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 |
|---|---|---|---|---|---|---|---|---|
2,839,195,742 | support meta_tensor.to(device='cpu') under fake_mode | bdhirsh | closed | [
"Merged",
"ciflow/trunk",
"release notes: composability",
"ciflow/inductor"
] | 8 | CONTRIBUTOR | Fixing this is actually a bit annoying:
(1) FakeTensorMode sees a function where all of its inputs are real tensors, so it tries to run the real compute before converting the output to a FakeTensor
(2) we don't actually want this, because the "real compute" is support to error normally, when you do `meta_tensor.t... | true |
2,839,188,491 | [StaticRuntime] Fix a bug that memory planner ignores subblocks | coufon | closed | [
"oncall: jit",
"fb-exported",
"release notes: jit"
] | 9 | CONTRIBUTOR | Summary: When Static Runtime graph node has sub-blocks, the memory planner does not consider sub-blocks' inputs as a node's input in memory planner. As the result, such nodes' inputs' lifetime is incorrect and corresponding tensor memory is released earlier than required and causes errors.
Differential Revision: D6919... | true |
2,839,169,365 | Rename PrimHOPBase to BaseHOP + minor changes | zou3519 | closed | [
"Merged",
"ciflow/trunk",
"release notes: foreach_frontend",
"module: dynamo",
"ciflow/inductor",
"keep-going"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146730
* __->__ #146727
This PR:
- renames PrimHOPBase to BaseHOP
- changes the backward pass to always return a tuple (to match the
forward pass).
Test Plan:
- tests
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @Xiaobin... | true |
2,839,166,213 | [ez][BE] get rid of the extra printf('\n') | YUNQIUGUO | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Summary: as title
Test Plan:
```
AOT_INDUCTOR_DEBUG_INTERMEDIATE_VALUE_PRINTER=3 TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 TORCHINDUCTOR_ABI_COMPATIBLE=1 TORCH_COMPILE_DEBUG=1 TORCH_LOGS="+graph, inductor, +schedule, output_code" buck2 run -c fbcode.enable_gpu_sections=true -c fbcode.nvcc_arch=h100a @//mode/opt fbcode//ca... | true |
2,839,124,193 | remove incorrect warnings from min/max documentation | ngimel | closed | [
"Merged",
"ciflow/trunk",
"release notes: python_frontend"
] | 6 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,839,084,980 | Add support for flexattention + int64 indexing | Chillee | open | [
"triaged",
"oncall: pt2",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention"
] | 0 | COLLABORATOR | ### 🚀 The feature, motivation and pitch
Today FlexAttention fails if you pass in too big of an input (since it'll need 64 bit indexing)
### Alternatives
_No response_
### Additional context
_No response_
cc @chauhang @penguinwu @zou3519 @ydwu4 @bdhirsh @yf225 @drisspg @yanboliang @BoyuanFeng | true |
2,839,077,535 | torch: Log a unified waitcounter for torch.compile and triton.autotune | c00w | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 10 | CONTRIBUTOR | Summary: Add a second more generic waitcounter to torch.compile. We'll keep expanding this as new generic pytorch compilation sites show up.
Test Plan: Waitcounter only change, relying on existing tests.
Differential Revision: D69215401
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @... | true |
2,839,003,336 | Test on in-graph constructed NJTs | jbschlosser | open | [
"Stale",
"topic: not user facing"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146722
* #146721
A recent set of bugs has been cropping up related to NJTs that constructed in-graph within a compiled function. This exercises different paths related to symbolic nested ints, etc. Some examples:
* #145874
* #1466... | true |
2,839,003,246 | Use inductor backend for NJT compile tests | jbschlosser | open | [
"Stale",
"topic: not user facing"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146722
* __->__ #146721
We've been using `backend="aot_eager_decomp_partition"` for NJT compile testing, but this can let inductor bugs slip through. This PR switches the compile tests to use `backend="inductor"`; let's see if test runtime... | true |
2,838,998,538 | [ca] remove private API: _compiled_autograd_should_lift | xmfan | closed | [
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: dynamo"
] | 12 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146735
* __->__ #146720
Since the functional autograd + compiled autograd migration, we don't trace into nodes anymore, and everything is lifted. We can't support this flag which tries to inline make_fx style in CA initial pass. There's no... | true |
2,838,985,012 | Operations that fail under `torch.export.export`(`torch.autograd.grad`) -> `torch.compile` | cw-tan | open | [
"oncall: pt2",
"oncall: export"
] | 1 | NONE | ### 🐛 Describe the bug
Not completely a `torch.compile` problem, more of a `torch.export.export` problem when used with `torch.autograd.grad`. We have seen minimal working examples of `torch.export.export` working for code that includes `x.requires_grad() ... torch.autograd.grad(y, [x])`, but some operations don't ge... | true |
2,838,935,121 | [CUDAGraph] add skip message for unbacked symint | BoyuanFeng | open | [
"Stale",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Add explicit skip message for unbacked symint in cudagraph, as suggested by @bdhirsh.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,838,922,626 | [BE][cuDNN] cuDNN to 9.7.1.26 for CUDA 12.8 | eqy | open | [
"module: build",
"module: cudnn",
"triaged",
"open source",
"Stale",
"ciflow/binaries",
"topic: not user facing",
"topic: build"
] | 7 | COLLABORATOR | cuDNN 9.7.1 is out now and is expected to be the longer-lived branch with more potential backports vs. 9.7.0
CC @nWEIdia @tinglvv
cc @malfet @seemethere @csarofeen @ptrblck @xwang233 | true |
2,838,859,306 | [BE] Remove outdated RPC benchmark | janeyx99 | closed | [
"Merged",
"ciflow/trunk",
"release notes: distributed (rpc)",
"skip-pr-sanity-checks"
] | 4 | CONTRIBUTOR | We have lots of outdated unused + uncalled code in our codebase, namely in our benchmarks and examples folders among others. The last change to this directory was 4 years ago and this code looks dead. cc @albanD @H-Huang for feedback
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->... | true |
2,838,811,832 | [export][ez] Allow math.trunc for serialization. | zhxchen17 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: export"
] | 4 | CONTRIBUTOR | Summary: as title.
