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 |
|---|---|---|---|---|---|---|---|---|
2,788,707,982 | [MPSInductor] Support `abs` in MetalPrintExpr | 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):
* #144827
* __->__ #144826
* #144796
* #144795
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @deser... | true |
2,788,678,605 | Fix MPS returns 0 on OOB | JoeyShapiro | closed | [
"triaged",
"open source",
"Stale",
"release notes: mps"
] | 2 | NONE | Fixes #144824
This adds additional checks to the MPS code to make sure the index is in range. If the index is out of range, it will cause a crash. This has caused me grief when testing code on MPS, then sending it CUDA. And would have gone unnoticed if I were to fully train on MPS, making my model perform undefined... | true |
2,788,672,010 | [MPS] Indexing Returns 0 if OOB | JoeyShapiro | open | [
"module: error checking",
"triaged",
"module: mps"
] | 3 | NONE | ### 🐛 Describe the bug
Using PyTorch to train a model on MacOS worked fine, so I switched to using CUDA, where it would crash. The issue is that CUDA will crash if you index out of bounds, along with CPU, MPS will return a 0. This causes an inconsistency in models, and will result in undefined behavior on MPS. This w... | true |
2,788,671,765 | Fix torch.normal ignores default_device | zeshengzong | closed | [
"open source",
"Stale"
] | 3 | CONTRIBUTOR | Following #144070 to Fixes #122886
| true |
2,788,666,272 | Performance regression when using @torch.compile compared to no compilation | vladkvit | closed | [
"needs reproduction",
"module: performance",
"oncall: pt2"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
I was playing around with torch.compile functionality, and came across a ~20x slowdown when running this toy code (counts even numbers):
```
import torch
from datetime import datetime
batch_size = 1024 * 8000
final_num = 2147483647
num_batches = final_num // batch_size
device = torch.device(... | true |
2,788,649,799 | Change back to 'linux.rocm.gpu.2'. | amdfaa | closed | [
"module: rocm",
"triaged",
"open source",
"topic: not user facing"
] | 2 | CONTRIBUTOR | Due to limited upstream CI capacity, we thought it would best to have periodic run on all the available upstream CI nodes.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,788,648,887 | [dynamo] Do not always skip code objects unconditionally | williamwen42 | open | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 0 | MEMBER | Currently, when Dynamo determines that a frame should be skipped, we will also skip tracing all future calls to the same code object. This can cause issues when skipping a frame is dependent on inputs to the function:
```python
import torch
@torch.compile(dynamic=False)
def fn(x, n):
if n == 0:
try:
... | true |
2,788,645,880 | restore rng generation for fbcode | ngimel | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"ci-no-td"
] | 16 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,788,631,856 | [aarch64] multiple inductor test failures related to vec128_bfloat16 | tinglvv | closed | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 5 | COLLABORATOR | ### 🐛 Describe the bug
Observing below errors on Grace Hopper GPU across multiple inductor tests
```
/usr/local/lib/python3.12/dist-packages/torch/include/ATen/cpu/vec/vec128/vec128_bfloat16_neon.h:83:37: error: cannot convert ‘__Uint16x8_t’ to ‘__Bfloat16x8_t’
83 | return vreinterpretq_u16_bf16(val); ... | true |
2,788,572,319 | dynamo: Don't crash with internal error if getattr on a tensor fails | c00w | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144817
This prevents crashes when getattr is called on a tensor for something
which doesn't exist.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @k... | true |
2,788,571,850 | [CD] Fix slim-wheel nvjit-link import problem | pytorchbot | closed | [
"open source"
] | 1 | COLLABORATOR | When other toolkit (say CUDA-12.3) is installed and `LD_LIBRARY_PATH` points to there, import torch will fail with
```
ImportError: /usr/local/lib/python3.10/dist-packages/torch/lib/../../nvidia/cusparse/lib/libcusparse.so.12: undefined symbol: __nvJitLinkComplete_12_4, version libnvJitLink.so.12
```
It could not ... | true |
2,788,564,082 | Support kernel options when flex_attention compiled with dynamic=True | sjain-profluent | closed | [
"triaged",
"oncall: pt2",
"module: dynamic shapes",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention"
] | 1 | NONE | ### 🐛 Describe the bug
I am trying to use the kernel_options to specify lower block sizes to improve flex attention performance for specific bidirectional-causal masking pattern that depend on a secondary tensor (here `sequence_ids` in the code below). When I run the code with `dynamic=False` , it runs without error ... | true |
2,788,552,934 | [BE] Parametrize `test_min_max` | pytorchbot | closed | [
"open source",
"topic: not user facing",
"ciflow/mps"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144251
* #144250
* __->__ #144249
It's better to have one unit test per dtype rather a combined one | true |
2,788,543,173 | [executorch hash update] update the pinned executorch hash | pytorchupdatebot | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 12 | COLLABORATOR | This PR is auto-generated nightly by [this action](https://github.com/pytorch/pytorch/blob/main/.github/workflows/nightly.yml).
