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 |
|---|---|---|---|---|---|---|---|---|
2,869,337,941 | torch.sort: Optimize memory usage with (dtype_indices: ScalarType, dynamic_indices_dtype: bool) options | voidbag | open | [
"module: cpu",
"triaged",
"open source",
"release notes: mps",
"module: inductor"
] | 14 | NONE | Fixes #147628
The static dtype of indices was kLong(64bit), consuming exccessive memory.
It reduces memory usage, determining the dtype of indices dynamically.
The dtype is one of Byte, UInt16, UInt32 and UInt64.
- ~This PR makes at::arange support uint16, uint32 and uint64 (`at::arange( uint(16|32|64) )->uint(... | true |
2,869,324,770 | Optimize memory usage of torch.sort significantly, with dynamic dtype indices | voidbag | open | [
"triaged",
"enhancement",
"module: python frontend"
] | 3 | NONE | ### 🚀 The feature, motivation and pitch
I optimized torch.sort to return indices with dynamic dtype, not fixed **64bit** torch.long
This proposal can save GPU memory usage significantly.
1.example:
Boolean matrix can express graph.
`ret = torch.sort(torch.zeros((69878, 10677), dtype=torch.bool, device="cuda:0"))`
... | true |
2,869,292,323 | Enforce full FIPS compliance with hashlib - ruff rule S324 on python 3.9+ | Skylion007 | closed | [
"good first issue",
"module: lint",
"triaged",
"enhancement",
"actionable"
] | 0 | COLLABORATOR | ### 🚀 The feature, motivation and pitch
This is to more broadly address the issue here. We need to add a special flag for compliance reasons that the hashlib function is not being used for cryptographic applications. More details can be found here: https://github.com/pytorch/pytorch/issues/147236 . In most builds of ... | true |
2,869,258,327 | HIP error: invalid device function on ROCm RX 7600XT | JackBinary | closed | [
"module: binaries",
"module: rocm",
"triaged"
] | 5 | NONE | ### 🐛 Describe the bug
#### **Issue Summary**
When attempting to perform any GPU compute task using PyTorch with the ROCm/HIP backend, I encounter the following error:
```
RuntimeError: HIP error: invalid device function
HIP kernel errors might be asynchronously reported at some other API call, so the stacktrace bel... | true |
2,869,203,135 | aten.grid_sampler_3d.default is missing a c-shim implementation, using proxy executor as fallback | bhack | open | [
"good first issue",
"triaged",
"oncall: pt2",
"module: inductor",
"oncall: export",
"module: aotinductor"
] | 4 | CONTRIBUTOR | ### 🐛 Describe the bug
Do we need any action item here?
### Error logs
```python
site-packages/torch/_inductor/ir.py:6638] [0/0] aten.grid_sampler_3d.default is missing a c-shim implementation, using proxy executor as fallback
```
### Versions
nightly
cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobi... | true |
2,869,162,898 | [MPS] Rand is broken for 5D+ tensors | andreabosisio | closed | [
"high priority",
"triage review",
"module: random",
"module: mps"
] | 2 | NONE | ### 🐛 Describe the bug
While trying to generate different samples with a diffusion model, I noticed the following problem with the `torch.randn` function when using MPS:
```python
import torch as th
rand_cpu_5d = th.randn((2, 1, 32, 32, 32), device="cpu")
print(th.allclose(rand_cpu_5d[0], rand_cpu_5d[1])) # False, ... | true |
2,869,016,582 | torch.export.export creates guards that denies exporting. | JibAxelera | closed | [
"oncall: pt2",
"export-triaged",
"oncall: export"
] | 6 | NONE | ### 🐛 Describe the bug
### Problem
Trying to export a conv neural network using torch.export.export. If the model and the input tensors are on the GPU and I have a batchnorm layer in the model, it creates guards that make exporting fail in any case.
### Standalone code to reproduce :
Just run the following python ... | true |
2,869,014,377 | Build a storage reader/writer to write checkpoints in HF format | ankitageorge | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: new features",
"topic: not user facing",
"ci-no-td",
"oncall: distributed checkpointing"
] | 12 | CONTRIBUTOR | Title - we want to write checkpoints in HF format with DCP, this diff allows this for the non-distributed use case.
Copy of [D68444967](https://www.internalfb.com/diff/D68444967) (https://github.com/pytorch/pytorch/pull/146352). That diff got reverted because of lint errors. The lint error was due to having imports o... | true |
2,868,996,465 | [Inductor] Update `set_driver_to_gpu` code to avoid backend re-initialization with new Triton | anmyachev | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 7 | COLLABORATOR | cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,868,888,671 | Enabled force_shape_pad for triton tests in test_kernel_benchmark | iupaikov-amd | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"rocm"
] | 18 | CONTRIBUTOR | During ROCm runs we naturally have those tests show that padding path will be slower for our archs and the pad_mm chooses to opt out of padding thus failing those tests.
Reasoning for this is per my understanding those tests don't check IF the operation should be padded in the first place, but HOW is it padded and ... | true |
2,868,774,546 | [Triton 3.3] [ROCm] Enabled split_scan support for ROCm builds | iupaikov-amd | closed | [
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"release notes: rocm",
"module: inductor",
"ciflow/inductor",
"ciflow/rocm"
] | 9 | CONTRIBUTOR | Fixes issue https://github.com/pytorch/pytorch/issues/133228
Enabled split_scan support for ROCm builds.
