = args
var_485 = torch.ones(sym_0)
return torch.unsafe_split(var_485, split_size=sym_1, dim=sym_2)
res = f((0,), 0, -1,)
print(res) # (tensor([]), )
res = torch.compile(f)((0,), 0, -1,) # ZeroDivisionEr... | true |
2,855,901,075 | [inductor][fuzzer] decomposition failed on `torch.unsafe_chunk` with empty size input tensor | WLFJ | open | [
"triaged",
"oncall: pt2",
"module: decompositions"
] | 0 | NONE | ### 🐛 Describe the bug
The inductor crashed when `torch.unsafe_chunk` with an empty size tensor.
```python
import torch
print(torch.__version__)
def f(*args):
sym_0, sym_1 = args
var_6 = torch.zeros(sym_0)
return torch.unsafe_chunk(var_6, chunks=sym_1, dim=0)
res = f((0,), 4,)
print('eager', res)
re... | true |
2,855,892,635 | [Pyper] Enable GQA in PMA module | mengluy0125 | open | [
"fb-exported"
] | 7 | CONTRIBUTOR | Summary: Add an option to use gqa instead of pma
Test Plan:
# how to set gqa
```
def enable_gqa(job):
job = job.set_arg_path("arch.mtml_model.shared_arch.pytorch_interformer.interformer.interformer_config.megaformer_config.use_gqa", True)
return job
```
# local reproduce
```
CUDA_VISIBLE_DEVICES=5 buck2 run ... | true |
2,855,833,080 | [inductor][fuzzer] `torch.ops.aten.lift` causes internal assertion `isFunctionalTensor` fail | WLFJ | open | [
"module: crash",
"triaged",
"oncall: pt2",
"module: empty tensor",
"topic: fuzzer"
] | 1 | NONE | ### 🐛 Describe the bug
# Bug Description
`torch.ops.aten.lift` works fine in eager mode, but cause `TORCH_INTERNAL_ASSERT(!at::functionalization::impl::isFunctionalTensor(self));` failed in inductor.
For example:
```python
import torch
print(torch.__version__)
def f(sym_0, sym_1, sym_2):
var_365 = torch.rand... | true |
2,855,821,215 | PaddleOCR PyTorch conflicts | monkeycc | open | [
"module: binaries",
"module: windows",
"triaged"
] | 3 | NONE | ```
python -m pip install paddlepaddle-gpu==2.6.2 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
pip install paddleocr
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
```
```python
from paddleocr import PaddleOCR
import torch
```
paddleocr 2.9.1
pytorch 2.6.0
... | true |
2,855,788,371 | [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 |
2,855,781,151 | [experimental][fbcode] delayed compile | bobrenjc93 | closed | [
"ciflow/trunk",
"module: dynamo",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147272
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames
Differential Revision: [D69728173](https://our.internmc.facebo... | true |
2,855,780,471 | [experimental] delayed compile | bobrenjc93 | closed | [] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147271
* #147270
| true |
2,855,780,449 | [experimental] delayed compile | bobrenjc93 | closed | [
"module: dynamo",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147271
* __->__ #147270
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,855,755,887 | [experimental] delayed export | bobrenjc93 | closed | [
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147269
* #147265
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,855,714,246 | flex_attention throws `CUDA error: an illegal memory access was encountered` | mauriceweiler | closed | [
"triaged",
"oncall: pt2",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention"
] | 2 | NONE | ### 🐛 Describe the bug
When calling torch.compiled `flex_attention` after a `max_pool2d` operation I encounter an error:
```
RuntimeError: CUDA error: an illegal memory access was encountered
```
To align the data layout expected by `flex_attention` and `max_pool2d` I am using `.reshape` and `.moveaxis` operations.
I... | true |
2,855,702,037 | flex_attention with N<128 tokens throws `CUDA error: device-side assert triggered` | mauriceweiler | closed | [
"triaged",
"oncall: pt2",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention"
] | 4 | NONE | ### 🐛 Describe the bug
When using torch.compiled flex_attention with N<128 tokens I get the following error:
```
RuntimeError: Triton Error [CUDA]: device-side assert triggered
```
It seems to be related to using a BlockMask with `H>1`. For `H=1` everything works out.
