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 |
|---|---|---|---|---|---|---|---|---|
2,970,505,614 | [inductor][fix] enable dtype promotion for bucketize | eknag | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 18 | CONTRIBUTOR | Summary:
bucketization involves comparing an input with border values. Without careful consideration of dtypes, this can cause dangerous implicit casting.
aten.bucketize resolves this via dtype promotion. We enable dtype promotion for the inductor bucketization pass so as to maintain alignment with the aten op.
... | true |
2,970,491,481 | update get start xpu document for v2.7 | pytorchbot | closed | [
"open source"
] | 1 | COLLABORATOR | update get start xpu document for v2.7 | true |
2,970,473,554 | [ROCm] Expand workspace size for gfx95 | jpvillam-amd | closed | [
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/rocm"
] | 8 | CONTRIBUTOR | Use same workspace size for gfx95* as gfx94*
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,970,415,160 | [dynamo] disable new test_assert_failure_in_generic_ctx_mgr internally | williamwen42 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 3 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150631
* #150471
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,970,403,509 | DISABLED test_parity__foreach_abs_fastpath_inplace_cuda_int8 (__main__.TestForeachCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: mta"
] | 4 | NONE | Platforms: linux, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_parity__foreach_abs_fastpath_inplace_cuda_int8&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/39919872843).
... | true |
2,970,312,779 | torch.compile on MPS: rms_norm not invoked | manuelcandales | closed | [
"triaged",
"module: correctness (silent)",
"module: reductions",
"module: mps",
"oncall: pt2",
"module: inductor"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
When forcing torch.compile to use the fused rms_norm MPS implementation, it doesn't do it
One way to notice the bug, is to see that the compiled model outputs zeros, instead of the correct values:
```python
import torch
with torch.no_grad():
x = torch.randn(1024, requires_grad=False, devic... | true |
2,970,197,455 | Release 2.7.0 validations checklist and cherry-picks | atalman | closed | [
"oncall: releng",
"triaged"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
Similar to https://github.com/pytorch/pytorch/issues/144503
We need to make sure that:
- [x] Validate Linux aarch64 CUDA builds with triton (Please note all CUDA Aarch64 builds where validated by Nvidia)
- [x] Python 3.13 and 3.13t wheel validate - https://github.com/pytorch/test-infra/acti... | true |
2,970,181,890 | Make error message descriptive | sibuachu | open | [
"oncall: distributed",
"fb-exported",
"release notes: distributed (sharded)"
] | 5 | NONE | Summary: Adding the number of locals shards to error messages makes it easier to debug.
Test Plan: UT
Differential Revision: D72396478
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k | true |
2,970,135,079 | Refactor `torch/utils/data/datapipes/gen_pyi.py` with `torchgen` | XuehaiPan | open | [
"open source",
"topic: not user facing",
"suppress-bc-linter"
] | 5 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150732
* #150731
* #150730
* __->__ #150626
* #150729
* #150728
* #150727
* #150726
| true |
2,970,102,392 | [cuda] Add new faster gammabeta backward kernel (#148605) (Reapply with launch bounds) | ahmadsharif1 | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: nn",
"ci-no-td"
] | 9 | CONTRIBUTOR | # Changes over the previous PR
This reverts commit 61a1f09 and adds `__launch_bounds__` to the kernel.
Previously I merged 114d404 that did not work on Blackwell because it consumed too many registers. It got reverted in 61a1f09. For more context see: https://github.com/pytorch/pytorch/issues/150266.
This PR r... | true |
2,970,051,270 | DISABLED test_special_polygamma_cpu_halide (__main__.HalideCpuTests) | clee2000 | open | [
"triaged",
"skipped",
"oncall: pt2",
"module: inductor"
] | 2 | CONTRIBUTOR | Platforms: linux
Example
https://hud.pytorch.org/pytorch/pytorch/commit/70b34a42c17cecd316487dc574dce3b8121270cc#39929315792-box
Broke some time when the halide build was failing due to cmake issues, so I don't know when it started
This test was disabled because it is failing on main branch ([recent examples](https... | true |
2,970,050,448 | Division by zero in ONNX export with `dynamo=True` leading to NaN outputs | novikov-alexander | open | [
"module: onnx",
"triaged"
] | 3 | NONE | ### Description
When converting [3DMOTFormer](https://github.com/dsx0511/3DMOTFormer) to ONNX using `torch.onnx.export`:
- With `dynamo=False`: Conversion succeeds and model works correctly
- With `dynamo=True`:
- Conversion succeeds but produces invalid ONNX models when input has many tracks (`tracks_in > 200`)
-... | true |
2,969,997,018 | `torch.compile` creates a CUDA context even for CPU based code | antoinebrl | open | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 4 | CONTRIBUTOR | ### 🐛 Describe the bug
Hello 👋! I attempted to use `torch.compile` on a simple code snippet intended for CPU execution in a multi-processing environment. However, I noticed that `torch.compile` allocates GPU memory whenever CUDA is available, even if the execution is strictly on the CPU. When used with multi-process... | true |
2,969,818,076 | 2.8.0 Nightly - "Feature 'cvt with .bf16.f16' requires .target sm_90 or higher" | scottmudge | open | [
"triaged",
"oncall: pt2",
"module: inductor",
"upstream triton"
] | 2 | NONE | ### 🐛 Describe the bug
Versions of 2.8.0 nightly **after** around ~2.8.0.dev20250326+cu128 are causing this issue during torch compile (inductor):
```
E0403 10:21:19.865000 339 venv/lib/python3.13/site-packages/torch/_inductor/runtime/triton_heuristics.py:617] ptxas /tmp/tmpg_4sx8hs.ptx, line 705; error : Feature ... | true |
2,969,686,747 | Update expected results for pr_time_benchmarks | atalman | closed | [
"module: dynamo",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Followup after revert : https://github.com/pytorch/pytorch/pull/150572 expected tests results need to be updated
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,969,638,978 | README: anaconda license violation / no longer recommend anaconda since it's no longer free to use | morotti | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 9 | CONTRIBUTOR | hello,
I was going over the documentation to build pytorch from source.