Must be handled in a non BC breaking way so this functionality is enabled conditionalised on triton version.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongx... | true |
2,868,664,454 | Document patched podman build for s390x runners | AlekseiNikiforovIBM | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 12 | COLLABORATOR | Podman patches from upstream are needed to resolve a couple of issues hit when using it.
Document automated build of podman
with applied patches fixing those issues.
| true |
2,868,554,982 | [ONNX] GNN model inaccuracy: scatter_reduce need to be fixed | canon-cmre-kamil-jacek | closed | [
"module: onnx",
"triaged"
] | 11 | NONE | ### 🐛 Describe the bug
A pytorch-geometric model (GAT) produces different results after conversion to ONNX.
I would not mind minor differences but depending on input data, these can be very large.
Code to reproduce:
```
import logging
import onnxruntime
import numpy as np
import torch
from torch_geometric.nn import ... | true |
2,868,504,276 | Remove useless options for third-party ONNX build | cyyever | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/periodic"
] | 8 | COLLABORATOR | Treat ONNX CMake targets properly and remove unneeded options. | true |
2,868,486,048 | Update merge rules for oneDNN part | EikanWang | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147615
| true |
2,868,473,116 | [Intel GPU] Enable SDPA on XPU | DDEle | closed | [
"module: cpu",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"keep-going",
"ciflow/xpu"
] | 20 | CONTRIBUTOR | Motivation
===
This PR is part of the plan of OneDNN Upstreaming, as #114848 [(comment)](https://github.com/pytorch/pytorch/issues/114848#issuecomment-2451553203) stated. The support of SDPA is via the overridable variance on XPU backend. Beside the added `Attention.cpp` file, `Graph.h` is added to hold utils for O... | true |
2,868,465,777 | torch.nn.AvgPool2d fails with stride >= 2^31 on CUDA | jiren-the-gray | open | [
"module: nn",
"module: cuda",
"triaged",
"module: 64-bit",
"module: pooling",
"topic: fuzzer"
] | 2 | NONE | ### 🐛 Describe the bug
Running `torch.nn.AvgPool2d` with a stride of 2^31 or larger fails on CUDA but works on CPU. [colab](https://colab.research.google.com/drive/1n27_nl_NrOtP0H2qAngBVE2jQcvGi4Pa?usp=sharing)
Minimal reproduction:
```python
import torch
m = torch.nn.AvgPool2d(3, stride=2**31)
input = torch.randn(2... | true |
2,868,465,485 | [Intel GPU] Add SDPA implementation on XPU with OneDNN | DDEle | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/xpu"
] | 9 | CONTRIBUTOR | Add XPU implementation of OneDNN based SDPA operator. Will be integrated and enabled later.
Depends on BUILD_GRAPH switch in #147608
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 | true |
2,868,457,345 | [Minor] Fix minor mistake in docstring of replace_pattern | xwu99 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"release notes: fx",
"fx"
] | 4 | NONE | Fixes #147610
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,868,455,320 | Minor mistake in docstring of replace_pattern in torch/fx/subgraph_rewriter.py | xwu99 | closed | [
"module: docs",
"triaged"
] | 0 | NONE | def pattern(w1, w2):
return torch.cat([w1, w2]).sum()
def replacement(w1, w2):
return torch.stack([w1, w2])
it should not have extra `sum()` according to the following generated code:
def forward(self, x, w1, w2):
stack_1 = torch.stack([w1, w2])
sum_1 =... | true |
2,868,446,431 | Adapt test_misc.py to HPUs | amathewc | closed | [
"triaged",
"open source",
"topic: not user facing",
"module: dynamo"
] | 3 | CONTRIBUTOR | This PR is related to https://github.com/pytorch/pytorch/pull/145476 . That PR had two files (test_functions.py and test_misc.py) . test_functions was causing CI/rebase/merge issues and hence removed for now. This PR contains only test_misc.py.
This is a continuation of https://github.com/pytorch/pytorch/pull/1443... | true |
2,868,374,447 | [Intel GPU] Enable BUILD_GRAPH for xpu_mkldnn | DDEle | closed | [
"module: mkldnn",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/xpu"
] | 4 | CONTRIBUTOR | For preparation of OneDNN based XPU SDPA enabling.
cc @gujinghui @PenghuiCheng @XiaobingSuper @jianyuh @jgong5 @mingfeima @sanchitintel @ashokei @jingxu10 @min-jean-cho @yanbing-j @Guobing-Chen @Xia-Weiwen @snadampal | true |
2,868,317,440 | Deprecate sm70 for cuda 12.8 binary | tinglvv | closed | [
"triaged",
"open source",
"Merged",
"ciflow/binaries",
"ciflow/trunk",
"topic: not user facing"
] | 9 | COLLABORATOR | follow up for https://github.com/pytorch/pytorch/pull/146265/files, dropping sm_70 as well, since "Architecture support for Maxwell, Pascal, and Volta is considered feature-complete and will be frozen in an upcoming release."