Update the pinned executorch hash. | true |
2,788,529,647 | Fix the wrong artifact in remaining workflows | huydhn | closed | [
"Merged",
"topic: not user facing",
"ciflow/inductor-perf-compare",
"ciflow/inductor-micro-benchmark",
"ciflow/inductor-micro-benchmark-cpu-x86"
] | 3 | CONTRIBUTOR | I missed them in https://github.com/pytorch/pytorch/pull/144694 as they weren't run often. But they are still failing nonetheless, i.e. https://github.com/pytorch/pytorch/actions/runs/12762640334/job/35578870178
The issue was from https://github.com/pytorch/pytorch/pull/125401 where it added `use-gha: ${{ inputs.us... | true |
2,788,529,565 | remove allow-untyped-defs from nn/utils/_expanded_weights/conv_expanded_weights.py | bobrenjc93 | closed | [] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144811
| true |
2,788,481,417 | Support torch.func.grad for Flex Attention | cora-codes | open | [
"triaged",
"oncall: pt2",
"module: functorch",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention"
] | 9 | NONE | ### 🚀 The feature, motivation and pitch
Currently, flex attention does not support `torch.func.grad`:
```python
import torch
from torch.nn.attention.flex_attention import flex_attention
torch.set_default_device("cuda")
q = torch.randn(1, 1, 1, 16)
k = torch.randn(1, 1, 1, 16)
v = torch.randn(1, 1, 1, 16)
torch.f... | true |
2,788,452,788 | use cooperative schedule in scaled_mm for fast_accum=false | ngimel | closed | [
"Merged",
"ciflow/trunk",
"release notes: cuda"
] | 3 | COLLABORATOR | This improves perf for large matrices by more than 2x, more detailed benchmark coming.
On master
On this branch
<img width="601" alt="image" src="https://github.com/user-attachments/assets/7f55152b-1110-45e4-b2ea-6f274d543869... | true |
2,788,436,356 | Remove C10_EMBEDDED | swolchok | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: cpp"
] | 9 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144808
I added this to support code sharing with ExecuTorch, but the operator<< overrides are load-bearing for builds -- we have other code that attempts to pretty-print Half/BFloat16, and implicit conversions can't be used to mak... | true |
2,788,418,444 | [inductor][BE] don't try/except ImportError for AttrsDescriptor versions | davidberard98 | 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):
* __->__ #144807
motivation: Ed's advice to avoid `except ImportError` (i.e. based on the fact that your target module/class might in fact exist, but you might run into some different ImportError whose stacktrace you now ignore).
additiona... | true |
2,788,398,033 | [export] handle buffer/input mutations for joint-graph | pianpwk | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: export"
] | 6 | CONTRIBUTOR | Summary: previous construction of GraphSignature output specs didn't consider buffer/user input mutations
Test Plan: test_experimental
Differential Revision: D68177409
| true |
2,788,397,408 | "GenericHOPVariable" / abstract out Dynamo support for HOPs | zou3519 | open | [
"triaged",
"oncall: pt2",
"module: dynamo",
"module: higher order operators",
"module: pt2-dispatcher"
] | 0 | CONTRIBUTOR | From HOP sync discussion (with @xmfan).
Idea 1: abstract out Dynamo support for HOPs
Some way to create a HOP where:
1) a user defines how to construct the inputs to each subgraph from the (args, kwargs)
2) using this, we can create a GenericHOPVariable that should be able to handle FX graphs as inputs. Dynamo can al... | true |
2,788,396,184 | Test of RST to MD | sekyondaMeta | closed | [
"Stale",
"topic: not user facing"
] | 2 | CONTRIBUTOR | Test of rst to md conversion
DO NOT MERGE | true |
2,788,354,655 | Something is fishy with discard_graph_changes | zou3519 | open | [
"triaged",
"oncall: pt2",
"module: higher order operators",
"module: pt2-dispatcher"
] | 1 | CONTRIBUTOR | Discovered with @yanboliang in https://github.com/pytorch/pytorch/pull/142830#discussion_r1913437378
cc @chauhang @penguinwu @ydwu4 @bdhirsh @yf225.
What's going on is:
1) we do a discard_graph_changes
2) then we do a speculate_subgraph, which gives us some lifted_freevars
The lifted_freevars map proxies from the di... | true |
2,788,325,314 | Add non_c_binding torch functions to allowlist for AOTAutogradCache, confirm no special handlers for them | jamesjwu | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144802
Differential Revision: [D68173093](https://our.internmc.facebook.com/intern/diff/D68173093/)
This diff allows any function in torch_non_c_binding_in_graph_functions to be safe to cache. These functions should be safe to ... | true |
2,788,292,455 | [ONNX] Use python_dispatcher in type promotion | justinchuby | closed | [
"module: onnx",
"open source",
"Merged",
"ciflow/trunk",
"release notes: onnx",
"topic: improvements"
] | 4 | COLLABORATOR | Fix #143118
Use python_dispatcher in the type promotion pass to preserve symbolic shapes according to @angelayi 's suggestions. (Thanks!)
