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 |
|---|---|---|---|---|---|---|---|---|
3,009,359,765 | DISABLED test_builtin_score_mods_different_block_size_float16_score_mod4_BLOCK_SIZE3_cuda_float16 (__main__.TestFlexAttentionCUDA) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: inductor"
] | 2 | NONE | Platforms: linux
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_builtin_score_mods_different_block_size_float16_score_mod4_BLOCK_SIZE3_cuda_float16&suite=TestFlexAttentionCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/... | true |
3,009,359,282 | DISABLED test_matmul_layer_norm_dynamic_shapes_cpu (__main__.DynamicShapesCpuTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: inductor"
] | 4 | NONE | Platforms: linux
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_matmul_layer_norm_dynamic_shapes_cpu&suite=DynamicShapesCpuTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/40881678238).
Over the pa... | true |
3,009,359,280 | DISABLED test_cublas_addmm_size_1000_cuda_bfloat16 (__main__.TestMatmulCudaCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"module: linear algebra",
"skipped"
] | 5 | NONE | Platforms: linux
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_cublas_addmm_size_1000_cuda_bfloat16&suite=TestMatmulCudaCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/40883373157).
Over the past ... | true |
3,009,358,174 | [export] set is_exporting() for strict | pianpwk | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"release notes: export"
] | 7 | CONTRIBUTOR | Helpful for upcoming work in figuring when to use stack trace in prettifying dynamic shapes errors
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
3,009,321,840 | Graph Partition Issue Tracker | BoyuanFeng | open | [
"triaged",
"module: cuda graphs",
"oncall: pt2",
"module: inductor"
] | 0 | CONTRIBUTOR | This issue tracks work items for graph partition which is a [feature](https://github.com/pytorch/pytorch/issues/125864) to increase cudagraph coverage. It splits off non-cudagraphable ops and cudagraphifies the remaining ops.
Features:
- [x] Inductor graph partition #147038
- [x] Cudagraph partition #147648
- [x] Dyn... | true |
3,009,312,779 | [ONNX] Update ONNX on CI | titaiwangms | closed | [
"module: onnx",
"open source",
"ciflow/trunk",
"topic: not user facing"
] | 4 | COLLABORATOR | Update ONNX version on CI (split from #151694 )
| true |
3,009,295,944 | [CUDA][TF32] Account for TF32 in `test_corrcoef` | eqy | closed | [
"module: cuda",
"module: complex",
"open source",
"Merged",
"module: tf32",
"ciflow/trunk",
"topic: not user facing"
] | 3 | COLLABORATOR | cc @ptrblck @msaroufim @jerryzh168 @ezyang @anjali411 @dylanbespalko @mruberry @nikitaved @amjames @zasdfgbnm | true |
3,009,287,485 | profile for torch.add(x, x) where x is a zero-sized tensor looks bogus | zou3519 | open | [
"oncall: profiler"
] | 6 | CONTRIBUTOR | ```py
from torch.profiler import profile, record_function, ProfilerActivity
import torch
x = torch.randn(0)
with profile(activities=[ProfilerActivity.CPU], record_shapes=True) as prof:
with record_function("model_inference"):
x + x
print(prof.key_averages().table(sort_by="cpu_time_total", row_limit=10)... | true |
3,009,262,596 | Add device check for inputs | yushangdi | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor (aoti)"
] | 8 | CONTRIBUTOR | Summary: Generate device checks for inputs in AOTI. Enable with AOTI_RUNTIME_CHECK_INPUTS=1
Test Plan:
```
buck run fbcode//mode/dev-nosan //caffe2/test/inductor:test_aot_inductor -- -r test_runtime_checks_device_type_failed
```
Differential Revision: D73382824
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guob... | true |
3,009,247,165 | [export] warn when Dim.AUTO 0/1 specializes | pianpwk | closed | [
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: export"
] | 15 | CONTRIBUTOR | Fixes #151582
example warning for Dim.AUTO:
```
torch/_export/non_strict_utils.py:499] dimension inputs['x'].shape[1] 0/1 specialized; Dim.AUTO was specified along with a sample input with hint = 1.