https://github.com/pytorch/pytorch/issues/145570
cc @ptrblck @atalman @nWEIdia
| true |
2,868,275,720 | [ONNX] aten_pow_scalar failure on dynamo export with dynamic shapes | borisfom | closed | [
"module: onnx",
"triaged"
] | 13 | CONTRIBUTOR | ### 🐛 Describe the bug
Here, encountered this error when trying to export DiffusionTransformer module. Same module exported fine with no dynamic shapes:
```
Traceback (most recent call last):
File "/usr/local/lib/python3.12/dist-packages/torch/onnx/_internal/exporter/_core.py", line 519, in _handle_call_function... | true |
2,868,264,783 | [Docs] Add `OpDTypes.any_common_cpu_cuda_one` | shink | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 7 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
| true |
2,868,248,550 | RuntimeError when running profiler in a loop | HardysJin | open | [
"oncall: profiler"
] | 0 | NONE | ### 🐛 Describe the bug
Hi,
This bug not always happen, if I use profiler to export chrome trace in a loop, this is likely to happen.
Code:
```
import vllm
import torch
import time
def add_requests(llm, num_tokens=4096, batch_size=1, max_out_tokens=8 ):
print(f"start inference for batch_size[{batch_size}], num_... | true |
2,868,226,483 | [dtensor][cp] experiment: try e2e cp flex_attention | XilunWu | open | [
"oncall: distributed",
"topic: not user facing",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147603
* #147517
* #147516
* #147515
* #147514
* #145353
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,868,207,478 | UnsupportedOperatorError: Exporting the operator 'aten::_make_per_tensor_quantized_tensor ' to ONNX opset version 11 | wangqianscu | open | [
"module: onnx",
"triaged"
] | 3 | NONE | ### 🐛 Describe the bug
When I export the torch model to onnx by torch.onnx.export(...), it raise error: UnsupportedOperatorError: Exporting the operator 'aten::_make_per_tensor_quantized_tensor ' to ONNX opset version 11.
So I tried the opset 12, 17 it also not support.
Then I try to use custom ops:
```
def make_pe... | true |
2,868,164,569 | [CI] Reduce the AOT target list to reduce build time | chuanqi129 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/xpu"
] | 6 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,868,150,722 | DISABLED test_sdpa_rewriter_14_cuda (__main__.SDPAPatternRewriterCudaDynamicTests) | pytorch-bot[bot] | open | [
"module: rocm",
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: inductor"
] | 1 | NONE | Platforms: rocm
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_sdpa_rewriter_14_cuda&suite=SDPAPatternRewriterCudaDynamicTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/37581378760).
Over the past... | true |
2,868,144,188 | Fixed abnormal behavior of LazyLinear when using LayzLinear and load_state together | FFFrog | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 15 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147599
Update Points:
- Update the logic of ``initialize_parameters``
- Add new testcases
The ISSUE Related:
https://github.com/pytorch/pytorch/issues/147389 | true |
2,868,123,392 | Turn onnx functions into static | cyyever | closed | [
"oncall: jit",
"open source",
"Merged",
"release notes: jit"
] | 6 | COLLABORATOR | To avoid exposing ONNX symbols.
cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel | true |
2,868,111,928 | [Dynamo] WeakRefVariable doesn't use the most updated python referent when call_function is executed | yanboliang | open | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
Repro:
```
import torch
@torch.compile(backend="eager", fullgraph=True)
def fn(y):
obj = torch.tensor([1.0, 2.0])
weak_ref = weakref.ref(obj)
if weak_ref() is None:
a = y + 1
else:
a = y - 1
del obj
if weak_ref() is None:
b = y + 1
else:
... | true |
2,868,089,014 | The performance of model with TP worse than without TP in CPU | jiqing-feng | closed | [
"oncall: distributed",
"triaged"
] | 21 | NONE | ### 🐛 Describe the bug
model: meta-llama/Llama-3.1-8B-Instruct
input shape: [1, 512]
latency is forward latency
instance: Intel 4th Gen Xeon SPR (1 numa node for 1 socket)
base image: gar-registry.caas.intel.com/pytorch/pytorch-ipex-spr:cpu-device
torch version:
intel_extension_for_pytorch 2.6.0
torch ... | true |
2,868,089,001 | Non-Determinism in Faster R-CNN Despite Setting All Deterministic Flags | mbar0075 | open | [
"triaged",
"module: determinism"
] | 0 | NONE | ### 🐛 Describe the bug
I am encountering a `RuntimeError` when running Faster R-CNN with `torch.use_deterministic_algorithms(True)`. Despite setting all known deterministic flags, the following error persists:
```
RuntimeError: roi_align_backward_kernel does not have a deterministic implementation, but you set 'torc... | true |
2,868,069,405 | [Dtensor] Pass device information in OffsetBasedRNGTracker | ankurneog | open | [
"oncall: distributed",
"triaged",
"open source",
"Stale",
"topic: not user facing"
] | 8 | CONTRIBUTOR | Fixes https://github.com/pytorch/pytorch/issues/147584
```OffsetBasedRNGTracker``` called without arguments will set default device type to cuda
https://github.com/pytorch/pytorch/blob/533b884870acd951e684e0bf551eb76904dec047/torch/distributed/tensor/_random.py#L105
cc @H-Huang @awgu @kwen2501 @wanchaol @feg... | true |
2,868,046,768 | Define USE_C10D_XCCL and USE_XCCL in pytorch | Chao1Han | open | [
"open source",
"release notes: xpu"
] | 23 | CONTRIBUTOR | ### Motivation:
Add `USE_XCCL` and `USE_C10D_XCCL` to enable support of XCCL backend building in stock PyTorch, similar to `USE_NCCL` and `USE_C10D_NCCL`.