Test Plan: CI
Differential Revision: D69317084
| true |
2,838,744,082 | [hop] Support more output types for `flat_apply` | 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
This patch enables `flat_apply` to support certain non-Tensor output
types like containers and graphable types. This will in turn enable the
upcoming `mark_traceable` to support more out... | true |
2,838,743,928 | [dynamo][fx] Support dataclass whose fields have `init=False` | StrongerXi | closed | [
"release notes: fx",
"fx",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146950
* #146367
* #146714
* __->__ #146713
* #147152
* #147145
Previously Dynamo and FX have code paths that reconstruct a dataclass
instance based on its type and fields; however they weren't taking
`init=False` into account (which is sup... | true |
2,838,701,506 | torch/_inductor/cpp_builder.py : _is_gcc Function Incorrectly Classifies clang++ as g++ | ankushjqc | closed | [
"triaged",
"actionable",
"oncall: cpu inductor"
] | 4 | NONE | Description: The _is_gcc function is intended to identify whether a given C++ compiler is GCC. However, it incorrectly classifies clang++ as g++ due to the current regular expression used in the function, which in turn lead to incorporation of wrong compiler option when using clang (for example fno-tree-loop-vectorize ... | true |
2,838,686,911 | Add different padding modes to torch.nn.utils.rnn.pad_sequence | nikas-belogolov | open | [
"module: nn",
"module: rnn",
"triaged"
] | 0 | NONE | ### 🚀 The feature, motivation and pitch
Add different padding modes to torch.nn.utils.rnn.pad_sequence like reflect, replicate and circular
I have sequences which I want to pad using replication, but needed to create a custom function for it.
### Alternatives
```python
def pad_sequence_replicate(sequences):
ma... | true |
2,838,633,337 | [MTIA] (3/n) Implement PyTorch APIs to query/reset device peak memory usage | chaos5958 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 8 | CONTRIBUTOR | Summary: Public summary (shared with Github): This diff implements a C++-Python binding to enable `reset_peak_memory_stats`.
Test Plan: The test is implemented in the following diff.
Reviewed By: yuhc
Differential Revision: D68988673
| true |
2,838,603,359 | FSDP: avoid resetting version counter of all_gather_output in inference_mode | bdhirsh | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: distributed (fsdp)",
"ciflow/inductor"
] | 8 | CONTRIBUTOR | Summary:
FSDP needs to hide VC bumps on its allgather buffer, but it does not need to do this is the allgather buffer was generated under inference mode.
more details here: https://www.internalfb.com/diff/D69115649?dst_version_fbid=1316814572779281&transaction_fbid=849120230625711
Test Plan: CI
Differential Revision... | true |
2,838,555,697 | Periodic Activations Module | GulkoA | open | [
"module: nn",
"triaged"
] | 3 | NONE | ### 🚀 The feature, motivation and pitch
Periodic activation functions were proposed in 2020 by [V. Sitzmann et al](http://arxiv.org/abs/2006.09661) and have since been used in numerous Implicit Neural Representation publications ([pi-GAN](https://arxiv.org/abs/2012.00926), [Compressive Neural Representations](https:/... | true |
2,838,543,202 | AutoAC can cause deadlocks with tensor parallelism and data-dependent flop formulas | lw | open | [
"module: activation checkpointing",
"triaged",
"oncall: pt2",
"module: pt2-dispatcher"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
Some operators' flop formulas depend on the content of their inputs, such as [FlashAttention when using a NestedTensor](https://github.com/pytorch/pytorch/blob/5d7532140f377195a831942f294c1f07c589bd9c/torch/utils/flop_counter.py#L298) (a.k.a varying sequence lengths, document-causal, block-diag... | true |
2,838,532,178 | cpp_wrapper: persist autotune example tensors until last use | benjaminglass1 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 11 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149350
* #147225
* __->__ #146706
Patches over an issue where randomly generated example tensors can cause kernel autotuning to fail, when those tensors would not be possible outputs from previous kernels in the sequence. This fixes a fail... | true |
2,838,426,132 | Remove NO_MULTIPROCESSING_SPAWN checks | cyyever | closed | [
"oncall: distributed",
"triaged",
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"release notes: distributed (torchelastic)",
"ci-no-td"
] | 13 | COLLABORATOR | py 3.9 has spawn.