Unfortunately, the first thing that come up is that you strongly recommend to use anaconda, which shouldn't be used because it's no longer free to use.
Could you please remove that from the doc?
I don't know if you are aware but anaconda... | true |
2,969,576,332 | `TensorBase.type()` may forget some features of previous Tensor | ILCSFNO | closed | [
"module: docs",
"triaged",
"actionable",
"module: tensor creation"
] | 8 | CONTRIBUTOR | ### 🐛 Describe the bug
I found that `.type()` can forget some features of previous Tensor, which I have found is `requires_grad`!
See the repro below, it can run well!
### Repro 1
```python
import torch
window_length = 10
window1 = torch.bartlett_window(window_length, requires_grad=True)
window2 = window1.type(torch... | true |
2,969,494,686 | DISABLED test_parity__foreach_abs_fastpath_inplace_cuda_int64 (__main__.TestForeachCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: mta"
] | 4 | NONE | Platforms: linux, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_parity__foreach_abs_fastpath_inplace_cuda_int64&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/39905713953).... | true |
2,969,442,328 | Why fill dtype of Tensor with torch.tensortype can work well | ILCSFNO | closed | [
"triaged",
"module: python frontend"
] | 5 | CONTRIBUTOR | ### 🐛 Describe the bug
Here is a normal usage of `TensorBase.type()` by `torch.bartlett_window`:
### Repro 1
```python
import torch
window_length = 10
window1 = torch.bartlett_window(window_length, requires_grad=True)
window2 = window1.type(torch.long)
print(window1)
print(window2)
```
### Output 1
```txt
tensor([0.0... | true |
2,969,391,508 | DDP and multi-GPU related issue | WenHuiShen-Bio | closed | [] | 0 | NONE | I am working on graph similarity prediction using the SimGNN model. Since SimGNN requires input as pairs of graphs, I cannot use PyTorch's DataLoader to batch multiple graphs together efficiently. As a result, my GPU utilization is only around 10% per GPU, and I am using 4 GPUs for multi-GPU training.
To improve GPU u... | true |
2,969,335,276 | conv2d fp8 support | sipie800 | open | [
"module: convolution",
"triaged",
"enhancement",
"module: float8"
] | 0 | NONE | ### 🚀 The feature, motivation and pitch
It seems that torch supports fp8 nn.linear now. Any plans to support fp8 nn.Conv2d?
### Alternatives
_No response_
### Additional context
_No response_
cc @yanbing-j @vkuzo @albanD @kadeng @penguinwu | true |
2,969,293,223 | Torch compile for `torch.searchsorted` failed when capturing scalar outputs, with scalar `values` taken from `sorted_sequence` | HollowMan6 | open | [
"triaged",
"oncall: pt2",
"module: dynamic shapes",
"module: decompositions"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
This is related to the extraction of specialized integers from data-dependent expressions.
A minimal reproducer:
```python
import torch
torch._dynamo.config.capture_scalar_outputs = True
def test_demo(sorted_seq: torch.Tensor):
return torch.searchsorted(sorted_seq, sorted_seq[-1].item()... | true |
2,969,204,886 | RAM leak during data loading with multiprocessing and Conv3d on CPU in Dataset __getitem__ | ilyas-sirazitdinov-snkeos | open | [
"module: dataloader",
"module: cpu",
"module: memory usage",
"triaged",
"module: mkldnn",
"module: data"
] | 7 | NONE | ### 🐛 Describe the bug
I have the following use case:
* My custom PyTorch Dataset receives a 3D tensor (these tensors have various shapes).
* It applies Gaussian blur preprocessing on the CPU.
* It returns the processed tensor.
* To speed up processing, I want to use multiprocessing.