By default, `USE_XCCL` is OFF and allowed set to ON explicitly. | true |
2,868,019,075 | Fix log2, PowByNatural printing | isuruf | closed | [
"module: cpu",
"open source",
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 6 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147592
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @voznesenskym @penguinwu @EikanWang @Guobing-Chen @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauh... | true |
2,867,991,595 | [outdated][experimental] delayed compile | bobrenjc93 | closed | [
"ciflow/trunk",
"release notes: fx",
"fx",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147591
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv @voznesenskym @penguinwu @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @jiayisunx @chenyang78 @kadeng @chauhang @amjames
Differential Revision: [D69996869](https:/... | true |
2,867,990,997 | [cutlass backend] cache_clear algorithm select cache on fresh inductor cache | henrylhtsang | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 8 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147590
Differential Revision: [D69959917](https://our.internmc.facebook.com/intern/diff/D69959917/)
AlgorithmSelectorCache is a cache. The expectation is that when we force disable cache + clear inductor caches, it would be clear... | true |
2,867,986,203 | check if force_disable_caches before using precompile cache | henrylhtsang | closed | [
"fb-exported",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147589
Differential Revision: [D69966889](https://our.internmc.facebook.com/intern/diff/D69966889/)
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf22... | true |
2,867,982,584 | Also support non-contiguous activation for torch._weight_int8pack_mm on CPU | sanchitintel | closed | [
"module: cpu",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"intel"
] | 9 | COLLABORATOR | ### Problem
Non-contiguous activation for `torch._weight_int8pack_mm` is unsupported on CPU.
So, with int8 WoQ with B16 activation with torchao, for batch-size 2 & above, an assertion is hit regarding non-contiguous A being unsupported. Such an issue was encountered with LLaMA models.
### Solution
Also support no... | true |
2,867,981,679 | Add unique kernel name support for user defined triton kernel | muchulee8 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Summary:
Add unique_user_kernel_names which mimics what unique_kernel_names do, but for user defined Triton kernels.
This does rewrite the copied kernel src, and modifies non-Inductor generated code, so we split it out from unique_kernel_names, where we have more control over all namings and generations.
Test Plan: On... | true |
2,867,975,674 | [cutlass backend] clear_on_fresh_inductor_cache when generatings cutlass ops | henrylhtsang | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147586
Differential Revision: [D69966732](https://our.internmc.facebook.com/intern/diff/D69966732/)
This is needed if we want to generate cutlass ops with different instantiation level in one session.
cc @voznesenskym @pengu... | true |
2,867,958,778 | [SDPA backward error]Error detected in ScaledDotProductEfficientAttentionBackward0 when input seqlen is very long and with attn_mask | tianyan01 | open | [
"triaged",
"module: sdpa"
] | 0 | NONE | ### 🐛 Describe the bug
Here is the minimal example. When I set the seqlen=53936, and input a attn_mask, it will send me an error "Error detected in ScaledDotProductEfficientAttentionBackward0". But when I set seqlen=46344, or remove the attn_mask, it will run ok. The threshold of the seqlen is 46344, once seqlen > 46... | true |
2,867,942,439 | [Distributed Tensor]OffsetBasedRNGTracker instantiation always try to create with CUDA backend | dayanandav | closed | [
"oncall: distributed",
"triaged",
"module: dtensor"
] | 2 | NONE | ### 🐛 Describe the bug
OffsetBasedRNGTracker create with always CUDA backend and cause problem when try to create with other backend(HPU)
[random._rng_tracker = random.OffsetBasedRNGTracker()](https://github.com/pytorch/pytorch/blob/5ef94ca8162c541bced46ecd4e31dfd9d524ac51/torch/distributed/tensor/_api.py#L1028) t... | true |
2,867,903,495 | [import][inductor] Simplify grid handling | jansel | open | [
"module: rocm",
"fb-exported",
"Merged",
"Reverted",
"Stale",
"ciflow/trunk",
"topic: not user facing",
"ciflow/mps",
"skip-pr-sanity-checks",
"module: inductor",
"ciflow/inductor",
"ciflow/xpu",
"ci-no-td",
"ciflow/inductor-rocm"
] | 28 | CONTRIBUTOR | 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 arg:
```py
kernel.run(a, b, xnumel, stream=stream0)
```
instead now the grid computation is ... | true |
2,867,854,134 | Refactor typing: Replace Any with ParamSpec for better type safety | devsashidhar | open | [
"oncall: distributed",
"triaged",
"open source",
"Stale",
"topic: not user facing"
] | 3 | NONE | Description
This PR refactors function signatures by replacing *args: Any and **kwargs: Any with ParamSpec to improve type safety and preserve argument information. This enhances the ability of static type checkers like mypy to provide better error detection and improves code maintainability.