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,838,413,744 | `SDPA`: `EFFICIENT_ATTENTION / FLASH_ATTENTION` backend, batch dim limited to 2**16-1 (CUDA error: invalid configuration argument) | Annusha | open | [
"module: cuda",
"triaged",
"module: sdpa"
] | 0 | NONE | ### 🐛 Describe the bug
As per title, see the repro
```python
import torch
import torch.nn.functional as F
from torch.nn.attention import sdpa_kernel
from torch.nn.attention import SDPBackend
def foo(batch_dim, backend):
query = torch.rand([batch_dim, 8, 4, 64], device='cuda', dtype=torch.float16)
key = torch.r... | true |
2,838,407,583 | [MPSInductor] Implement Welford reduction | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146703
Still work in progress, though fallback works as expected, but custom shader is not
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @c... | true |
2,838,402,072 | OSX Arm64 cross-compilation of pytorch extensions fails with conda | stefdoerr | closed | [
"module: cpp-extensions",
"triaged",
"module: third_party"
] | 6 | NONE | ### 🐛 Describe the bug
The current (2.5.1) pytorch version includes the following function in `cpp_extension.py`. The third path being added to the list in `paths` is not being treated for CONDA like `lib_include` is. I'm not entirely sure if this is on purpose or a mistake but this causes compilation issues demonstr... | true |
2,838,387,074 | Unable to Import PyTorch After Upgrade in Docker Environment | unbreading | open | [
"triaged",
"module: docker"
] | 2 | NONE | ### 🐛 Describe the bug
### Problem Description
Before upgrade:
<img width="838" alt="Image" src="https://github.com/user-attachments/assets/8950b303-510f-4136-b44d-8a3b55185941" />
After upgrade (by `pip install torch==2.4.0`)
<img width="837" alt="Image" src="https://github.com/user-attachments/assets/25a81dc3-... | true |
2,838,358,805 | Cleanup CallOnce.h | cyyever | closed | [
"oncall: distributed",
"oncall: jit",
"open source",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 6 | COLLABORATOR | Fixes #ISSUE_NUMBER
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @EikanWang @jgong5 @wenzhe-nrv @sanchitintel | true |
2,838,281,474 | `#type: ignore` linter | rec | open | [
"module: typing",
"module: lint",
"triaged",
"better-engineering"
] | 0 | COLLABORATOR | ### The problem
Accurate Python type annotations are essential to building reliable and reusable software, but there are almost 5000 `#type: ignore` statements in Python files below `torch/`.
After going through several of hundred such statements, I'd say the majority have a simple type solution that doesn't need an ... | true |
2,838,237,423 | Possible memory leak when running test_model_exports_to_core_aten.py test | AlekseiNikiforovIBM | open | [
"module: memory usage",
"oncall: pt2",
"oncall: export"
] | 2 | COLLABORATOR | ### 🐛 Describe the bug
Running test_model_exports_to_core_aten.py test multiple times results in test process consuming increasingly more memory.
Steps to reproduce on x86:
```
docker run -it fedora:latest
dnf update -y
dnf install -y python3-pip git gcc gcc-c++ rust cargo python3-devel
git clone --recurse-submodule... | true |
2,838,140,600 | There is a performance drop because we have not yet implemented the batching rule for aten::mse_loss_backward. | f-fuchs | open | [
"triaged",
"module: batching",
"module: vmap",
"module: functorch"
] | 0 | NONE | Hey,
I recently started using https://github.com/TorchJD/torchjd and now I get the following user warning:
```
/home/fuchsfa/demultiple/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:823: UserWarning: There is a performance drop because we have not yet implemented the batching rule for aten::mse_loss_bac... | true |
2,837,938,657 | Add full_like.default to list of ops with kwargs | Erik-Lundell | closed | [
"triaged",
"open source",
"release notes: quantization",
"release notes: AO frontend"
] | 4 | NONE | The _maybe_insert_input_observers_for_node function expects ops, except a few exceptions, to have zero kwargs. full_like.default seems to be one of these cases and should therefore be added to the list.