The Python snippet below sim... | true |
2,968,886,844 | Fix codegen, change str comparison opeator to == for proper equality … | jgrzybek-habana | closed | [
"open source",
"Merged",
"ciflow/trunk",
"release notes: fx",
"fx"
] | 7 | CONTRIBUTOR | cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,968,853,387 | `weight_decay` etc. works contradictory to `params` without grad | ILCSFNO | closed | [
"module: autograd",
"module: optimizer",
"triaged"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
The docs of [torch.optim.Adam()](https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam), [torch.optim.AdamW()](https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW) and [torch.optim.RAdam()](https://pytorch.org/docs/stable/generated/torch... | true |
2,968,636,428 | [Easy] Add `output_size` in forward method of ConvTranspose2d | zeshengzong | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: nn",
"topic: docs"
] | 10 | CONTRIBUTOR | Fixes #74593
Add description for `forward` in [ConvTranspose2d](https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html) doc
## Test Result
| true |
2,968,599,277 | Refactoring: fix the python constant check | FFFrog | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 7 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150608
As the title stated.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,968,571,714 | `torch.jit.script` does not respect `torch.set_default_dtype` | defaultd661 | open | [
"oncall: jit"
] | 0 | NONE | # Bug 1: `torch.jit.script`
### 🐛 Describe the bug
When scripting a function that returns an empty tensor, the scripted function does not respect the default dtype set by `torch.set_default_dtype`. Instead, it returns a tensor with torch.float32, even when the expected dtype should be torch.float64.
To reproduce:
``... | true |
2,968,542,345 | Make `nn.MultiLabelMarginLoss` error message user friendly | zeshengzong | open | [
"triaged",
"open source",
"release notes: nn"
] | 1 | CONTRIBUTOR | Fixes #106011
## Test Result
```python
import torch
import torch.nn as nn
input_tensor = torch.rand([20, 9, 20])
target_tensor = torch.rand([2, 2, 2, 2, 2, 2, 2])
loss_function = nn.MultiLabelMarginLoss()
loss = loss_function(input_tensor, target_tensor)
RuntimeError: Expected input tensor to ha... | true |
2,968,535,490 | [INDUCTOR] Explanation: Backend compiler `inductor` failed with aten._loc al_scalar_dense.default | jiqing-feng | closed | [
"oncall: pt2",
"oncall: cpu inductor"
] | 5 | NONE | ### 🐛 Describe the bug
Backend compiler `inductor` failed with aten._loc al_scalar_dense.default
To reproduce
```python
import torch
from optimum.quanto import ActivationQBytesTensor, absmax_scale, qint8, quantize_activation
device = torch.device("cpu")
input_shape = (10, 32, 32)
a = torch.randn(input_shape).to(dev... | true |
2,968,517,052 | Development docker image contains extra conda PyTorch installation | stevenlele | closed | [] | 2 | NONE | It should contain the built-from-source version (in `/opt/conda/lib/python3.*/site-packages/`) only, but the conda installation (`/opt/conda/pkgs/pytorch*`) is still there. It's because the `COPY --from` directive does not actually override the target folder - it merges them.
https://github.com/pytorch/pytorch/blob/fc... | true |
2,968,508,956 | [Inductor][CPU] Add GEMM templates for _weight_int4pack_mm_for_cpu with AMX | Xia-Weiwen | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"intel",
"module: inductor",
"ciflow/inductor"
] | 5 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150603
**Summary**
It's part of the task to enable max-autotune with GEMM template for WoQ INT4 GEMM on CPU.
This PR adds AMX-based GEMM templates for `torch.ops.aten_weight_int4pack_mm_for_cpu`. It brings performance benefits o... | true |
2,968,508,046 | DISABLED test_parity__foreach_abs_fastpath_inplace_cuda_int32 (__main__.TestForeachCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: mta"
] | 5 | NONE | Platforms: linux, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_parity__foreach_abs_fastpath_inplace_cuda_int32&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/39889618952).... | true |
2,968,442,338 | Profiler with record_shapes=True and deterministic algorithms enabled causes crash with FlashAttention | JungHoyoun | open | [
"high priority",
"triage review",
"module: crash",
"module: determinism",
"oncall: profiler",
"module: sdpa"
] | 1 | NONE | ### 🐛 Describe the bug
When using `torch.profiler.profile(record_shapes=True)` with `torch.use_deterministic_algorithms(True)`, calling `scaled_dot_product_attention` with `SDPBackend::flash_attention` crashes.
This seems to happen only when both profiler shape recording and deterministic mode are on.