Motivation
Many funct... | true |
2,867,827,953 | cpp libtorch transformerimpl lack some parameter between with python pytorch | mullerhai | open | [
"module: cpp",
"module: nn",
"triaged"
] | 0 | NONE | ### 🐛 Describe the bug
Hi,
I find libtorch some layer impl not the same as python pytorch ,like transformer layer
https://pytorch.org/docs/stable/generated/torch.nn.Transformer.html
in python
Transformer
CLASStorch.nn.Transformer(d_model=512, nhead=8, num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=2... | true |
2,867,798,923 | Fix issue #146018: Improve CachingAutotuner handling | devsashidhar | open | [
"triaged",
"open source",
"Stale",
"topic: not user facing"
] | 5 | NONE | Fixes #146018
### Summary:
This PR addresses issue #146018 where `CachingAutotuner` fails when running on the `meta` device due to size inference issues. The fix ensures that dynamic shape handling works correctly when multiple calls with different tensor sizes are made.
### Changes:
- Improved handling of `Cac... | true |
2,867,782,426 | [Easy][optim] Add LBFGS params optional desc | zeshengzong | closed | [
"open source",
"Merged",
"ciflow/trunk",
"release notes: optim"
] | 3 | CONTRIBUTOR | [LBFGS docs](https://pytorch.org/docs/stable/generated/torch.optim.LBFGS.html#torch.optim.LBFGS) missing `optional` description for params in compare with other optimizer docs, like [Adam](https://pytorch.org/docs/stable/generated/torch.optim.Adam.html)
## Test Result
### Before
![image](https://github.com/use... | true |
2,867,740,968 | [distributed] Register sharding strategy for aten.amax.default to support float8 rowwise scaling | danielvegamyhre | closed | [
"oncall: distributed",
"triaged",
"module: dtensor"
] | 0 | CONTRIBUTOR | **Summary**
While debugging an [issue](https://github.com/pytorch/torchtitan/issues/864) in torchtitan related to float8 with rowwise scaling + async TP + torch.compile, I found a different issue:
With eager mode + float8 rowwise + vanilla TP, we get a different error:
`Operator aten.amax.default does not have a sha... | true |
2,867,697,013 | demo myst_nb with compile tutorial | williamwen42 | open | [
"Stale"
] | 4 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147577
| true |
2,867,689,045 | [ONNX][demo] Rotary embedding | justinchuby | open | [
"open source",
"Stale",
"release notes: onnx"
] | 4 | COLLABORATOR | This change gives users the ability to use onnx ops directly with `torch.ops.onnx.*` and showcases an implementation for RotaryEmbedding. The operators are native pytorch which play well with the ecosystem. | true |
2,867,680,043 | ncclUnhandledCudaError | youreternity1997 | closed | [
"oncall: distributed",
"module: c10d"
] | 0 | NONE | ### 🐛 Describe the bug
| true |
2,867,671,792 | [export] don't use unbacked_renamings in export | pianpwk | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"fx",
"ciflow/inductor",
"release notes: export"
] | 8 | CONTRIBUTOR | Plan: avoid the use of unbacked renamings, and introduce a pass run in `_produce_aten_artifact` that recomputes unbacked bindings. Decided to do this because in we don't serialize unbacked renamings (or any ShapeEnv state), so this used to compose poorly with de/serialization. This hopefully establishes the invariant t... | true |
2,867,667,255 | export method | avikchaudhuri | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: export"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147573
The `export` API takes a `nn.Module` and traces its `forward` method. However sometimes it is useful to export different methods of a `nn.Module`, either as a one-off for debugging or as a set of methods that are called in ... | true |
2,867,661,880 | [dynamo] Support reads to global/captured tensors in `nonstrict_trace`-ed function | StrongerXi | 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):
* __->__ #147572
* #147571
* #146950
* #146367
* #146714
As title. Without this patch we get the following error:
Tweaking the `allow_non_fake_inputs` flag on tensor mode doesn't quite
work for AOTAutograd, which also needs to fake-tensor... | true |
2,867,661,811 | [dynamo] Support `nonstrict_trace` on class method | StrongerXi | closed | [
"Merged",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147572
* __->__ #147571
* #146950
* #146367
* #146714
As title, also see
1. new test `test_nonstrict_trace_on_method` for example.
2. newly added comments for why we need special treatment on methods.
cc @voznesenskym @penguinwu @EikanW... | true |
2,867,646,768 | `view()` + modify-in-place fails silently with DTensor | ad8e | open | [
"oncall: distributed",
"triaged",
"module: dtensor"
] | 4 | CONTRIBUTOR | ### 🐛 Describe the bug
Run command on 2-GPU machine: `torchrun --standalone --nnodes=1 --nproc-per-node=2 my_file.py`
```
import torch
import torch.nn as nn
from torch.distributed._tensor import DTensor, Shard, Replicate, distribute_tensor, distribute_module, init_device_mesh
from torch.distributed._composable.fsdp i... | true |
2,867,636,366 | constexpr all the things in irange.h | swolchok | closed | [
"fb-exported",
"ciflow/trunk",
"topic: not user facing"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147569
I got complaints while irangeifying some files in ExecuTorch
that irange could not be used in a constexpr function. This made the
complaints go away.