To reproduce this issue, run `python reproducer.... | true |
2,855,694,472 | [MPS] Implement and test round.decimals | malfet | closed | [
"Merged",
"release notes: mps",
"ciflow/mps"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147266
* #147286
If inductor can do it, why not eager | true |
2,855,656,718 | [experimental] delayed compile | bobrenjc93 | closed | [
"ciflow/trunk",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147269
* __->__ #147265
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames
Differential Revision: [D69708204](https://our.internmc... | true |
2,855,649,776 | `Dim.DYNAMIC` inferred to be constant | bhack | open | [
"triaged",
"oncall: pt2",
"module: dynamic shapes"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
```python
example_frames = torch.randn(1, num_frames, H, W, 3, device=device, dtype=input_dtype)
dynamic_shapes = {
"video": {1: Dim.DYNAMIC},
"query_points": {0: torch.export.Dim.STATIC},
}
```
### Error logs
```python
I0215 18:22:50.860000 11289 site-packag... | true |
2,855,426,096 | How to trigger several independent communications simultaneously? | Ind1x1 | open | [
"oncall: distributed",
"triaged"
] | 0 | NONE | For example, in training with 4 GPUs, I divide the GPUs into pairs and create two communication groups: group1 = dist.new_group([0, 1]) and group2 = dist.new_group([2, 3]). If I want to run independent dist.all_gather operations within both communication groups simultaneously, it results in an error. I'd like to ask ho... | true |
2,855,311,984 | Add the memory and dispatch to the logging module. | jokercw147 | open | [
"triaged",
"open source",
"Stale"
] | 7 | NONE | We want to print logs for the memory and dispatch separately. Therefore, the memory and dispatch log modules are added to this PR. | true |
2,855,306,692 | `F.interpolate()` + `torch.compile(dynamic=True)` produces wrong shape | gau-nernst | closed | [
"triaged",
"oncall: pt2",
"module: dynamic shapes"
] | 0 | NONE | ### 🐛 Describe the bug
```python
import torch
import torch.nn.functional as F
@torch.compile(dynamic=True)
def f(x):
return F.interpolate(x, scale_factor=1 / 300, mode="linear")
f(torch.randn(1, 8, 396 * 300)).shape # torch.Size([1, 8, 395]) -> wrong shape, should be (1, 8, 396)
```
- This does not happen for... | true |
2,855,300,233 | add PrivateUse1 backend in fsdp collecitves | zqwenn | closed | [
"oncall: distributed",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: distributed (fsdp)"
] | 11 | CONTRIBUTOR | add PrivateUse1 backend in fsdp collecitves
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,855,274,524 | Unable to export to ONNX | The serialized model is larger than the 2GiB limit imposed by the protobuf library. | NSTiwari | closed | [
"module: onnx",
"triaged"
] | 5 | NONE | ### 🐛 Describe the bug
I'm trying to convert and export the PaliGemma 2 model to ONNX using a custom script, however, it fails with the following error:
RuntimeError: The serialized model is larger than the 2GiB limit imposed by the protobuf library. Therefore the output file must be a file path, so that the ONNX exte... | true |
2,855,202,831 | t2 | henrylhtsang | closed | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147258
* #147257
Summary:
Test Plan:
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,855,202,489 | t1 | henrylhtsang | closed | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147258
* __->__ #147257
Summary:
Test Plan: | true |
2,855,186,400 | [inductor] [dtype checking] `nn.LayerNorm` looses the check for `dtype=complex` | shaoyuyoung | open | [
"triaged",
"oncall: pt2",
"module: aotdispatch",
"module: pt2-dispatcher"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
**symptom**: When using `LayerNorm` with `dtype=complex`, eager throws errors both on CPP and CUDA but inductor pass the check for them.