---
### 🔁 *... | true |
2,968,399,098 | [CI][docker] Use install_cusparselt when possible in docker image | clee2000 | closed | [
"Merged",
"ciflow/binaries",
"topic: not user facing"
] | 3 | CONTRIBUTOR | spot checked builds for line like `Found CUSPARSELT: /usr/local/cuda/lib64/libcusparseLt.so`. I don't know if there's another way to do it
I am slowly trying to reduce the duplicated code in docker image installs
Pros:
* less dup code
Cons:
* more docker copies | true |
2,968,286,459 | test 2 | laithsakka | open | [] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150599
| true |
2,968,266,535 | Added A Error Handling Block Around Recovering DynamicLayerStack | zero000064 | closed | [
"triaged",
"open source",
"topic: not user facing",
"module: dynamo"
] | 3 | CONTRIBUTOR | Fixes #149801
Added an error handling for DynamicLayerStack to ensure if it's recovered as desired, if not , raise that exception.
In the finally part, https://github.com/pytorch/pytorch/blob/6470b373c16017f5cb8f1aa4060bb60632b18160/torch/_dynamo/eval_frame.py#L675, pop-up method is called to recover the DynamicL... | true |
2,968,265,301 | [training] Adding NUMA support for pytorch | efiks | open | [
"oncall: distributed",
"fb-exported",
"release notes: distributed (c10d)"
] | 12 | CONTRIBUTOR | Test Plan:
build and run tests for modified libraries locally
buck2 build arvr/mode/platform010/opt //xplat/caffe2:pytorch_ovrsource
buck run arvr/mode/win/debug-md -c python.package_style=inplace //xplat/caffe2:pytorch_test_ovrsource
buck test arvr/mode/linux/opt -c python.package_style=inplace //xplat/caffe2:pytor... | true |
2,968,178,341 | Fix nn.LazyModuleMixin examples | zeshengzong | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: nn",
"topic: docs"
] | 7 | CONTRIBUTOR | Fixes #150404
## Test Result
| true |
2,968,112,619 | suppress neon missing message on armv8 build | nihui | open | [
"triaged",
"open source",
"topic: not user facing"
] | 2 | CONTRIBUTOR | null | true |
2,968,102,254 | Add debug_lines of FXGraphCacheKey to AOTAutogradCacheEntry | jamesjwu | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 12 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150594
Previously we didn't save debug_lines because it's pretty large, but compared to the size of FXGraphCache entries it's still pretty small. So let's add it to AOTAutogradCache for easier debugability.
Differential Revision: [D... | true |
2,968,068,167 | Make LazyModuleMixin materialize after load_state_dict | zeshengzong | open | [
"triaged",
"open source"
] | 4 | CONTRIBUTOR | Fixes #73009
## Test Result
```bash
pytest -s test/nn/test_lazy_modules.py
```
| true |
2,967,990,991 | torch.onnx.export result in opset=1 | ducknificient | closed | [
"module: onnx",
"triaged"
] | 3 | NONE | ### 🐛 Describe the bug
why the opset version is ignored after exporting from pytorch ?
```py
from transformers import DistilBertTokenizer, DistilBertModel
tokenizer = DistilBertTokenizer.from_pretrained(
pretrained_model_name_or_path="distilbert/distilbert-base-uncased",
)
model = DistilBertModel.from_pretrai... | true |
2,967,975,324 | [CUDA] include nvtx3 header in wheel so downstream torch extension can find it | ppham-nv | open | [
"triaged",
"open source",
"release notes: build",
"topic: build"
] | 3 | NONE | When building pytorch with USE_SYSTEM_NVTX=0 or undefined then there's no information to downstream torch extension to figure out which nvtx3 headers was used with pytorch. This PR packages the nvtx3 header (340kb) into the torch wheel install so torch extension can reference it. This will help with keeping nvtx3 versi... | true |
2,967,889,365 | DISABLED test_parity__foreach_abs_fastpath_inplace_cuda_int16 (__main__.TestForeachCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: mta"
] | 5 | NONE | Platforms: linux
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_parity__foreach_abs_fastpath_inplace_cuda_int16&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/39875114980).
Over t... | true |
2,967,852,288 | [audio hash update] update the pinned audio hash | pytorchupdatebot | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 6 | COLLABORATOR | This PR is auto-generated nightly by [this action](https://github.com/pytorch/pytorch/blob/main/.github/workflows/nightly.yml).
Update the pinned audio hash. | true |
2,967,846,361 | Make sure torch.compiler._is_compiling_flag=True in aoti | yushangdi | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Summary: See internal Diff summary
Differential Revision: D72355449
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,967,844,138 | [Inductor] Add Additional Configs for persistent+TMA version of Triton mm and addmm | NikhilAPatel | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 41 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150587
Summary:
This PR introduces additional autotuning configurations for the persistent+TMA version of Triton `mm` and `addmm` operations. The new configurations are as follows:
* `(128, 128, 64, 5, 8)`
* `(256, 128, 64, 4, 8)`
* ... | true |
2,967,834,326 | [dynamo] context manager/decorator for dynamo config patching during tracing | williamwen42 | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo",
"keep-going",
"ci-no-td"
] | 20 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150586
Implement traceable config patching for Dynamo: enables restricted patching of Dynamo config where user can use a context manager/decorator to change tracing behavior for parts of the code.