I added a constexpr function in irange_test that used to fail to build
with... | true |
2,867,626,074 | `copy_()` fails with HSDP in FSDP2 | ad8e | open | [
"oncall: distributed",
"triaged",
"module: fsdp",
"module: dtensor"
] | 4 | CONTRIBUTOR | ### 🐛 Describe the bug
Run on a 2-GPU machine: `torchrun --standalone --nnodes=1 --nproc-per-node=2 this_file.py`
```
import torch
from torch.distributed._tensor import DTensor, Shard, Replicate, distribute_tensor, distribute_module, init_device_mesh
from torch.distributed._composable.fsdp import fully_shard, MixedPr... | true |
2,867,624,771 | [cond] support mismatched output in inductor | ydwu4 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147567
In this PR, we extract `codegen_unbacked_symbol_defs` of FallbackKernel out as a `codegen_unbacked_symbol_defs_for_outputs` method in wrapper. With it, HOPs can support the case where the subgraph returns a tensor with unbac... | true |
2,867,590,155 | Add support for non functional collectives under FakeTensorMode and fake_pg for memory tracking | sanketpurandare | closed | [
"oncall: distributed",
"triaged",
"open source",
"Merged",
"release notes: distributed (c10d)"
] | 5 | CONTRIBUTOR | This PR adds support for non-functional collectives under `FakeTensorMode` and `fake_pg`. It helps eliminate the patching of collectives for memory and runtime estimation.
It also modifies the `ModTracker` to enable the post-backward hook call for modules whose inputs don't require gradients but parameters do.
... | true |
2,867,589,930 | [dynamo][checkpoint] non-reentrant checkpoint + ambient saved tensor hooks is silently incorrect | xmfan | open | [
"module: activation checkpointing",
"triaged",
"oncall: pt2",
"module: dynamo",
"module: higher order operators",
"module: pt2-dispatcher"
] | 0 | MEMBER | ### 🐛 Describe the bug
```python
# test/test_autograd.py:test_save_on_cpu_and_checkpoint
a = torch.randn(2, 2, requires_grad=True)
with torch.autograd.graph.save_on_cpu():
h = a.pow(2)
h = checkpoint(lambda x: x.pow(2).pow(2), h, use_reentrant=False)
# h = checkpoint(torch.compile(la... | true |
2,867,527,259 | [Inductor][NFC] Remove unused functions from `compile_tasks.py` | anmyachev | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 11 | COLLABORATOR | cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,867,507,261 | torch.distributed.elastic.multiprocessing.start_process description does not reflect API | ntw-au | open | [
"oncall: distributed",
"triaged",
"module: elastic"
] | 1 | NONE | ### 📚 The doc issue
The `tee` parameter to `torch.distributed.elastic.multiprocessing.start_process()` was removed in #120691 and released in PyTorch 2.3.0. However, the [2.3 documentation](https://pytorch.org/docs/2.3/elastic/multiprocessing.html#torch.distributed.elastic.multiprocessing.start_processes) (and subseq... | true |
2,867,485,736 | [dynamo] Save/restore system random state more carefully [attempt 3] | williamwen42 | open | [
"module: dynamo",
"ciflow/inductor"
] | 3 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147562
Attempt 3 at https://github.com/pytorch/pytorch/issues/145329
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,867,471,025 | [partitioner] always ban compiler-driven recompute of collectives by default | bdhirsh | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: distributed (miscellaneous)"
] | 6 | CONTRIBUTOR | This should fix the hang in https://fb.workplace.com/groups/1075192433118967/permalink/1603268720311333/
The argument here is that:
(1) in general, it is not safe for the partitioner to sometimes choose to recompute collectives in the backward. Why? If we are running a distributed job, where many ranks are compil... | true |
2,867,465,431 | Fix import of getArtifactLogger for ir_pre_fusion and ir_post_fusion | dulinriley | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Fixes #147002
There was an issue with the previous PR https://github.com/pytorch/pytorch/pull/147248 that didn't show up in CI,
where a logging import was not complete in torch/_inductor/debug.py before importing it.
This only happened if someone directly imported the file without doing any other imports before.
... | true |
2,867,460,817 | [inductor][subgraph] Plumbing to get ShapeAsConstantBuffer from subgraph to main graph output | anijain2305 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #139325
* __->__ #147559
I am unable to create a test case that fails without the next PR. The idea is to have a symint which is returned by the inner subgraph and then returned by the forward graph after partitioning.
cc @voznesenskym... | true |
2,867,425,685 | [export] Remove report from draft-export output | angelayi | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: export"
] | 6 | CONTRIBUTOR | Summary: This matches the export API. To print the report, people can just do `print(ep._report)`. This information is also displayed in the terminal after the draft_export call.