**device backend**: both on CPP and triton
**exposed area**: `complex32`, `complex64`, `complex128`
**repro**
```python
import torch
import torch.nn as nn
im... | true |
2,855,153,472 | [inductor] [cpu] `torch.nn.RReLU()` doesn't respect `fallback_random` flag | shaoyuyoung | open | [
"triaged",
"oncall: pt2",
"module: inductor",
"oncall: cpu inductor"
] | 6 | CONTRIBUTOR | ### 🐛 Describe the bug
**symptom**: ablation says `aot_eager_decomp_partition` is the first backend with incorrect optimization
**codegen backend**: only CPP backend.
**repro**
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch._inductor import config
config.fallback_random = Tr... | true |
2,855,096,094 | [TorchRec][PT2] disable contextlib in PT2 train pipeline | TroyGarden | closed | [
"oncall: distributed",
"internals",
"fb-exported",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"ci-no-td"
] | 14 | CONTRIBUTOR | Summary:
X-link: https://github.com/pytorch/torchrec/pull/2730
Pull Request resolved: https://github.com/pytorch/torchrec/pull/2596
# context
* more details in the [post](https://fb.workplace.com/groups/1075192433118967/permalink/1587079018596970/)
* disable contextlib with PT2
Test Plan:
* run command
```
TORCH... | true |
2,855,074,316 | Fix clang-tidy warnings in torch/jit | cyyever | open | [
"oncall: jit",
"triaged",
"open source",
"NNC",
"release notes: jit"
] | 4 | COLLABORATOR | Fixes #ISSUE_NUMBER
cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel | true |
2,855,074,118 | utils: Update md5 call to be fips compliant | seemethere | closed | [
"Merged",
"ciflow/trunk",
"release notes: python_frontend",
"topic: not user facing"
] | 8 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147252
Updates md5 call to be fips compliant according to this issue:
* https://github.com/pytorch/pytorch/issues/147236
Not going to add a conditional here because minimum the python version
that we support is already 3.9
... | true |
2,855,063,681 | [inductor] Simplify grid handling | jansel | closed | [
"topic: not user facing",
"ciflow/mps",
"skip-pr-sanity-checks",
"module: inductor",
"ciflow/inductor",
"ciflow/inductor-rocm"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147251
Before this PR, calling a triton kernel would look like:
```py
kernel.run(a, b, xnumel, grid=grid(xnumel), stream=stream0)
```
where the `grid=` was passed as a callable (function closure) arg. This PR removes the grid a... | true |
2,855,043,474 | Hipify: use usedforsecurity=False for MD5 | JBlitzar | closed | [
"open source",
"topic: not user facing"
] | 4 | NONE | Fixes #147236
CCing people in the original issue
cc @malfet @seemethere @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd @Legends0
| true |
2,855,026,820 | [Inductor] Fix 3D tiling with permute | blaine-rister | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | This PR adds a test case and tiny fix for 3D tiling. Before this PR, tiling would crash because one of the candidates lacked a `"y"` dimension. Now, when we're calculating 3D tiling candidates, we assume the y size is 1 if it's missing.
The test case implements a 3D permute using block pointers.
```
@triton.jit
... | true |
2,855,011,729 | Move ir_pre_fusion.txt and ir_post_fusion.txt to TORCH_LOGS | dulinriley | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 17 | CONTRIBUTOR | Fixes #147002
Moves ir_{pre, post}_fusion.txt to be controlled by TORCH_LOGS instead of TORCH_COMPILE_DEBUG.