The new `dont_skip_tracing` deco... | true |
2,967,791,920 | [distributed] Crash when trying to use default PG after creating new PG | xmfan | open | [
"oncall: distributed",
"triaged"
] | 6 | MEMBER | ### 🐛 Describe the bug
Not sure if I'm doing something dumb, but I couldn't find docs on it and even LLMs were puzzled:
Repro:
```python
# CRASH=1 torchrun --nproc_per_node=8 try_async_pg.py
import os
import torch
import torch.distributed as dist
rank = int(os.environ["RANK"])
world_size = int(os.environ["WORLD_S... | true |
2,967,779,113 | [WIP] try always splitting in reshape view | pianpwk | open | [] | 2 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
| true |
2,967,769,406 | fix dynamic shapes for kwargs | avikchaudhuri | open | [
"fb-exported",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: export"
] | 14 | CONTRIBUTOR | Summary:
In this PR we change how `dynamic_shapes` map to the top-level structure of `args` and `kwargs`.
Previously, we would match `dynamic_shapes` to the input signature of a module, using `inspect.Signature.bind`; instead, now we match it with the structure of `args` and `kwargs`.
This has some desirable con... | true |
2,967,754,639 | [test] DTensor moe compile fixes for dynamic shapes | bdhirsh | open | [
"oncall: distributed",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | (not for landing)
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150582
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k | true |
2,967,728,114 | add unit test for preferred_blas_library settings | jeffdaily | closed | [
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"ci-no-td"
] | 12 | COLLABORATOR | Follow up to #150212 that was committed without a unit test. | true |
2,967,725,526 | [ROCm] Add support for SymmetricMemory | pragupta | closed | [
"oncall: distributed",
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)",
"rocm",
"keep-going",
"ciflow/rocm-mi300",
"ciflow/periodic-rocm-mi300"
] | 28 | CONTRIBUTOR | This is an attempt to re-land the initial PR https://github.com/pytorch/pytorch/pull/134817 with recent design changes from upstream.
**NOTE:**
ROCm currently does NOT have multicast/multimem hardware support at the moment, so those features are disabled in symmetric memory for ROCm. This also means that we current... | true |
2,967,707,707 | Add Chillee as core reviewer | zou3519 | closed | [
"Merged",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150579
| true |
2,967,652,953 | ROCm Sparsity through HipSparseLT | petrex | open | [
"module: rocm",
"triaged",
"open source",
"release notes: sparse"
] | 4 | CONTRIBUTOR | TLDR:
- This pull request introduces support for hipSPARSELt in ROCm, current usage would be semi-structure sparsity.
- Require **ROCm 6.3** && **gfx942/gfx950**.
- The average performance uplift (compare to dense operation) is ~ 20% in ROCm 6.4 but expect further performance lift along the way.
... | true |
2,967,652,613 | [TorchScript] Enum scripting failures in python 3.11+ | davidberard98 | open | [
"oncall: jit"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
**python's Enums have changed subtly from 3.10 to 3.12**. See the comment below for more details. This comment contains the original bug report (for an enum).
repro:
```python
import torch
from enum import Enum
class MyOptions(str, Enum):
ABC = "abc"
DEF = "def"
def __str__(self... | true |
2,967,647,901 | [cuda] Added CUDA kernels for RMSNorm | ahmadsharif1 | open | [] | 3 | CONTRIBUTOR | This speeds up RMSNorm in eager mode by 2-5x for both the forward and backward passes.
Example:
TODO: Fix the regressions in the narrow case once CI is green
This PR is still draft | true |
2,967,645,348 | [FlexAttention] Remove dead code | drisspg | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: flex attention"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150575
cc @Chillee @yanboliang @BoyuanFeng | true |
2,967,627,272 | [mem profiler] mem fragmentation and pynvml view | sfc-gh-sbekman | open | [
"module: cuda",
"module: memory usage",
"triaged",
"module: CUDACachingAllocator"
] | 15 | NONE | ### 🚀 The feature, motivation and pitch
As we know what cuda allocator shows doesn't have a 1:1 correlation to free memory usage because of fragmentation, so often one gets OOM while there are many GBs of free memory, except they are fragmented.
I'm trying to figure out how to find out the fragmentation happens in t... | true |
2,967,595,114 | [Bugfix] Fix compile error with `torch.Tensor.unsqueeze_` and inplace views called from Tensor Class | Lucaskabela | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Fixes #129673
### Summary:
Modifying a tensor by reshaping in place (such as `unsqueeze_`) should cause a graph break; however, when accessed through `torch.Tensor` api as opposed to as self attribute caused the code to crash with an error (see attached issue)
Paths differed when traced due to the stack variab... | true |
2,967,572,036 | Revert "[fx] Move Node._prepend/Node._remove_from_list to C++ (#148261)" | atalman | closed | [
"release notes: fx",
"fx",
"ci-no-td"
] | 1 | CONTRIBUTOR | This reverts commit 5d4e7d58b42623a9024a84f0050967ff0318dcdb.