Addresses https://github.com/pytorch/pytorch/issues/146621
Fixes #146621
| true |
2,837,938,500 | Enable Windows tests | cyyever | open | [
"module: windows",
"triaged",
"open source",
"Stale",
"topic: not user facing"
] | 5 | COLLABORATOR | Fixes #ISSUE_NUMBER
cc @peterjc123 @mszhanyi @skyline75489 @nbcsm @iremyux @Blackhex | true |
2,837,926,509 | Enable Windows tests | cyyever | closed | [
"open source",
"topic: not user facing"
] | 1 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,837,869,932 | Partitioner moves useless and memory-heavy op from bwd to fwd | lw | open | [
"oncall: distributed",
"triaged",
"oncall: pt2",
"module: aotdispatch",
"module: pt2-dispatcher"
] | 5 | CONTRIBUTOR | ### 🐛 Describe the bug
This code reproduces a fp8 row-wise scaled FFN with sequence-parallelism support (derived from [here](https://github.com/facebookresearch/lingua/blob/main/lingua/float8.py)), and prints the forward Inductor graph as produced by the partitioner:
```py
import tempfile
import torch
import torch.d... | true |
2,837,752,586 | [clang-tidy] Add suppression clang-diagnostic-shadow | dmpolukhin | open | [
"fb-exported",
"Stale",
"topic: not user facing"
] | 13 | NONE | Summary:
Reviewed By: varun2784
Differential Revision: D69182465
| true |
2,837,713,751 | MPS support `torch.linalg.norm` on complex numbers | JackVittori | open | [
"triaged",
"module: complex",
"enhancement",
"module: linear algebra",
"actionable"
] | 3 | NONE | ### 🚀 The feature, motivation and pitch
I am working on a quantum machine learning project with Pennylane and Pytorch on a M3 Pro and I would like to use the GPU to train my models. I would like to be able to use torch.linalg.norm on torch.complex type. Example
```python
import torch
device = torch.device('mps')
s... | true |
2,837,663,381 | Enable pt2e quantization path for arm | choudhary-devang | open | [
"module: cpu",
"triaged",
"open source",
"module: arm",
"release notes: quantization",
"release notes: AO frontend"
] | 56 | NONE | **Title**: Enable PyTorch 2 Export Quantization path for ARM CPUs.
**Description:**
- This PR extends the PyTorch 2 Export Quantization (PT2E Quantization) workflow—originally available only on x86 CPUs—to support ARM platforms. PT2E Quantization is an automated, full-graph quantization solution in PyTorch that im... | true |
2,837,610,676 | Update addbmm, addmm, addmv and baddbmm description | zeshengzong | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: python_frontend"
] | 16 | CONTRIBUTOR | Fixes #146611, following #146482
## Test Result
| true |
2,837,604,376 | [Dynamo] Support for more binary ops | mieshkiwrk | open | [
"triaged",
"oncall: pt2",
"module: dynamo",
"module: graph breaks",
"module: compile ux"
] | 0 | NONE | ### 🐛 Describe the bug
Below example gives graph break `torch._dynamo.exc.Unsupported: builtin: and_ [<class 'torch._dynamo.variables.user_defined.UserDefinedObjectVariable'>, <class 'torch._dynamo.variables.constant.ConstantVariable'>] False`
Looks like intended for now and it can be supported with little effort by... | true |
2,837,602,919 | Make GetCPUAllocatorMaybePinned to be Device-Agnostic | FFFrog | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 4 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146687
----
- Keep cuda first to perserve BC
- Remove cuda first if it is possible to have only one accelerator at a time in the future | true |
2,837,600,545 | Unexpected specialization during estimate_runtime | laithsakka | closed | [
"triaged",
"oncall: pt2",
"module: dynamic shapes",
"module: aotdispatch",
"module: pt2-dispatcher"
] | 3 | CONTRIBUTOR | idk if the title is clear enough probably not but this is issue is about a specialization that happens during forward backward partition.
we have the following joint graph:
```python
def forward(...):
# bla bla bla
# the important part
mul_24: "Sym(s2*s9)" = primals_3 * primals_10
vie... | true |
2,837,596,471 | Fix integer overflow in (fake) quantization | Flamefire | closed | [
"module: cpu",
"release notes: quantization"
] | 1 | COLLABORATOR | The `static_cast<int64_t>` can overflow for large float values and/or a small scale (e.g. 9.2e14 & 1e-4)
Fix a similar issue in the mask calculation where `std::lrint` is used which may convert to a 32 bit float returning an implementation defined value on overflow.
Stay in float mode using `std::round` and `fmin/f... | true |
2,837,575,394 | Optimize LRScheduler docs | zeshengzong | open | [
"triaged",
"open source",
"release notes: optim"
] | 10 | CONTRIBUTOR | Fixes #120735
Add more description about [`LRScheduler`](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.LRScheduler.html#torch.optim.lr_scheduler.LRScheduler)
## Changes
> 1. What the constructor's last_epoch argument is.
And does epoch start from 0 or 1?
Also there are two terms - "epoch... | true |
2,837,564,535 | Web Page do not match the original documentation | ZhaoqiongZ | closed | [
"module: docs",
"triaged",
"module: xpu"
] | 2 | CONTRIBUTOR | ### 📚 The doc issue
For the documentation Getting Started on Intel GPU at this link https://pytorch.org/docs/stable/notes/get_start_xpu.html, the content hasn't been updated to the latest version in the release/2.6 branch https://github.com/pytorch/pytorch/blob/release/2.6/docs/source/notes/get_start_xpu.rst . The la... | true |
2,837,560,463 | How to get last layer hidden state of transformer model while convert model to onnx format? | Jianshu1only | closed | [
"module: onnx",
"triaged"
] | 1 | NONE |
I am currently working with a model that has been exported to the ONNX format. For my project, I need to extract the last layer hidden states during inference. However, I couldn’t find any documentation or example that explains how to achieve this using an ONNX-exported model.