Updated tests of these logs as well.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amj... | true |
2,855,009,860 | Remove CAFFE2_USE_EXCEPTION_PTR | cyyever | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 8 | COLLABORATOR | The check is for older compilers and is now aways true. | true |
2,855,007,793 | dynamo: Don't crash when encountering a object with no __name__ | c00w | 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):
* __->__ #147246
This was triggering on ScriptFunctions. Note that other than badly implemented c functiosn, this seems to be almost impossible to trigger, so I wrote a smaller unit test, rather than a full repro. Let me know if people feel... | true |
2,854,999,369 | Update lintrunner sympy version to 1.13.3 | henrylhtsang | closed | [
"topic: not user facing"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147245
| true |
2,854,996,263 | Add SmallVectorImpl move constructor and other fixes | cyyever | open | [
"triaged",
"open source",
"Stale",
"topic: not user facing"
] | 4 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,854,977,439 | [ROCm] [TunableOp] Track top solutions during tuning process | naromero77amd | closed | [
"module: rocm",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/rocm"
] | 9 | COLLABORATOR | For each set of GEMM parameters that are evaluated by Tunableop, keep track of the top 5 solutions. Print the top 5 solutions when `PYTORCH_TUNABLEOP_VERBOSE=2`.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang | true |
2,854,965,074 | [ca] trace saved variable unpacking | xmfan | closed | [
"Merged",
"Reverted",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo",
"module: compiled autograd",
"ci-no-td"
] | 6 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147891
* #147804
* #147796
* __->__ #147242
## Before
Previously, CA will always unpack all saved variables stored in the autograd graph before executing it. This meant that we can't capture unpack hooks as part of the CA graph, and the... | true |
2,854,956,823 | [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 |
2,854,950,703 | [symbolic shapes] Add replacement for backed symints | angelayi | closed | [
"Merged",
"ciflow/trunk",
"release notes: fx",
"fx",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147240
* #146939
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,854,947,874 | `DeviceCopy in input program` source hint | bhack | open | [
"triaged",
"oncall: pt2"
] | 2 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
Can we give an hint on where this is coming from in the code. It could be useful if we want to maintain the warning so that the user have a concrete action.
### Alternatives
_No response_
### Additional context
nightly
cc @chauhang @penguinwu | true |
2,854,946,005 | [apf] Fix input adapter | angelayi | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: export"
] | 6 | CONTRIBUTOR | Summary: Add support for inputs that no longer exist in `input_fields`, but is not actually used by the original program. In this case, we just give it a dummy input based on the node's metadata.
Test Plan: Verified for S488841
Differential Revision: D69328093
| true |
2,854,889,308 | [export] Loosen symint input serialization | angelayi | closed | [
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: export"
] | 8 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
| true |
2,854,865,676 | Enforce FIPS compliance on Pytorch on python 3.9+ | Legends0 | closed | [
"module: build",
"module: rocm",
"good first issue",
"triaged",
"actionable"
] | 10 | NONE | Since python 3.9, when FIPS compliance is enforced `hashlib.md5()` may not be usable without the usedforsecurity parameter set to False (https://docs.python.org/3/library/hashlib.html).
The hipify utility is not currently able to operate on a FIPS system without an error:
https://github.com/pytorch/pytorch/blob/122476... | true |
2,854,862,989 | logging: close handler after removing it | tebartsch | open | [
"triaged",
"open source",
"Stale",
"topic: not user facing"
] | 7 | NONE | Fixes
```python
import unittest
import os
import tempfile
import torch
import tracemalloc
tracemalloc.start(10)
class Test(unittest.TestCase):
def test(self):
with tempfile.TemporaryDirectory() as temp_dir:
os.environ["TORCH_LOGS_OUT"] = f"{temp_dir}/test.log"
to... | true |
2,854,849,568 | Allow strobelight profiling a specific frame id , ex [27/*] | laithsakka | open | [
"oncall: profiler"
] | 0 | CONTRIBUTOR | title.
cc @robieta @chaekit @guotuofeng @guyang3532 @dzhulgakov @davidberard98 @briancoutinho @sraikund16 @sanrise | true |
2,854,840,765 | [inductor] Don't leak pointers to cpp_wrapper with lru_cache | jansel | 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):
* #147251
* __->__ #147233
Putting lru_cache on methods will keep pointers to the `self` objects
alive forever and leak memory.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayi... | true |
2,854,799,114 | [Inductor][ROCm][CK] Unhardedcoded kernel shapes for ck_conv_template codegen | AviralGoelAMD | closed | [
"module: rocm",
"triaged",
"open source",
"topic: not user facing",
"module: inductor"
] | 3 | 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 |
2,854,795,072 | Ensure conj/neg flags are set in destination for CUDA->CPU copies | amjames | open | [
"open source",
"topic: not user facing"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147231
* #149226
Fixes #146286 | true |
2,854,747,811 | Code Refactoring for getting start and stride from global ranks | shengfukevin | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 13 | CONTRIBUTOR | Summary: Code Refactoring for getting start and stride from global ranks, this function can be used in different collective backend.
Differential Revision: D69555405
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
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