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,967,546,762 | Improve speed of pytorch docs build | svekars | open | [
"module: build",
"module: docs",
"triaged",
"topic: build"
] | 1 | CONTRIBUTOR | ### 📚 The doc issue
**Current Situation:**
* The documentation build process takes approximately 35+ minutes.
* 15 minutes for building torch on linux-jammy-py3.9-gcc11.
* 20 minutes for the doc build.
* 10 minutes to upload for preview
**Problem:**
* Rebuilding torch is unnecessary for changes limited to... | true |
2,967,527,621 | [aoti] Fix cannot determine truth value of Relation error when propagating unbacked symint in lowering | yushangdi | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: export"
] | 4 | CONTRIBUTOR | Summary: Fix cannot determine truth value of Relation error when propagating unbacked symint in lowering
Test Plan:
```
buck run fbcode//mode/dev-nosan //caffe2/test/inductor:test_aot_inductor -- -r aoti_runtime_asserts
```
Differential Revision: D72331070
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-... | true |
2,967,504,618 | Enable lazy cloning in `Tensor.to` between CPU and MPS | kurtamohler | open | [
"open source",
"release notes: lazy",
"release notes: mps",
"ciflow/mps"
] | 5 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150569
* #150721
* #148408
| true |
2,967,502,705 | Overload unary - operator on at::vec::Vectorized to call neg() | swolchok | closed | [
"module: cpu",
"Merged",
"ciflow/trunk",
"release notes: cpp"
] | 19 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150568
* #150380
Makes Vectorized look even more like a scalar type, getting me closer to being able to use the same generic code with scalars and Vectorized (e.g., for sigmoid, which needs `exp(-x)`).
cc @jgong5 @mingfeima @Xiaobin... | true |
2,967,460,019 | Initial Implementation of Padded Tensor | alexanderb14 | open | [
"open source",
"module: inductor",
"ciflow/inductor"
] | 4 | NONE | This PR introduces the initial implementation of `PaddedTensor`, a Tensor subclass, enabling `reduce-overhead` performance benefits for workloads with dynamic shapes.
## Background and Motivation
Currently, reduce-overhead requires statically shaped models due to constraints in the CUDAGraphs backend. This limitati... | true |
2,967,451,151 | [MPSInductor] Speedup `sum`/`prod` reductions | malfet | closed | [
"Merged",
"topic: performance",
"release notes: mps",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150566
By using cooperative `simd_sum`/`simd_product` instead of a C-style for loop for threadgroup reductions. This also allows significantly reduce amount of shared memory needed to perform those reductions
Using such reducti... | true |
2,967,422,917 | Move formulas on separate line in loss.py | svekars | closed | [
"module: docs",
"Merged",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Move formulas on separate line in loss.py for better readability.
cc @sekyondaMeta @AlannaBurke | true |
2,967,265,103 | Experiment with user buffer registration for FSDP2 | lw | open | [
"oncall: distributed",
"release notes: distributed (fsdp)",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150564
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,967,264,915 | Fix detection of GPU multicast | lw | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150564
* __->__ #150563
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,967,253,836 | DISABLED test_parity__foreach_abs_fastpath_inplace_cuda_float64 (__main__.TestForeachCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: mta"
] | 5 | NONE | Platforms: linux, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_parity__foreach_abs_fastpath_inplace_cuda_float64&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/39857047873... | true |
2,967,248,452 | [invoke_subgraph] Force grad_outs to be contiguous at tracing time | anijain2305 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150561
* #150556
* #150486
* #150450
* #150082
I am unable to come up with a testcase. It passes many end-to-end tests that fail with ReshapeError at https://ossci-raw-job-status.s3.amazonaws.com/log/39717218372
![image](ht... | true |
2,967,226,014 | ci: Set minimum cmake version for halide build | seemethere | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 6 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150560
This was failing due to pybind being strict about their cmake version
requirements.
This resolves errors like:
```
652.1 Compatibility with CMake < 3.5 has been removed from CMake.
652.1
652.1 Update the VERSION... | true |
2,967,171,305 | [submodule] [Snapshot/Profiler] Memory Snapshot On Demand | sraikund16 | closed | [
"enhancement",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: profiler"
] | 18 | CONTRIBUTOR | Summary:
Profiler side of memory snapshot.
1. Add API to actually do snapshot when client interface is called
2. Add ifdefs to builds so that kineto hooks snapshot correctly.
Design Philosophy: There is one interesting part of this implementation and it is during export. For export we are callign the python impl of t... | true |
2,967,126,866 | Binary docker builds - use image tagged with folder sha | clee2000 | closed | [
"ciflow/binaries",
"topic: not user facing"
] | 5 | CONTRIBUTOR | It is hard to test the docker images that are built for binaries because the the binary workflows are hard coded to run on an image from docker io, with the main tag. To test, you have to make a change to generate_binary_build_matrix to fetch the correct tag from aws ecr and open a separate PR to test
insert exampl... | true |
2,967,083,116 | torch.compile specific Exceptions are not serializable | zou3519 | open | [
"triaged",
"oncall: pt2",
"vllm-compile"
] | 3 | CONTRIBUTOR | See https://github.com/vllm-project/vllm/issues/15592 for motivation. e.g. BackendCompilerFailed is not serializable. We should understand the serializablility constraint and then determine if we want to make these exceptions serializable.