```
example error when Dim.DYNAMIC specializes:
```
- Received user-specified dim hint Dim.DYNAMIC(min=None, ... | true |
3,009,238,130 | [ONNX] Update decomposition logic to loop over onnx registry | titaiwangms | closed | [
"module: onnx",
"open source",
"Merged",
"ciflow/trunk",
"release notes: onnx",
"topic: bug fixes"
] | 8 | COLLABORATOR | Fixes #150367
This PR makes decomposition table from onnx registry, which includes registered ops not only ATen and prim. This will help to keep the custom ops that are specified in the custom_translation table from decomposition during ONNX export. | true |
3,009,225,842 | [cutlass backend] Move cutlass compiled cache to cache_dir | henrylhtsang | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 8 | CONTRIBUTOR | Moved "compiled_cache.db" to cache folder.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
3,009,206,644 | [Sana][HybridCache] Fix bug in detect_attr_assignment | tugsbayasgalan | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: AO frontend"
] | 6 | CONTRIBUTOR | Summary: tree_flatten_with_map will internally call unflatten function with user supplied function. But this function was not returning anything causing the leaves to be None. This is wrong when the constructor is sensitive to this behaviour
Test Plan: CI
Differential Revision: D73388529
| true |
3,009,151,254 | Optimize printing sympy expressions during logging and cache key computation | laithsakka | closed | [
"triaged",
"oncall: pt2",
"module: dynamic shapes"
] | 0 | CONTRIBUTOR | repo:
```
import torch
def _cumsum(o):
ret = [0] * (len(o) + 1)
for i in range(len(o)):
ret[i + 1] = ret[i] + o[i]
return ret
@torch.compile(dynamic=True)
def func(o):
out = _cumsum(o)
return out
func([i for i in range(2000)])
```
We have a fast print implementation used in inductor here
... | true |
3,009,147,832 | Support more dtypes for input, indices in gather | isuruf | closed | [
"module: cpu",
"open source",
"Merged",
"ciflow/trunk",
"release notes: cuda",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 9 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151715
* __->__ #151822
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @jerryzh168 @voznesenskym @penguinwu @EikanWang @Guobing-Chen @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 ... | true |
3,009,138,412 | Updates NCCLConfig with QOS variable | syed-ahmed | closed | [
"oncall: distributed",
"open source",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 3 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151821
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k | true |
3,009,101,560 | Pytorch aten::col2im not currently supported on the MPS backend | cats256 | closed | [
"triaged",
"module: mps"
] | 2 | NONE | ### 🚀 The feature, motivation and pitch
The aten::im2col was implemented but the backward version aten::col2im is not.
```
import torch
import torch.nn.functional as F
device = "mps" if torch.backends.mps.is_available() else "cpu"
if __name__ == '__main__':
print("torch version:", torch.__version__)
tenso... | true |
3,009,086,925 | [SymmMem] Add all_to_all_vdev | kwen2501 | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151993
* __->__ #151819
* #151498
* #151261
Merge in/out splits into one tensor
Multi-block
Use sync instead of barrier
Use nvshmemx_collective_launch
Rotate blocks among peer
write back input splits
Parallel scan works
Use scan for ... | true |
3,009,078,319 | use vectorized loads and stores for all datatypes in torch.cat | ngimel | open | [
"release notes: cuda"
] | 1 | COLLABORATOR | Enable vectorized stores in cat whenever possible.
Unforunately, cat on the last dim still struggles to reach peak bw, when last dim sizes are small, so writes from the different threads are not coalesced. Still, it's about 15% gain for the shapes that are supported and where just vectorized reads weren't enough (wher... | true |
3,009,072,489 | Save/load op profiles | angelayi | closed | [
"Merged",
"ciflow/trunk",
"release notes: composability",
"skip-url-lint"
] | 7 | CONTRIBUTOR | Add ability to save/load op profiles into a yaml file:
```python
op_profile = self.get_sample_op_profile()
# Save
save_op_profiles(op_profile, "op_profile.yaml")
# Load
loaded = load_op_profiles("op_profile.yaml")
assert op_profile == loaded
``` | true |
3,009,069,770 | [easy] Fix test_dynamo_timed | masnesral | 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):
* __->__ #151816
Summary: The structured logging counter is a global that might have been affected by earlier tests. Clear it explicitly.