Tested locally. I wasn't able to create a minimal repro except for using the full model | true |
2,788,257,912 | [fsdp2] maybe unreliable `set_unshard_in_backward(False)` | leonardo0lyj | closed | [
"oncall: distributed"
] | 1 | NONE | Hey Andrew @awgu,
As a big fan of FSDP2, I keep posting improvement 😄
This flag ([`set_unshard_in_backward(False)`](https://github.com/pytorch/pytorch/blob/aa57f0c6637d4377d2d86d377fdf41840498960a/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L408)) is super helpful to skip `unshard()` in backward pass especia... | true |
2,788,239,172 | Update ck | alugorey | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: releng",
"skip-pr-sanity-checks"
] | 22 | CONTRIBUTOR | Updates the CK version and re-implements kernel generation
cc @albanD | true |
2,788,218,021 | [MPSInductor] Add `min`/`max` to MetalExprPrinter | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144796
* #144795
* __->__ #144798
After that `GPUTests::test_avg_pool2d8_mps` and `GPUTests::test_avg_pool2d5_mps` passes
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayi... | true |
2,788,199,965 | [AMD] De-noise tf32 warnings | xw285cornell | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | CONTRIBUTOR | Summary: This is way too noisy especially during unit tests. So just log once.
Test Plan: OSS CI. Tested on a unit test and now I only see one line (hard to notice :) ).
Differential Revision: D68167633
| true |
2,788,190,330 | Fix FakeTensor device creation for MPS | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144827
* #144826
* __->__ #144796
* #144795
By promoting torch.device("mps") to `torch.device("mps:0")`, but skipping `is_initialized` check, as MPS does not really support multi-GPU right now
This fixes `GPUTests.test_remove_no_ops_mps`
... | true |
2,788,190,250 | [BE] Extend `test_remove_no_ops` | malfet | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144827
* #144826
* #144796
* __->__ #144795
----
- Use `is_dtype_supported` to skip dtype promotions portion of the test on unsupported device
- Extend it to use `torch.float16` so promotions could be checked there
- Implement `CpuInt... | true |
2,788,167,847 | [c10d][NCCL] Implement ncclCommInitRankScalable (merging #136789) | fduwjj | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)",
"ciflow/binaries_wheel"
] | 8 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144794
Try to land https://github.com/pytorch/pytorch/pull/136789/files on our end and fix any remaining issues.
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,788,152,394 | [CUDAGraph][Docs] add `cuda` to `torch.randn` | BoyuanFeng | closed | [
"Merged",
"module: cuda graphs",
"ciflow/trunk",
"topic: not user facing"
] | 6 | CONTRIBUTOR | Previous doc example created `torch.randn` tensor on cpu so CUDAGraph was skipped.
Fixes #144386
cc @mcarilli @ezyang @eellison @penguinwu | true |
2,788,147,618 | massive number of runtime asserts can hamper compile times | bdhirsh | closed | [
"high priority",
"triage review",
"oncall: pt2",
"module: dynamic shapes"
] | 5 | CONTRIBUTOR | internal xref: https://fb.workplace.com/groups/1075192433118967/permalink/1585006445470894/
We're spending ~2 hours in one job cranking out a few thousand runtime asserts. The relevant bit is this section of the logs:
```
# .... 4000 lines of runtime asserts
[trainers0]:[rank0]:I0114 06:02:10.688613 5302 torch/fx/exp... | true |
2,788,126,685 | fix as_bool serde | avikchaudhuri | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: export"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144791
Differential Revision: [D68167701](https://our.internmc.facebook.com/intern/diff/D68167701/) | true |
2,788,117,169 | [torch][ao][EASY] Change print to log in numeric debugger to avoid large output | dulinriley | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: quantization",
"release notes: AO frontend"
] | 4 | CONTRIBUTOR | Summary:
This print statement was spewing a bunch of data in logs by default, but it should
be silenceable.
Use `log.debug` instead.