Whether the ONNX format retains the cap... | true |
2,837,499,722 | [MPS] lu unpack | Isalia20 | closed | [
"open source",
"Merged",
"topic: improvements",
"release notes: mps",
"ciflow/mps"
] | 7 | COLLABORATOR | Implements lu unpack function on MPS. Haven't added new tests because they are covered by removing the lu_unpack from UNIMPLEMENTED_XFAILLIST in test_mps with `test_output_match` function | true |
2,837,489,208 | Need guidance to modify number of cores/threads settings when inferencing under transformers&pytorch framework | luentong | closed | [
"module: cpu",
"triaged",
"module: arm"
] | 6 | NONE | In my project I need to run a LlavaLlamaForCausalLM instance inference under transformers framework, which is basically a subclass of nn.Module. I want to modify number of cores/threads settings when doing inferencing, in a machine with 160-core cpus and no gpu. Which folder in the source code of transformers/pytorch i... | true |
2,837,429,944 | Torch 2.6.0 cu126 is missing several dependencies in the METADATA-file | anates | closed | [
"high priority",
"oncall: releng",
"triaged",
"module: regression"
] | 7 | NONE | ### 🐛 Describe the bug
When upgrading from torch-2.6.0+cu124 to torch-2.6.0+cu126 on unix, several dependencies are lost in the METADATA-file:
For cu124 the following packages exist:
```
Requires-Dist: nvidia-cuda-nvrtc-cu12 (==12.4.127) ; platform_system == "Linux" and platform_machine == "x86_64"
Requires-Dist: nv... | true |
2,837,367,604 | [dynamo][not ready] polyfill infra for classes | anijain2305 | open | [
"Stale",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146678
* #146737
* #146677
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,837,367,528 | [dynamo][user-defined] User class.__new__ instead of special casing | anijain2305 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"keep-going"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146819
* #146737
* __->__ #146677
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,837,299,044 | `out` should exist as an instance variable out of the func itself | ILCSFNO | closed | [
"triaged",
"module: random",
"module: python frontend"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
Parameter `out` is widely used in Pytorch, e.g. `torch.randint()`, `torch.arange()`, `torch.fft.rfftfreq()`, `torch.quantile()`, `torch.ormqr()`, ...
But for its usage, `out` serves as the return obj of the exact funcs, which means that `out` should exist as an instance variable out of the fun... | true |
2,837,264,708 | [ROCm] Move ROCm unstable MI300 jobs back to stable | amdfaa | closed | [
"module: rocm",
"triaged",
"open source",
"Merged",
"topic: not user facing",
"ciflow/inductor",
"ciflow/rocm",
"ciflow/inductor-rocm"
] | 5 | CONTRIBUTOR | Fixes #145790
Needs #145504 to be merged first to resolve an artifact uploading issue with MI300 runners.
This PR moves rocm unstable MI300 back to stable. The change to unstable was introduced through this [PR](https://github.com/pytorch/pytorch/pull/145790). This was because the MI300s were failing with a [docker... | true |
2,837,220,128 | [ONNX] Support while and scan HOPs | justinchuby | open | [
"module: onnx",
"triaged"
] | 0 | COLLABORATOR | cc @xadupre @titaiwangms @shubhambhokare1 @gramalingam | true |
2,837,217,502 | Dynamo should handle `class.method_descriptor(instance, *args, **kwargs)` generically | zou3519 | open | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 0 | CONTRIBUTOR | See https://github.com/pytorch/pytorch/pull/146587#discussion_r1944919312
This is an uncommon (but legitimate) way to invoke a method. For example:
```py
# The str case that was fixed by #146587
s = "foobar"
str.isalnum(s)
# example with torch.Tensor (I haven't tested this)
x = torch.randn(3)
torch.Tensor.sin(x)
``` ... | true |
2,837,163,817 | inconsistency in torch.nn.Tanh on CPU and GPU | alionapi | closed | [
"module: nn",
"module: cpu",
"triaged"
] | 1 | NONE | ### 🐛 Describe the bug
Inconsistency in `torch.nn.Tahn` CPU and GPU:
```
import torch
self = torch.tensor([[[[0.0396193 + 1.5585054j, 0.5038033 - 1.3928472j],
[1.1071061 + 1.0378395j, 0.0687875 - 0.1666800j]],
[[-0.9338380 - 1.0284885j, 0.2591278 + 0.5482853j],
... | true |
2,837,147,286 | Update torch-xpu-ops commit pin | xytintel | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"keep-going",
"ciflow/xpu"
] | 13 | CONTRIBUTOR | Update the torch-xpu-ops commit to [80c375570e2b6b2989a8610da1871f8a50dfddc7](https://github.com/intel/torch-xpu-ops/commit/80c375570e2b6b2989a8610da1871f8a50dfddc7), includes:
- Aten operator coverage improvement
- SYCL kernel optimization
- Nested Tensor OPs support
| true |
2,837,114,502 | [Dynamo] Eliminate single `Self` import from typing_extensions | zeshengzong | closed | [
"open source",
"topic: not user facing",
"module: dynamo"
] | 2 | CONTRIBUTOR | Replace `from typing_extensions import Self` with `from typing import`
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,837,061,751 | Optimize inductor `Self` typing | zeshengzong | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 9 | CONTRIBUTOR | Replace method return type with `Self` typing
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @desertfire @chauhang @aakhundov | true |
2,837,057,069 | Make sure cutlass kernel .cu file has configuration name and nvcc compile command | henrylhtsang | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 10 | CONTRIBUTOR | I think its good to have everything in the .cu file. Especially the nvcc compile command.