Fixes #148093
Test Plan: `pytest test/dynamo/test_utils.py`
cc @voznesenskym @penguinwu @EikanWang @jgo... | true |
3,009,045,638 | Ensure runners have the required prefix | ZainRizvi | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Clone changes from https://github.com/pytorch/pytorch/pull/151696/ since that PR wouldn't merge | true |
3,009,042,979 | [MergeBot] Update PullRequestResolved Regex | malfet | closed | [
"Merged",
"topic: not user facing"
] | 3 | CONTRIBUTOR | By copying an updated one from https://github.com/ezyang/ghstack/commit/cff091f3f3a598c36eb4ca99622833e1011d6fbc
| true |
3,009,038,580 | Back out "Do not propagate real tensor in extern kernel" | yushangdi | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Summary:
D73002775 breaks aot_compile for many draft exported models on PT2I dashboard. Revert.
Example error msg:
```
OrderedSet([]) >= OrderedSet([u1185, u1186, u1187]) (inductor >= fx)
fx node is: %embedding_bag_byte_prepack : [num_users=4] = call_function[target=torch.ops.quantized.embedding_bag_byte_prepack.defa... | true |
3,009,031,983 | [CUDA][CPU] Bump system memory requirement for `test_cross_entropy_large_tensor` | eqy | closed | [
"module: cuda",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | COLLABORATOR | `/usr/bin/time` seems to show max resident pages at 119GiB
cc @ptrblck @msaroufim @jerryzh168 | true |
3,008,980,915 | [CUDA][MXFP8] bump tolerances for `test_blockwise_mxfp8_nvfp4_numerics` | eqy | closed | [
"module: cuda",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"matrix multiplication",
"module: float8"
] | 5 | COLLABORATOR | got a slightly lower sqnr on a smaller GPU
cc @ptrblck @msaroufim @jerryzh168 @yanbing-j @vkuzo @albanD @kadeng @penguinwu | true |
3,008,977,635 | StringCordView: make iterator fast when there is only one piece | swolchok | closed | [
"oncall: jit",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: jit"
] | 10 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151850
* #151849
* __->__ #151810
* #151807
* #151806
* #151805
* #151804
* #151803
* #151802
* #151801
This makes the StringCordView iterator a variant holding
either the existing implementation (when there is more than one piece)
or a sim... | true |
3,008,941,848 | [export] deserialization for unbacked ranges is wrong | pianpwk | open | [
"oncall: pt2",
"export-triaged",
"oncall: export"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
ShapeEnv range info is wrong for unbacked symbols after we deserialize, with lower bound of 2:
```
import io
import torch
from torch.export import export, save, load
class Foo(torch.nn.Module):
def forward(self, x):
n = x.item()
return torch.empty(n)
ep = export(Foo(), (to... | true |
3,008,934,242 | [BE] Move aarch64 docker build to larger node | malfet | closed | [
"Merged",
"topic: not user facing"
] | 3 | CONTRIBUTOR | They happen once a week or so, not sure why it needs to be on the slowest machine possible
| true |
3,008,917,707 | Fix missing moves in SchemaTypeParser::parseFakeAndRealType | swolchok | closed | [
"oncall: jit",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: jit"
] | 12 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151850
* #151849
* #151810
* __->__ #151807
* #151806
* #151805
* #151804
* #151803
* #151802
* #151801
Was seeing a small amount of shared_ptr traffic from these.
The std::move(text) at the top is just a piggyback.
Differential Revision:... | true |
3,008,917,624 | Fix a missed c10::TypeFactory::create spot in function_schema_parser | swolchok | closed | [
"oncall: jit",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: jit"
] | 10 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151850
* #151849
* #151810
* #151807
* __->__ #151806
* #151805
* #151804
* #151803
* #151802
* #151801
Looks like we are supposed to be using TypeFactory instead of direct creation everywhere that might run on mobile.