Differential Revision: D68166823
| true |
2,788,109,990 | [c10d][ez] Add comments to the end of Macro for better readability | fduwjj | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144794
* __->__ #144789
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,788,073,242 | Avoid the builtin `numbers` module. | randolf-scholz | closed | [
"module: distributions",
"module: typing",
"triaged",
"actionable"
] | 5 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
Currently, torch uses the builtin [`numbers`](https://docs.python.org/3/library/numbers.html) module [in a few places (only ~40 hits)](https://github.com/search?q=repo%3Apytorch%2Fpytorch%20%2Ffrom%20numbers%20import%7Cimport%20numbers%2F&type=code). However, the `numbers` modu... | true |
2,788,028,918 | torch.compile() within TorchDispatchMode always causes an unknown guard failure. | galv | open | [
"triaged",
"module: __torch_dispatch__",
"oncall: pt2",
"module: dynamo",
"dynamo-triage-jan2025"
] | 5 | COLLABORATOR | ### 🐛 Describe the bug
When I run torch.compile() under an "infra" TorchDispatchMode, it seems that a recompile always happens, but I don't know what guard is failing:
```
import torch
from torch.overrides import TorchFunctionMode
from torch.utils._python_dispatch import TorchDispatchMode
from torch._dynamo import ... | true |
2,788,005,968 | test | angelayi | closed | [
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aakhundov @BoyuanFeng | true |
2,787,960,566 | speculation_log: Raise a unique error for divergence issues | c00w | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144785
This is primarily sent for discussion and to see what tests fail due to
this. The idea is that rather than capturing this as a regex on the
fail_reason, just give it a unique failure type
cc @voznesenskym @penguinwu @EikanWan... | true |
2,787,959,498 | symbolic_convert: Don't fail when we hit a undefined name | c00w | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 15 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144784
We're using a python builtin NameError here,
instead of throwing a Unsupported exception. This causes the
NameError to get wrapped in a InternalTorchDynamoError
instead of just causing a graph break, and letting the user c... | true |
2,787,944,513 | [codemod] Remove unused-variable in caffe2/aten/src/ATen/native/quantized/cpu/fbgemm_utils.cpp +1 | r-barnes | closed | [
"module: cpu",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: quantization",
"release notes: cpp",
"topic: improvements",
"topic: not user facing"
] | 4 | CONTRIBUTOR | Summary:
LLVM-15 has a warning `-Wunused-variable` which we treat as an error because it's so often diagnostic of a code issue. Unused variables can compromise readability or, worse, performance.
This diff either (a) removes an unused variable and, possibly, it's associated code or (b) qualifies the variable with `... | true |
2,787,897,138 | Fix triton masked loading for non-block tl.loads | isuruf | closed | [
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor",
"ci-no-td"
] | 17 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144782
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aakhundov @Bo... | true |
2,787,859,796 | [FSDP2] Make post-backward condition more robust | awgu | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: distributed (fsdp2)"
] | 3 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144781
Fixes https://github.com/pytorch/pytorch/issues/144755
cc @H-Huang @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,787,773,274 | [AOTI] Mark run_impl as optnone | desertfire | closed | [
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144780
Summary: Optimizing a large cpp wrrapper code can be really slow. Using this PR to measure the impact of setting optnone.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe... | true |
2,787,710,102 | Adding Infiniband to RDZV Backend for optimal torch run training | ArkashJ | open | [
"oncall: distributed",
"module: docs"
] | 10 | NONE | ### 📚 The doc issue
The documentation can include more information on optimally using infiniband for running ML trainings using torchrun. It would be helpful to add the bash commands for users to see how to set the RDZV host to be the the infiniband url. *For Nvidia GPUs*
**Infiniband URL**
One can run `ifconfig` an... | true |
2,787,642,883 | `torch.profiler.record_function` doesn't register kernels from `backward` function | anmyachev | open | [
"oncall: profiler"
] | 3 | COLLABORATOR | ### 🐛 Describe the bug
`__profile_kernel_of_func` (`record_function` label) shows zero timings for XPU (maybe for `CUDA` the situation is the same, but I have no way to check) unless `record_function` is used inside `backward` function.
```python
import torch
from torch.profiler import profile, ProfilerActivity, rec... | true |
2,787,609,917 | [ROCm] CK SDPA - Move arch check to CK patch | alugorey | closed | [
"module: rocm",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: releng",
"skip-pr-sanity-checks",
"rocm",
"ciflow/rocm"
] | 11 | CONTRIBUTOR | __gfxXXX__ should only be visible by device code. Move the check to the ck kernel
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd @albanD | true |
2,787,598,412 | Slow performance when running TransformerDecoder with low batch size in fp16 | DaniNem | closed | [] | 1 | NONE | ### 🐛 Describe the bug
Hey!
I want to understand why this snippet of code runs slow for batch sizes of 2/4/8 when using fp16, the time it takes for bs=2 on my system is 0.55 sec for no batch version and 2.8 sec for the batched on.
```
from torch import nn
import torch
import time
from torch.amp import autocast
dev... | true |
2,787,593,946 | compile time regression 1/9 | zou3519 | closed | [
"high priority",
"triaged",
"oncall: pt2",
"module: compile-time"
] | 16 | CONTRIBUTOR | [TorchInductor OSS Compile Time Dashboard](https://www.internalfb.com/intern/unidash/dashboard/?tab_id=1587385408528217)
- torchbench inference: sam_fast_dynamo_benchmark 70->84
- HF inference: BartForConditionalGeneration 32->42
- TIMM inference (a lot of models regressed)
cc @ezyang @gchanan @kadeng @msaroufim @ch... | true |
2,787,557,214 | Enable CPP Extension Open Registration tests on Arm | murste01 | open | [
"triaged",
"open source",
"topic: not user facing"
] | 4 | CONTRIBUTOR | Enables most tests under CPP Extension Open Registration as they pass on Arm now.