Technically, the configuration name can be found in the template already. So let me know if you think its not needed.
Differential Revision: D69281295
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @X... | true |
2,837,037,220 | Different gradient calculation for tensor.min() vs tensor.min(dim=0) | tuero | closed | [
"module: autograd",
"triaged",
"module: derivatives"
] | 2 | NONE | ### 🐛 Describe the bug
In a previous issue [here](https://github.com/pytorch/pytorch/issues/35699), it was noted that `tensor.min()` and `tensor.min(dim=0)` behave differently. The following example was given as the behaviour at the time:
```python
import torch
a = torch.tensor([0.1, 0.3, 0.1], dtype=torch.float32, r... | true |
2,837,029,646 | Enable Windows tests | cyyever | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 9 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,837,029,045 | Fix linter F821 error | cyyever | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 7 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,837,020,732 | [Docs] Fix description of `input` in `torch.addbmm()` | shink | closed | [
"open source",
"Merged",
"ciflow/trunk",
"release notes: python_frontend",
"topic: docs"
] | 20 | CONTRIBUTOR | Fixes #146613
| true |
2,836,969,359 | Exponentially slow compile time with repeated logsumexp when gradient is enabled | hkchengrex | closed | [
"oncall: pt2",
"module: compile-time"
] | 3 | NONE | ### 🐛 Describe the bug
I am trying to compile a simplified Sinkhorn algorithm. The core function is
```python
@torch.compile
def sinkhorn(Mr, u, v):
for _ in range(num_iters):
v = torch.logsumexp(Mr + u.unsqueeze(1), 0)
u = torch.logsumexp(Mr + v.unsqueeze(0), 1)
return u, v
```
With a **sta... | true |
2,836,963,310 | [Optimus] Include more corner cases in the select cat aten pass | mengluy0125 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor",
"inductor_pattern_match"
] | 7 | CONTRIBUTOR | Summary: Thanks to Shuai for reporting the bug in the pattern. We found there's a typo in the pass, where we should make sure all the selects will go to the cat node.
Test Plan:
buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:split_cat_fx_aten_passes -- test_select_cat_post_grad
Buck UI: https://www... | true |
2,836,950,107 | [cond] Refactor cond_op's signature to take *operands. | ydwu4 | open | [
"Stale",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"keep-going",
"release notes: export"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146661
* #146660
This is a BC-breaking change for hop's IR schema. Previously,
```python
torch.cond(pred, true_fn, false_fn, (a, b))
# Old representation
torch.ops.higher_order.cond(pred, true_gm, false_gm, (a, b))
# New rep... | true |
2,836,949,960 | [hop][inductor] don't promote arg type for cond and while_loop | ydwu4 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 8 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #146661
* __->__ #146660
Hop subgraph codegen assumes arguments's type are not promoted. Otherwise, we might generate wrong kernel.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-n... | true |
2,836,932,104 | [MTIA] (2/n) Implement PyTorch APIs to query/reset device peak memory usage | chaos5958 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: mtia"
] | 7 | CONTRIBUTOR | Summary:
Public summary (shared with Github): This diff implements the correct version of the PyTorch API "max_memory_allocated".
Nit: The file previously contained two unit tests with the same name (due to wrong revert); I deleted a deprecated one to revamp the correct version.
Test Plan:
```
buck2 test //mtia/host... | true |
2,836,931,882 | [HOP] Mutation and alias rework | bohnstingl | open | [
"triaged",
"open source",
"topic: not user facing",
"module: inductor",
"module: dynamo"
] | 7 | COLLABORATOR | This PR reworks the way the input mutations and various aliases are checked
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov @ydwu4
| true |
2,836,910,493 | [FlexAttention] Fix dynamic shapes in max-autotune | drisspg | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"module: flex attention"
] | 9 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146657
# Fixes
https://github.com/pytorch/pytorch/issues/146624
### Updated
From offline discussion going w/ sizehint
However this does incur guards. I couldn't really think of a fancy way to do this. I was going t... | true |
2,836,900,548 | Optimize isclose() for CPU and GPU by adding specific implementations | ZelboK | open | [
"module: cpu",
"triaged",
"open source",
"release notes: intel"
] | 1 | CONTRIBUTOR | `isclose()` is currently quite slow, so this PR adds specific implementations for both CPU and cuda.