There are issues, like the frame object in BackendCompilerFrame is not serializ... | true |
2,967,080,506 | [invoke_subgraph][min-cut partitioner] Fix bug to use the correct root module | anijain2305 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150561
* __->__ #150556
* #150486
* #150450
* #150082
| true |
2,967,071,724 | Use 'rocm' naming for rocm-related workflows/jobs | jithunnair-amd | closed | [
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/slow",
"ciflow/rocm",
"ciflow/inductor-rocm",
"ciflow/rocm-mi300"
] | 3 | COLLABORATOR | Reduces number of places in the workflow files needing update for ROCm version update
cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,967,059,245 | Update torch-xpu-ops commit pin to 98c808d | chuanqi129 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/xpu"
] | 6 | COLLABORATOR | Update the torch-xpu-ops commit to [98c808dea6de7330c415aa777d6921944cf79887](https://github.com/intel/torch-xpu-ops/commit/98c808dea6de7330c415aa777d6921944cf79887), include
- Fixes #150001 by removing pre-CXX11 ABI logic from build script for XPU
- Fixes #150430
- Fixes XCCL build issue caused by PR #150398
| true |
2,967,036,718 | [aoti] make a check function for each input | yushangdi | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: export"
] | 8 | CONTRIBUTOR | Summary: make a check function for each input to avoid too large to optimize error on `__check_inputs_outputs`
Test Plan:
```
buck run fbcode//mode/dev-nosan //caffe2/test/inductor:test_aot_inductor -- -r runtime_checks
```
Differential Revision: D72286280
cc @voznesenskym @penguinwu @EikanWang @jgong... | true |
2,967,029,974 | Fix link formatting in cpp_extension.py | svekars | open | [
"module: docs",
"topic: not user facing"
] | 1 | CONTRIBUTOR | Fix link formatting
cc @sekyondaMeta @AlannaBurke | true |
2,967,015,168 | ci: Use cache / progress when local docker build | seemethere | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150560
* __->__ #150551
It's a bit annoying to try and work on these locally when the cache /
progress isn't being used so let's just set it so that those flags are
only valid when in CI directly.
`${CI}` is a default environment variable ... | true |
2,966,990,699 | [Release/2.7][MPS] Warn that torch.compile is a protoype | malfet | closed | [
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | And reference https://github.com/pytorch/pytorch/issues/150121
| true |
2,966,988,622 | Address Cmake update issue in windows magma builds | atalman | closed | [
"Merged",
"topic: not user facing"
] | 3 | CONTRIBUTOR | 1. Fixes Cmake update error: https://github.com/pytorch/pytorch/actions/runs/14223930697/job/39858632864
```
CMake Error at CMakeLists.txt:1 (cmake_minimum_required):
Compatibility with CMake < 3.5 has been removed from CMake.
Update the VERSION argument <min> value. Or, use the <min>...<max> syntax
to te... | true |
2,966,976,769 | Segmentation fault when using torch.tensor from a non python created thread | jabraham17 | open | [
"module: cpp",
"triaged",
"module: pybind",
"release notes: python_frontend"
] | 2 | NONE | ### 🐛 Describe the bug
When trying to run PyTorch from a non-Python created thread, I am finding that using 2.6.0 runs into a segmentation fault. This appears to be a regression from 2.5.0, as 2.5.0 and 2.4.0 both work fine with the exact same code.
<details>
<summary> Full C code to reproduce </summary>
```c
//... | true |
2,966,967,328 | [BE] Fix triton windows build | pytorchbot | closed | [
"open source",
"topic: not user facing"
] | 1 | COLLABORATOR | Fixes #150480 | true |
2,966,946,686 | [export] Refactor strict to pass fake tensors to dynamo | angelayi | open | [
"ciflow/trunk",
"module: dynamo",
"ciflow/inductor",
"keep-going",
"release notes: export"
] | 2 | CONTRIBUTOR | Currently in the strict-export workflow this is what happens:
1. We take example inputs and dynamic shapes, and pass it to Dynamo
a. Dynamo turns the dynamic shapes spec into constraints
b. Dynamo turns the inputs into fake tensors, some with symbolic shapes depending on the constraints
c. After tracing... | true |
2,966,929,304 | [aoti] Split ConstantType definition out of model.h | zhxchen17 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Summary:
Splitting the type definition of ConstantType into a separate header because it's needed by Sigmoid OSS but the entire model.h header include cause the following compilation error:
```
2025-04-01T18:12:42.0391272Z FAILED: caffe2/CMakeFiles/torch_cpu.dir/__/torch/csrc/nativert/kernels/AOTICallDelegateKernel.cpp... | true |
2,966,861,696 | [ONNX] dynamic_axes does not rename dynamic dimension in torch.onnx.export | xadupre | open | [
"module: onnx",
"triaged",
"onnx-triaged"
] | 0 | COLLABORATOR | ### 🐛 Describe the bug
Unexpected names for the dynamic dimension when dynamic_axes is used instead of dynamic_shapes in torch.onnx.export. Found in https://github.com/huggingface/optimum/pull/2219.