| true |
2,787,518,284 | [caffe2] Use the manifold cache backend as the default | AishwaryaSivaraman | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 17 | CONTRIBUTOR | Test Plan: CI
D68155591
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,787,506,259 | aot_inductor TIMM convit_base inference regression on dashboard | zou3519 | closed | [
"high priority",
"triaged",
"oncall: pt2",
"oncall: export",
"module: aotinductor",
"pt2-pass-rate-regression"
] | 2 | CONTRIBUTOR | See https://hud.pytorch.org/benchmark/timm_models/inductor_aot_inductor?dashboard=torchinductor&startTime=Tue,%2031%20Dec%202024%2015:26:32%20GMT&stopTime=Tue,%2014%20Jan%202025%2015:26:32%20GMT&granularity=hour&mode=inference&dtype=bfloat16&deviceName=cuda%20(a100)&lBranch=main&lCommit=1dab79470dbecef79ba4c7d4308d8a18... | true |
2,787,470,791 | Removed unused _RequiredParameter | dmpiergiacomo | closed | [
"triaged",
"open source",
"Merged",
"Reverted",
"Stale",
"ciflow/trunk",
"topic: bc breaking",
"ciflow/inductor",
"release notes: optim",
"ci-no-td"
] | 26 | CONTRIBUTOR | As per this [discussion](https://discuss.pytorch.org/t/a-question-about-requiredparameter/137977), I figured that `_RequiredParameter` is no longer used.
The `required` object was initially introduced in this [PR](https://github.com/pytorch/pytorch/commit/4db66679238dae8539c270a61f60b9c0c4bb440d) as the `SGD` optimi... | true |
2,787,298,410 | [ARM] multiple test failures in TestQuantizedConv on Aarch64 | robert-hardwick | open | [
"oncall: quantization",
"module: tests",
"module: arm"
] | 3 | COLLABORATOR | ### 🐛 Describe the bug
After enabling 'test_quantization" we consistently see 2 test failures on all Aarch64 platforms.
**TestQuantizedConv.test_qconv2d_relu** and **TestQuantizedConv.test_qconv2d**
```
The failure output is
AssertionError:
Arrays are not almost equal to 0 decimals
X: tensor([[[[0.0000, 0.0000, 2... | true |
2,787,292,491 | Mark CUDA-12.6 as experimental for 2.6 release | malfet | closed | [
"Merged",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Because that's the first time we are trying to release it, and it also is the first release to use manylinux2_28 | true |
2,787,280,299 | Is it possible to remove NCCL submodule and use only nccl binaries from pypi instead ? | atalman | open | [
"module: build",
"module: cuda",
"triaged",
"module: nccl"
] | 8 | CONTRIBUTOR | ### 🐛 Describe the bug
Currently we do both we have submodule:
https://github.com/pytorch/pytorch/tree/main/third_party/nccl
And we use pypi nccl binaries:
https://github.com/pytorch/pytorch/blob/main/.github/scripts/generate_binary_build_matrix.py#L62
And we have a code to check if submodule version is consistent ... | true |
2,787,236,868 | Unable to build with ATEN_THREADING=TBB option | carusyte | open | [
"module: build",
"module: docs",
"triaged",
"module: tbb"
] | 4 | NONE | While the doc [here](https://pytorch.org/docs/stable/notes/cpu_threading_torchscript_inference.html) says we could set the `ATEN_THREADING` build option to TBB, I encountered the following error:
```
<- omitted previous log for brevity ->
-- Looking for backtrace
-- Looking for backtrace - found
-- backtrace facility ... | true |
2,787,193,050 | Matmul with int32 parameters on Intel GPU leads to errors | qwqdlt | open | [
"triaged",
"module: xpu"
] | 8 | NONE | ### 🐛 Describe the bug
torch.matmul with int32 parameters leads to errors, when running on XPU (Intel GPU) in the following program.
```python
import numpy as np
import torch
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.val = torch.nn.Parameter(torch.ones([1], dtyp... | true |
2,787,176,572 | Fix full_like decomposition to preserve strides | isuruf | open | [
"oncall: distributed",
"open source",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144765
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenya... | true |
2,787,054,655 | EZ fix to make sure local pytest run succeeds in export | tugsbayasgalan | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144764
Previously run_tests() was protected under IS_FBCODE flag so that following works:
```
python test/export/test_export_legacy.py
```
But it fails on:
```
pytest test/export/test_export_legacy.py
```
This is bec... | true |
2,787,020,727 | Unconditional dependency on setuptools | adamjstewart | closed | [
"triaged",
"open source",
"topic: not user facing"
] | 33 | CONTRIBUTOR | In [segmentation-models.pytorch](https://github.com/qubvel-org/segmentation_models.pytorch/actions/runs/12753548754/job/35545453127#step:6:3442), we noticed that actions like `torch.compile` actually require setuptools for all Python versions. Setuptools is unconditionally imported in `torch/utils/cpp_extension.py`. Th... | true |
2,786,988,144 | Remove optimization pass to reduce number of copies in export IR | tugsbayasgalan | closed | [
"ciflow/trunk",
"release notes: export"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144762
This pass seems like a big headache because it fails to distinguish user-introduced copy_ vs the ones export introduce. The original motivation was that when we do run_decompositions, we unlift the exported program which ... | true |
2,786,918,451 | torch.rand_like() for nested tensors | kkj15dk | closed | [
"triaged",
"module: nestedtensor"
] | 1 | NONE | ### 🚀 The feature, motivation and pitch
torch.randn_like() works for nested tensors, however torch.rand_like() does not
Is there a reason for this, I was imagining the implementation would be quite similar?