CUDA implementation seeing ~4.9x improvement at 100m elements and ~18.7x improvement at 400m elements
CPU implementation seeing ~5.7x improvements at 100m elements and ~5.9x improvements at 400m elements
simpl... | true |
2,836,886,440 | add MXFP8 support to torch._scaled_mm | vkuzo | closed | [
"topic: improvements",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | # 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.
This is a skeleton PR where we test basic functionality (numerics of various simple matrices, as ... | true |
2,836,855,253 | [DCP] Introduce modules metadata in the storage_meta | saumishr | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 29 | CONTRIBUTOR | Summary: Introduce the list of modules in the storage_meta which is shared between the planner and the storage writer. We will use it to let the storage writer know about the modules in the state dict and create module directories in the checkpoint.
Test Plan: UTs
cc @H-Huang @awgu @kwen2501 @wanchaol @fegi... | true |
2,836,842,594 | windows Magma and cuda build for cu128 | tinglvv | closed | [
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: releng",
"ciflow/binaries_wheel",
"ci-no-td"
] | 16 | COLLABORATOR | https://github.com/pytorch/pytorch/issues/145570
removing `.ci/pytorch/windows/internal/cuda_install.bat` as it is a duplicate with` .github/scripts/windows/cuda_install.bat`. The later one is the one in use - https://github.com/pytorch/pytorch/pull/146653/files#diff-613791f266f2f7b81148ca8f447b0cd6c6544f824f5f46a78... | true |
2,836,826,872 | [inductor] Better exception error messages for cache_on_self | jansel | 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):
* __->__ #146652
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @desertfire @chauhang @aakhundov | true |
2,836,797,935 | [inductor] Use index_dtype (int32/int64 depending on size) for argmax accumulators | jansel | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146651
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @desertfire @chauhang @aakhundov | true |
2,836,784,653 | fuzzer: disable "fail_on_recompile_limit_hit" and "suppress_errors" | exclamaforte | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 21 | CONTRIBUTOR | Summary:
needed for https://github.com/pytorch/pytorch/pull/146513
Test Plan:
the existing tests
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @desertfire @chauhang @aakhundov | true |
2,836,771,124 | Fix get_top() to return the base level event of the stack, not the most recently started event | jamesjwu | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146649
`get_top()` is really confusing when talking about a stack, because it can mean the most recently started event on the stack or the toplevel event in perfetto(which displays the stack upside down). Rename to `get_outermost`... | true |
2,836,748,517 | [MPS] Extend `torch.special.sinc` to complex | malfet | closed | [
"Merged",
"topic: improvements",
"release notes: mps",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146648
And to integral data types as well
Was too lazy to deduce the formula myself(or write a sympy script), but ChatGPT did a decent job of doing it, though it forgot that input must be multiplied by $$\pi$$:
```math
\text{Re... | true |
2,836,723,921 | placeholder for shell dtype improvements in pytorch/pytorch | vkuzo | open | [
"module: internals",
"triaged"
] | 0 | CONTRIBUTOR | ### Context
Context: https://github.com/pytorch/pytorch/issues/146414
Tracking the issues we encounter as we add MX dtypes to core for fixing later.
#### printing of shell dtypes
We should be able to print a tensor with a shell dtype. Currenly this fails for bits, int1..7, uint1..7 dtypes. A workaround for `float4... | true |
2,836,716,420 | UNSTABLE trunk / linux-focal-rocm6.3-py3.10 / test | huydhn | closed | [
"module: rocm",
"module: ci",
"triaged",
"unstable"
] | 1 | CONTRIBUTOR | Part of https://github.com/pytorch/pytorch/issues/146409
They run only in PR when `ciflow/trunk `is used https://github.com/pytorch/pytorch/pull/145629?
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd @seemethere @malfet @pytorch/pytorch-dev-infra | true |
2,836,705,454 | [Inductor] Add a unit test | desertfire | closed | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* (to be filled)
Summary: To follow up https://github.com/pytorch/pytorch/pull/146293, add a JIT Inductor unit test. Other Triton template may need similar fixes.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zh... | true |
2,836,705,164 | Scalar tensor fails to broadcast with shape of in-graph constructed NJT | jbschlosser | open | [
"triaged",
"module: nestedtensor",
"oncall: pt2",
"module: inductor"
] | 0 | CONTRIBUTOR | Repro:
```python
import torch
@torch.compile
def f(values, offsets):
nt = torch.nested.nested_tensor_from_jagged(values, offsets)
return torch.where(nt > 0.0, torch.ones_like(nt), 0)
values = torch.randn(10, 5, device="cuda")
offsets = torch.tensor([0, 2, 5, 7, 10], device="cuda")
output = f(values, offsets)
... | true |
2,836,697,642 | [inductor] Improve codegen for argmax+max | jansel | open | [
"triaged",
"oncall: pt2",
"module: inductor",
"internal ramp-up task"
] | 7 | CONTRIBUTOR | ```py
@torch.compile(dynamic=True)
def fn(x):
return torch.max(x, -1)
```
generates the following code:
```py
@triton.jit
def triton_red_fused_max_0(in_ptr0, out_ptr0, out_ptr1, ks0, xnumel, r0_numel, XBLOCK : tl.constexpr, R0_BLOCK : tl.constexpr):
rnumel = r0_numel
RBLOCK: tl.constexpr = R0_BLOCK
xoff... | true |
2,836,695,721 | [poc] force UntypedStorage.from_buffer(buf) to return meta storage under FakeTensorMode | bdhirsh | closed | [
"Merged",
"release notes: composability"
] | 2 | CONTRIBUTOR | context here: https://fb.workplace.com/groups/326136610199609/permalink/495389539940981/
This PR is an attempt to make it such that if you create a tensor from an external buffer (using `UntypedStorage.from_buffer(buf)`, we can generate a proper fake tensor for you out of the box.