Test Plan: CI
Reviewed By: SherlockNoMad
Differential Revision: D69689154
| true |
2,867,414,339 | use statically_known_true instead of guard_size_oblivious in pattern matcher | bobrenjc93 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 14 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147557
We shouldn't add guards here. Use statically_known_true instead. Internal xref: https://fb.workplace.com/groups/1075192433118967/?multi_permalinks=1609560723015466&comment_id=1610040026300869¬if_id=1740082892544333¬if_t... | true |
2,867,371,825 | [caffe2] Ignore compiler option when building using clang | Nicoshev | closed | [
"module: cpu",
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"merging"
] | 10 | CONTRIBUTOR | Summary:
Skip adding unrecognized option optimize("-fno-tree-loop-vectorize") when building using clang
This piece of code began to be compiled after armv9a has been set as default compilation profile
Test Plan: buck2 run mode/opt -c python.package_style=inplace -c fbcode.enable_gpu_sections=true -c fbcode.platform01... | true |
2,867,360,966 | [codemod] Fix unused-value issue in caffe2/aten/src/ATen/cuda/detail/CUDAHooks.cpp +4 | r-barnes | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: cpp",
"topic: improvements",
"topic: not user facing"
] | 13 | CONTRIBUTOR | Summary:
LLVM has a warning `-Wunused-value` which we treat as an error because it's so often diagnostic of a code issue. Unused values often indicate a programming mistake, but can also just be unnecessary cruft that harms readability and performance.
For questions/comments, contact r-barnes.
- If you approve of th... | true |
2,867,347,134 | [cutlass backend] Fix standalone runner test after swizzle became a runtime parameter | henrylhtsang | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147554
Differential Revision: [D69945114](https://our.internmc.facebook.com/intern/diff/D69945114/)
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf22... | true |
2,867,337,196 | ROCm MX-FP8 Gemm | petrex | open | [
"module: rocm",
"module: mkldnn",
"open source"
] | 3 | CONTRIBUTOR | TLDR: MX-FP8 matrix multiplications through hipblaslt (require AMD gfx950 && ROCm 6.5+)
This pull request introduces several changes to enhance support for the MX format on ROCm, particularly for the gfx950 device. Key changes include adding validation for matrix dimensions and setting block sizes for the MX format... | true |
2,867,329,276 | Fix sympy float priting | isuruf | closed | [
"module: cpu",
"open source",
"Merged",
"ciflow/trunk",
"module: dynamic shapes",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 6 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147552
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @ezyang @penguinwu @bobrenjc93 @voznesenskym @EikanWang @Guobing-Chen @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @... | true |
2,867,308,530 | FlexAttention compiled has illegal memory access or device-side assert even though all tensors are contiguous | leijurv | open | [
"triaged",
"oncall: pt2",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention"
] | 19 | NONE | ### 🐛 Describe the bug
```python
import torch
import torch.nn.attention.flex_attention
flex_compiled = torch.compile(torch.nn.attention.flex_attention.flex_attention)
torch.set_default_device("cuda")
print(torch.__version__)
if False:
# these params trigger device-side assert:
BATCH = 64
HEADS = 64
S... | true |
2,867,304,220 | Define `__all__` for `torch.utils.tensorboard` | ringohoffman | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: python_frontend"
] | 8 | CONTRIBUTOR | Fixes the issue:
```python
import torch.utils.tensorboard
torch.utils.tensorboard.FileWriter # pyright: "FileWriter" is not exported from module "torch.utils.tensorboard"
torch.utils.tensorboard.RecordWriter # pyright: "RecordWriter" is not exported from module "torch.utils.tensorboard"
torch.utils.tensorboard... | true |
2,867,289,003 | Enable strobelight profiling specific compile frame ids using COMPILE_STROBELIGHT_FRAME_FILTER | laithsakka | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147549
* #147547
running python test/strobelight/examples/compile_time_profile_example.py
```
strobelight_compile_time_profiler, line 123, 2025-02-20 14:08:08,409, INFO: compile time strobelight profiling enabled
strobelight_com... | true |
2,867,288,053 | torch._scaled_mm with MXFP8 | vkuzo | closed | [
"module: cuda",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"ci-no-td"
] | 27 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147548
# summary
Add blockwise MXFP8 support to `torch._scaled_mm` on CUDA capability 10.0 and higher devices. If the scales for A and B are of dtype `torch.float8_e8m0fnu`, we dispatch to the blockwise kernel from cuBLAS.
Th... | true |
2,867,277,682 | move _strobelight/example to avoid graph breaks | laithsakka | closed | [
"Merged",
"topic: not user facing"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147549
* __->__ #147547
| true |
2,867,270,879 | Add continuous run for cachebench | oulgen | closed | [
"Merged",
"ciflow/trunk",
"release notes: releng"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147546
* #147537
This PR adds a continuous run for cache bench. | true |
2,867,269,167 | [MPS] fix attention for >4d tensors | Isalia20 | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: bug fixes",
"release notes: mps",
"ciflow/mps"
] | 3 | COLLABORATOR | Fixes #147443
and adds tests for >4d tensors | true |
2,867,234,530 | Adding Small Epsilon in linalg_eig_backward to Improve Numerical Stability on GPU | alexanderlerner | closed | [
"module: autograd",
"triaged",
"module: linear algebra"
] | 6 | NONE | ### 🚀 The feature, motivation and pitch
Hi PyTorch Team,
My team and I work on physics-inspired ML models where we use torch.linalg.eigh to get the eigenvector corresponding to the lowest eigenvalue of a Hermitian matrix. We sometimes run into numerical issues during backpropagation as we repeat training iterations ... | true |
2,867,180,616 | No gradient for `residuals` in the return value of `torch.linalg.lstsq` | Bichidian | closed | [
"module: autograd",
"triaged",
"module: linear algebra"
] | 6 | CONTRIBUTOR | The return value of `torch.linalg.lstsq` is a named tuple `(solution, residuals, rank, singular_values)`. I find that `solution` has gradient but `residuals` does not. Is this expected? I'm using `gels` driver.
cc @ezyang @albanD @gqchen @pearu @nikitaved @soulitzer @Varal7 @xmfan @jianyuh @mruberry @walterddr @xwang2... | true |
2,867,136,691 | Increase memory for linux binary builds | jeanschmidt | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"ci-no-td"
] | 11 | CONTRIBUTOR | Recently I detected that some linux manywheels builds are flaky ([ex](https://github.com/pytorch/pytorch/actions/runs/13438309056/job/37555475510)).