I need to change elements in a nested tensor randomly, the probability given by a uniform distribution. Mayb... | true |
2,786,694,443 | [Intel CPU] Fix issue #143482. | RanTao123 | open | [
"triaged",
"open source",
"Stale",
"topic: not user facing"
] | 5 | CONTRIBUTOR | Fix issue in https://github.com/pytorch/pytorch/issues/143482.
To aviod out-of-bound aceess, values in indices should be less than num_weights. | true |
2,786,606,462 | [Intel GPU] Avoid unnecessary copy when the dst of Matmul is non-contiguous | jianyizh | closed | [
"module: cpu",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/xpu",
"module: xpu"
] | 13 | CONTRIBUTOR | We should not always call contiguous on the dst of matmul. We have already removed copy of matmul input in https://github.com/pytorch/pytorch/pull/143784
I also fixed an accuracy issue by using onednn sum post op instead of binary add in the case of inplace to avoid UT failure.
cc @jgong5 @mingfeima @XiaobingSupe... | true |
2,786,463,069 | [APS] Update proxy_tensor to take kwargs | yvonne-lab | closed | [
"fb-exported",
"Stale",
"release notes: fx",
"fx"
] | 5 | NONE | Summary: `__init__` should be able to take kwargs. This diff updates `AttrProxy` to take kwargs when initializing the proxy class.
Test Plan: Existing unit tests
Differential Revision: D68092190
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,786,431,845 | Installation is broken: `pip3 install torch torchvision torchaudio` fails | hirak99 | closed | [
"module: binaries",
"triaged",
"module: python version"
] | 4 | NONE | ### 🐛 Describe the bug
Installation is broken.
Following the instructions here, https://pytorch.org/get-started/locally/ if I select (Stable, Linux, Pip, Python, Cuda 12.4), it says I should run the following - `pip3 install torch torchvision torchaudio`.
**Replication of Issue**
Create a new venv and run this -
`... | true |
2,786,384,412 | [Reopen] [Intel GPU] Set higher tolerance for some models only on XPU Device | retonym | open | [
"triaged",
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ci-no-td"
] | 20 | CONTRIBUTOR | Reopen the previous stale closed PR https://github.com/pytorch/pytorch/pull/134192
We need to increase the tolerance slightly to ensure that certain models pass accuracy check on the XPU device.
This pull request preserves the original tolerance threshold for the CUDA device and introduces a new key higher_fp16_bf1... | true |
2,786,333,527 | [fsdp2] maybe unreliable condition check to enforce post_backward() | leonardo0lyj | closed | [
"triaged",
"module: fsdp"
] | 0 | NONE | Hey Andrew @awgu,
As a big fan of FSDP2, I keep posting improvement 😄
This line of condition check to enforce`post_backward()` at root backward maybe a bit unreliable:
`if fsdp_param_group and (fsdp_param_group.is_unsharded
or not fsdp_param_group.unshard_in_backward):
fsdp_param_group.post_backward()`
ht... | true |
2,786,321,057 | [WIP][Intel GPU] [pt2e] remove h2d copy of scale and zero point in int8 conv | jianyizh | closed | [
"module: cpu",
"open source",
"release notes: quantization",
"module: inductor"
] | 2 | CONTRIBUTOR | Not ready for review
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @voznesenskym @penguinwu @EikanWang @Guobing-Chen @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aakhundov @BoyuanFeng | true |
2,786,241,683 | [DCP] Fix fsspec fsync bug on .finish() | cassanof | closed | [
"oncall: distributed",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 7 | CONTRIBUTOR | Fixes #144752
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @LucasLLC @MeetVadakkanchery @mhorowitz @pradeepfn @ekr0 | true |
2,786,238,092 | [DCP] BUG: FsspecWriter calls os.fsync on .finish(), therefore program crashes on checkpoint save | cassanof | closed | [
"triaged",
"oncall: distributed checkpointing"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
With Fsspec, you can't fsync on a file. This has caused bugs during the write phase, which were fixed here: https://github.com/pytorch/pytorch/pull/119287
However, this issue has not been fixed for the .finish method, which is inherited by `FsspecWriter`:
https://github.com/pytorch/pytorch/blo... | true |
2,786,226,212 | [dynamo] Add `--profile-details` and `--export-perfdoctor` option | xuzhao9 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 43 | CONTRIBUTOR | Summary:
Add `--profile-details` option to add shapes and other details to the Kineto profile.