The annoying bit is that there a... | true |
2,836,693,565 | [dim order] solve broken doc | Gasoonjia | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 9 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146641
Differential Revision: [D69265340](https://our.internmc.facebook.com/intern/diff/D69265340/) | true |
2,836,661,764 | POC for mixed prec optim frontend | janeyx99 | open | [
"Stale",
"release notes: optim"
] | 2 | CONTRIBUTOR | This PR is a prototype for what a frontend for asking for mixed precision can look like torch.optim through set_dtype_policy in optimizer.py.
This is not meant to be landable but to start some discussions on what people want/would like to see and to ask if there are things I haven't considered yet.
This currently... | true |
2,836,653,407 | [ONNX] Adjust and add deprecation messages | justinchuby | closed | [
"module: onnx",
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: onnx",
"topic: deprecation",
"ci-no-td"
] | 14 | COLLABORATOR | Adjust and add deprecation messages to torch.onnx utilities and verification methods because they are only related to torch script and are obsolete.
Removed unused `_exporter_states.py` and removed the internal deprecation module in favor of the typing_extensions deprecated decorator. | true |
2,836,597,113 | use None to slice when list has one element only | henrylhtsang | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 10 | CONTRIBUTOR | When autotune_num_choices_displayed is None and the list of choices has length 1, slicing with `[:-1]` means getting all elements except the last one, which resulted in an empty list.
Slicing with `[:None]` works.
Differential Revision: D69265168
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-C... | true |
2,836,588,986 | gloo: fix building system gloo with CUDA/HIP | nlbrown2 | open | [
"module: build",
"module: cuda",
"triaged",
"open source",
"topic: not user facing"
] | 4 | CONTRIBUTOR | Fix incorrect linking of Gloo's libraries when building with system Gloo. Previously, either Gloo's native library or Gloo's CUDA library were linked. However, Gloo had changed such that all users of Gloo must link the native library, and can optionally link the CUDA or HIP library for Gloo + CUDA/HIP support.
This ha... | true |
2,836,582,754 | example repro failure | c00w | closed | [
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146636
Summary:
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,836,580,218 | ByteTensor fails under FakeTensorMode() | haibchen | open | [
"triaged"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
The code block blow reproduces the error, "Attempted to set the storage of a tensor on device "meta" to a storage on different device "cpu". This is no longer allowed; the devices must match" . With FakeTensorMode, the tensor is allocated on 'CPU' device just fine.
```
from torch._subclasses.f... | true |
2,836,519,538 | Add Structured Tracing for Traced Graph Edge Details for AC Debugging | basilwong | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 16 | CONTRIBUTOR | Summary:
Updating the structured trace infrastructure so that we are able to output to Zoomer and have an E2E solution.
Context Doc: https://docs.google.com/document/d/1T6omIBEWVhbOiwDLSLffgQwjxiT2rQv8QvvQwXkw4fY/edit?usp=sharing
Test Plan:
### Testing Structured Log + tlparse locally
Command:
```
TORCH_TRACE=/data/... | true |
2,836,464,358 | [NJT] Fix inference mode for composite implicit ops without nested-specific kernel | soulitzer | closed | [
"Merged",
"ciflow/trunk",
"topic: bug fixes",
"release notes: nested tensor"
] | 12 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #146633
| true |
2,836,339,614 | [ROCm] OCP FP8 Support for new GPUs | petrex | closed | [
"module: rocm",
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: rocm",
"release notes: linalg_frontend",
"ci-no-td"
] | 13 | CONTRIBUTOR | TLDR: Follow up/ Build on top of https://github.com/pytorch/pytorch/pull/144476. add OCP FP8 support for gfx950
refer to https://github.com/pytorch/ao/pull/1677
This pull request includes several changes to improve compatibility and support for new GPU architectures and data types, particularly for ROCm. The key up... | true |
2,836,320,869 | Support ignoring parameters in FSDP2 | ckluk2 | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: distributed (fsdp)",
"ciflow/inductor"
] | 24 | CONTRIBUTOR | Differential Revision: D69153051
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,836,270,226 | [torch] fix builds for older pybind | suo | closed | [
"oncall: jit",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: jit"
] | 4 | MEMBER | Summary:
some versions of pybind we build with don't have `py::set_error`.
So just use the underlying python C API.
Test Plan: unit tests
Differential Revision: D69254629
cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel | true |
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