After investigating, could not detect issues when investigating the runner logs, its disk space available, network usage or CPU load. Unfortunately, memory information ... | true |
2,867,125,401 | Add XPU to is_compile_supported to support roi_align op in torchvision | frost-intel | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"release notes: xpu"
] | 6 | COLLABORATOR | Part of the required fix for https://github.com/intel/torch-xpu-ops/issues/1264.
To support `roi_align`, torchvision uses `is_compile_supported` in `torch/_dynamo/utils.py` to compile a non-deterministic version of the op for backwards passes. This PR adds XPU device to the supported compile devices.
The `is_com... | true |
2,867,109,918 | Update ruff linter for PEP585 | aorenste | closed | [
"Merged",
"ciflow/trunk",
"release notes: onnx",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | This turns on PEP585 enforcement in RUFF.
- Updates the target python version
- Stops ignoring UP006 warnings (PEP585)
- Fixes a few issues which crept into the tree in the last day
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147540
cc @voznesenskym @penguinwu @Ei... | true |
2,867,100,386 | GRU does not return reverse hidden states when bidirectional=True | amitportnoy | closed | [] | 0 | NONE | ### (Non-issue)
In the code below `output, (h_n, c_n) = gru(x)` is my bug, since GRU does not return c_n
closing this
### 🐛 Describe the bug
Using `torch==2.5.1`, GRU with `bidirectional=True`, does not return the reverse direction hidden state in `h_n`.
(LSTM will return those states, the issue is with GRU specif... | true |
2,867,088,021 | [fx] demote node prepend to self log from warning to debug | xmfan | closed | [
"Merged",
"ciflow/trunk",
"release notes: fx",
"fx"
] | 3 | MEMBER | FIXES https://github.com/pytorch/pytorch/issues/147175
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147538
This is harmless, not sure why this is a user warning. Writing reordering graph passes is more concise when we ignore this warning.
cc @ezyang @SherlockNoMad @Eik... | true |
2,867,065,999 | Add cachebench | oulgen | closed | [
"Merged",
"ciflow/trunk",
"release notes: benchmark",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147546
* __->__ #147537
This PR adds a new benchmark called cachebench in order to measure/demonstrate the prowess of PT2 caching.
```
python benchmarks/dynamo/cachebench.py --output="result.json"
```
cc @voznesenskym @penguinwu @Eik... | true |
2,867,064,519 | Fix PEP585 update | aorenste | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: dataloader",
"topic: not user facing"
] | 11 | CONTRIBUTOR | Summary: D69920347 causes a pyre failure due to changing a base object from typing.Iterable to abc.Iterable. For now revert that change until it can be dealt with on its own.
Test Plan:
failures from D69920347 pass locally
unit tests pass
Reviewed By: oulgen
Differential Revision: D69936518
| true |
2,867,062,781 | reland "[sigmoid] Test OSS model runner with test_export.py" | zhxchen17 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: export",
"ci-no-td"
] | 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
Differential Revision: D69937387
| true |
2,867,029,542 | specify only some dimensions in shapes collection | avikchaudhuri | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: export"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147534
Differential Revision: [D69936316](https://our.internmc.facebook.com/intern/diff/D69936316/) | true |
2,867,009,291 | Fix register constant to be usable in exportz | tugsbayasgalan | closed | [
"Merged",
"ciflow/trunk",
"release notes: fx",
"fx"
] | 8 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #147533
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv
Differential Revision: [D69939737](https://our.internmc.facebook.com/intern/diff/D69939737)
@diff-train-skip-merge | true |
2,867,009,203 | better error message | tugsbayasgalan | closed | [
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: export"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147533
* __->__ #147532
Differential Revision: [D69939736](https://our.internmc.facebook.com/intern/diff/D69939736) | true |
2,866,960,408 | FSDP wrapped module cannot be called with zero arguments | gkanwar | closed | [
"oncall: distributed",
"triaged",
"module: fsdp"
] | 1 | NONE | ### 🐛 Describe the bug
When calling an FSDP-wrapped torch module with zero arguments, an index error is thrown.
Reproducer code, which should be launched with an appropriate `torchrun`:
```
import torch
import torch.distributed
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
import os
class Mode... | true |
2,866,891,445 | [fx][dynamo][be] Don't allow arbitrary dataclass in the graph | StrongerXi | open | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
Right now Dynamo and fx tracing allows dataclass instances in the graph, represented as `call_function(dataclass_ctor, args...)`. Relevant PRs:
- #99576
- #134846
The issue is that dataclass constructor could have arbitrary user code.
More context: https://docs.google.com/document/d/1rgm7_tn... | true |
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