Add `--export-perfdoctor` to directly dump trace to perfdoctor for webview.
Test Plan:
```
$ buck2 run mode/opt //caffe2/benchmarks/dynamo:torchbench_internal -- --only mrs_video_watch_over --performance --training --amp --... | true |
2,786,216,298 | Failed to export the model to ONNX | asdfmnbvuj | closed | [
"module: onnx",
"triaged"
] | 1 | NONE | ### 🐛 Describe the bug
onnx_program = torch.onnx.dynamo_export(self.enconder_dust, (x,pos))
### Versions
Traceback (most recent call last):
File "/opt/miniconda3/envs/tensor_rt/lib/python3.11/site-packages/torch/onnx/_internal/_exporter_legacy.py", line 1222, in dynamo_export
).export()
^^^^^^^^
File ... | true |
2,786,215,632 | fix torch.atan for torch.complex datatypes on CPU | jiayisunx | closed | [
"module: cpu",
"open source",
"Merged",
"ciflow/trunk",
"release notes: complex"
] | 4 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144749
Fix https://github.com/pytorch/pytorch/issues/141487.
This issue is caused by the lack of special handling of the case where the real number/imag number is 0/Inf/NaN in the vectorized implementation of `atan`. For correc... | true |
2,786,178,498 | [inductor] [cuda] [fake tensor] `torch.nextafter` loose the check for different device tensor on inductor | shaoyuyoung | open | [
"triaged",
"oncall: pt2",
"module: fakeTensor",
"module: inductor",
"module: pt2-dispatcher"
] | 6 | CONTRIBUTOR | ### 🐛 Describe the bug
Actually, I am not sure whether it is the eager issue or inductor?
Because from my personal understanding, I think eager should pass the check like `torch.add` (`x = torch.nextafter(x, torch.tensor(1.0))` can pass the check)
```python
import torch
import torch.nn as nn
import torch.nn.functiona... | true |
2,786,174,062 | add fp8 support to index_cuda | danielvegamyhre | closed | [
"Merged",
"ciflow/trunk",
"release notes: quantization"
] | 6 | CONTRIBUTOR | Fixes #133605
**Summary**
This PR adds support for FP8 data types to the `index_cuda` op.
It uses `AT_DISPATCH_V2` which is a new macro that can handle arbitrary number of dtypes, as opposed to the old implementations which had a separate macro for each possible number of dtype arguments (e.g. `AT_DISPATCH_AL... | true |
2,786,167,538 | expose extra torch_python apis | garfield1997 | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | CONTRIBUTOR | Fixes #144302
After checking the code of my third-party devices, I think these APIs are also relied on by us, so I exposed them according to the discussion in the issue. | true |
2,786,151,984 | [BE] Make a SymbolInfo NamedTuple | ezyang | closed | [
"Merged",
"ciflow/trunk",
"release notes: fx",
"topic: not user facing",
"fx",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144745
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
cc @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,786,151,846 | Allow GradientEdge as torch.autograd.backward outputs | soulitzer | closed | [
"Merged",
"ciflow/trunk",
"release notes: autograd",
"topic: improvements"
] | 9 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144744
| true |
2,786,147,732 | [BE] Remove lambda from str | ezyang | closed | [
"Merged",
"release notes: fx",
"fx",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144745
* __->__ #144743
* #144471
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
cc @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,786,134,646 | outerNode->outputs().size() | asdfmnbvuj | closed | [
"oncall: jit",
"module: onnx"
] | 1 | NONE | ### 🐛 Describe the bug
RuntimeError: outerNode->outputs().size() == node->inputs().size() INTERNAL ASSERT FAILED at "/opt/conda/conda-bld/pytorch_1729647348947/work/torch/csrc/jit/passes/dead_code_elimination.cpp":138, please report a bug to PyTorch.