```python
import onnx
import torch
import transformers
from torch.export import Dim
# Load the model
model = transfor... | true |
2,966,827,195 | add batching rule for `torch.Tensor.scatter_add_` | guilhermeleobas | closed | [
"open source",
"Merged",
"ciflow/trunk",
"module: functorch",
"release notes: torch.func"
] | 5 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150543
cc @zou3519 @Chillee @samdow @kshitij12345 | true |
2,966,815,229 | Revert "[fx] Move Node._prepend/Node._remove_from_list to C++ (#148261)" | jansel | closed | [
"Merged",
"ciflow/trunk",
"release notes: fx",
"fx",
"module: dynamo",
"ciflow/inductor",
"ci-no-td"
] | 12 | CONTRIBUTOR | Reverts #148261 due to possible memory leak
This reverts commit 5d4e7d58b42623a9024a84f0050967ff0318dcdb.
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv @voznesenskym @penguinwu @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,966,792,337 | [MPSInductor] Disable mm/bmm decompositions | manuelcandales | closed | [
"Merged",
"topic: performance",
"release notes: mps",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Disables mm/bmm decompositions.
torch.compile on MPS was speeding up stories15M (~4x) but it was making stories110M much slower.
Self-contained reproducer to demonstrate the difference (before the change, after it should be identical)
```python
import torch
import timeit
def bench_mm(f, x, y):
from torch... | true |
2,966,791,485 | PropagateUnbackedSymInts does not know about shape checks in guards | angelayi | closed | [
"triaged",
"oncall: pt2",
"module: dynamic shapes",
"module: aotdispatch",
"module: dynamo",
"module: pt2-dispatcher"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
```python
def test_runtime_asserts(self):
class M(torch.nn.Module):
def forward(self, x, y):
b = x.item()
torch._check_is_size(b)
torch._check(b < y.shape[0])
return y[:b]
ep = torch.export... | true |
2,966,768,048 | AOTI drops runtime asserts | angelayi | closed | [
"oncall: pt2",
"oncall: export",
"module: aotinductor"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
AOTI drops runtime asserts that export adds, which is bad if draft-export makes some assumptions and adds asserts into the graph to ensure soundness.
```python
def test_aoti_runtime_asserts(self):
class M(torch.nn.Module):
def forward(self, x, y):
b = x.... | true |
2,966,690,676 | .exponential_ has different RNG in nightlies | felipemello1 | closed | [
"triaged",
"module: random"
] | 1 | NONE | ### 🐛 Describe the bug
Updating to nightlies broke our tests in torchtune. After investigating, we found that the "exponential_" operation had a different rng.
```python
import torch
torch.manual_seed(42)
print("torch.rand(5):", torch.rand(5))
print("torch.empty(5).exponential_(1):", torch.empty(5).exponential_(1))... | true |
2,966,672,698 | [pytorch] add experimental TORCH_LIBRARY_THREAD_UNSAFE_LAZY_INIT | rmaz | closed | [
"oncall: jit",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: mobile"
] | 7 | CONTRIBUTOR | Summary: Add an experimental feature to defer pytorch library initialization cost to post startup. As noted this feature is not thread safe, it requires the client to maintain thread safety at library load time.
Reviewed By: zou3519
Differential Revision: D71917841
cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel | true |
2,966,401,236 | API change for new enum in cusparseltsplitkmode-t for cusparseLT 0.7.0+ | tinglvv | open | [
"module: bc-breaking",
"triaged",
"open source",
"release notes: sparse",
"topic: bc breaking"
] | 14 | COLLABORATOR | Changing the bool to int to express split_k_mode. Before 0.7.0 we only have 2 cusparseLtSplitKMode_t enum values ONE_KERNEL and TWO_KERNELS so a boolean is enough but since 0.7.0 there are more.
For Blackwell, there has to be minor change to parameter split_k_one_kernel (https://github.com/pytorch/pytorch/blob/main/... | true |
2,966,323,791 | [AOTI][dashboard] Update how peak memory is measured | desertfire | closed | [
"Merged",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150534
Summary: In the dashboard measurement script, AOTI needs to run Eager first to register the output pytree, so the peak memory compression ratio on the dashboard is always close to 1. Update AOTI run to use an extra warmup run,... | true |
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