Traceback (most recent call last):
File "/opt/miniconda3/env... | true |
2,786,114,801 | [cherry-pick][dtensor] expose the __create_chunk_list__ in the doc (#144100) | wanchaol | closed | [
"oncall: distributed",
"open source",
"ciflow/inductor"
] | 3 | COLLABORATOR | as titled, this PR expose this dunder method as a public API in the doc, so that different checkpoint implementations can leverage this protocol, instead of exposing a separate API
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144100
Approved by: https://github.com/awgu
ghstack dependencies: #1440... | true |
2,786,110,463 | [cherry-pick] [dtensor] improve doc of the DTensor class (#144099) | wanchaol | closed | [
"oncall: distributed",
"ciflow/inductor"
] | 1 | COLLABORATOR | as titled: explicitly list all public members to make sure the public API stays consistent, also use groupwise as the member order to make doc look better
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144099
Approved by: https://github.com/awgu
(cherry picked from commit 48a05ee7735709406b782474... | true |
2,786,076,408 | Update torch-xpu-ops commit pin | xytintel | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/xpu"
] | 6 | CONTRIBUTOR | Update the torch-xpu-ops commit to [22cc419e4e60f469341712a5a103fa309a7dfd48](https://github.com/intel/torch-xpu-ops/commit/22cc419e4e60f469341712a5a103fa309a7dfd48), includes:
- Fix building issue https://github.com/intel/torch-xpu-ops/issues/1279
- Aten operator coverage improvement
Note: new torch-xpu-ops com... | true |
2,786,073,401 | Back out "[Submodule] Upgrade to Cutlass 3.6" | drisspg | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: sparse"
] | 6 | CONTRIBUTOR | Summary: Revert due to perf regressions see: https://github.com/pytorch/pytorch/issues/144729
Test Plan: sand castle
Differential Revision: D68137326
| true |
2,786,066,893 | Revert D67866269 | drisspg | closed | [
"fb-exported",
"release notes: sparse"
] | 3 | CONTRIBUTOR | Summary:
This diff reverts D67866269
https://www.internalfb.com/tasks/?t=212439515
Perf regression
Test Plan: NA
Differential Revision: D68137255
| true |
2,785,993,987 | [inductor] fix index.Tensor fallback | shunting314 | 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):
* __->__ #144736
The original issue is we see accuracy problem in a meta internal model [meta internal link](https://fb.workplace.com/groups/1075192433118967/posts/1567334737238065/). The debugging is hard but the root cause is relatively si... | true |
2,785,988,105 | Use random64 in Fischer-Yates algorithm for large N (#143682) | kit1980 | closed | [] | 1 | CONTRIBUTOR | Fixes bug in randperm https://nbsanity.com/static/a4774194938414dedcec7d6e99727d31/Shuffling_20in_20torch_20vs_20numpy-public.html
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143682
Approved by: https://github.com/eqy, https://github.com/albanD, https://github.com/malfet
Fixes #ISSUE_NUMBER
| true |
2,785,962,154 | [Pipelining] fix test_schedule.py (missing destroy_process_group | wconstab | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144734
* #144596
* #144352
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @d4l3k @c-p-i-o | true |
2,785,960,982 | optimize the decomposition of aten.native_group_norm | jiayisunx | closed | [
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 10 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144733
Summary:
Optimize the decomposition of aten.native_group_norm. Reduce unnecessary repeated operations by changing the order of operations for `mean`, `rstd`, `weight`, `bias `and `input`, which can improve performance when `... | true |
2,785,951,052 | [MPS] Fix bitwise shifts for uint8 | pytorchbot | closed | [
"open source",
"release notes: mps",
"ciflow/mps"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144251
* #144250
* #144249
Previosly all bitwise operations were aliased to the same type, but this is wrong for shift ops
Rather than building an overly complex logic, let's just instantiate using shared `scalarToMetalTypeString` h... | true |
2,785,936,123 | [mps/inductor] Add support for `round()` | dcci | closed | [
"Merged",
"topic: not user facing",
"module: mps",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | MEMBER | With this change, inductor/test_view_on_aliased passes.
cc @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhan... | true |
2,785,925,137 | Revert "Use random64 in Fischer-Yates algorithm for large N (#143682)… | kit1980 | closed | [
"release notes: dataloader"
] | 1 | CONTRIBUTOR | … (#143875)"
This reverts commit b1a10ecad96f04db9baff453ae42ef4dd45b62f4.
Fixes #ISSUE_NUMBER
| true |
2,785,922,646 | [Perf] Flash-Attn Bwd slow down w/ cutlass 3.6.0 in General | drisspg | closed | [
"high priority",
"triage review",
"module: cuda"
] | 12 | CONTRIBUTOR | # Summary
### Update
In fact appears that 3.6.0 is in general slower than 3.5.1 for FAv2 at its current state:
| Batch Size | Sequence Length | Forward Pass Slowdown | Backward Pass Slowdown |
|------------|----------------|----------------------|----------------------|
| 1 | 128 | 1.51x slower ... | true |
2,785,897,200 | [caffe2][remove dead code] Removed unused zippydb code | AishwaryaSivaraman | closed | [
"fb-exported",
"Stale",
"module: dynamo"
] | 6 | CONTRIBUTOR | Summary:
Edward mentioned ZippyDb Cache is no longer used, removing the zippy db bits.
this leads to 563 reduced transitive deps
Test Plan: CI?
Reviewed By: oulgen
Differential Revision: D68083897
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayis... | true |
2,785,885,928 | Register nonzero for meta device for FBLSim | lurunming | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: fx",
"fx"
] | 10 | CONTRIBUTOR | Summary:
Fix `nonzero is not registered to meta` issue:
```
"NotImplementedError: aten::nonzero: attempted to run this operator with Meta tensors, but there was no fake impl or Meta kernel registered".
```
Reviewed By: ezyang
Differential Revision: D66525640
cc @ezyang @SherlockNoMad @EikanWang @jgon... | true |
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