Differential Revisio... | true |
3,008,917,530 | Fix easy missing moves in function_schema_parser | swolchok | closed | [
"oncall: jit",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: jit"
] | 14 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151850
* #151849
* #151810
* #151807
* #151806
* __->__ #151805
* #151804
* #151803
* #151802
* #151801
Just some straightforward not-moving-upon-return.
Differential Revision: [D73376718](https://our.internmc.facebook.com/intern/diff/D733... | true |
3,008,917,434 | Add & use Token::text_view() (which returns a string_view unlike text()) | swolchok | closed | [
"oncall: jit",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: jit"
] | 11 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151850
* #151849
* #151810
* #151807
* #151806
* #151805
* __->__ #151804
* #151803
* #151802
* #151801
Sadly, I can't just fix text() because that might cause lifetime issues in somebody's code.
Differential Revision: [D73376715](https://... | true |
3,008,917,351 | Fix return type of TypeFactoryBase<c10::DynamicType>::get | swolchok | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 11 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151850
* #151849
* #151810
* #151807
* #151806
* #151805
* #151804
* __->__ #151803
* #151802
* #151801
getBaseType() actually returns a reference. This was causing shared_ptr copies.
Differential Revision: [D73376717](https://our.internmc... | true |
3,008,917,238 | Create and use DynamicTypes for check in DispatchKeyExtractor::makeBitsetForDispatchArgs | swolchok | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 11 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151850
* #151849
* #151810
* #151807
* #151806
* #151805
* #151804
* #151803
* __->__ #151802
* #151801
On mobile, many but not all things in the JIT type subsystem start using DynamicType. Not using DynamicType was imposing a startup time... | true |
3,008,917,147 | Don't copy DynamicType argument to DynamicType::create | swolchok | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 11 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151850
* #151849
* #151810
* #151807
* #151806
* #151805
* #151804
* #151803
* #151802
* __->__ #151801
This improves performance of DynamicType::isSubtypeOfExt.
Differential Revision: [D73129449](https://our.internmc.facebook.com/intern/d... | true |
3,008,917,062 | Fix extra heap allocation in Source constructor | swolchok | closed | [
"oncall: jit",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: jit"
] | 11 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151850
* #151849
* #151810
* #151807
* #151806
* #151805
* #151804
* #151803
* #151802
* #151801
* __->__ #151800
* #151682
This was a sneaky one: the StringCordView default constructor allocates.
Differential Revision: [D73129448](https:/... | true |
3,008,913,954 | Expanding subset of tensor reads wrong memory | martenlienen | open | [
"triaged",
"module: correctness (silent)",
"bug",
"oncall: pt2",
"module: dynamic shapes"
] | 7 | NONE | ### 🐛 Describe the bug
I have derived the following minimal failing example:
```python
import torch
def expand(x, n):
return x.expand((n,))
@torch.compile()
def f(n: int, device: str):
numbers = torch.arange(10, device=device)
for i in range(len(numbers)):
expanded = expand(numbers[i], n)
... | true |
3,008,878,859 | [c10d][fr] Fix another bug when we should continue when the op list is empty | fduwjj | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 10 | CONTRIBUTOR | Differential Revision: D73375318
We shouldn't check the op list when it is empty. And later, when it is empty we pops it out from the queue we will check for collective matching. Added a unit test for this case and also covered the case fixed https://github.com/pytorch/pytorch/pull/151683 in the unit test as well.
... | true |
3,008,798,100 | Rename register_fake_profile to unsafe_generate_fake_kernels | angelayi | closed | [
"Merged",
"ciflow/trunk",
"release notes: export"
] | 3 | CONTRIBUTOR | Fixes https://docs.google.com/document/d/1BZsuUR1zJ-52Y7wP4yWX8beB4dwYbgdu5o1qKam_iWg/edit?disco=AAABiJdX1XU | true |
3,008,778,456 | Update docs dependencies for local build | svekars | closed | [
"module: docs",
"Merged",
"ciflow/trunk",
"topic: docs",
"topic: not user facing"
] | 17 | CONTRIBUTOR | Fixes #151786
- Changed requirements.txt to a symlink to .ci/docker/requirements-docs.txt
- Updated README.md with better doc build instructions.
cc @sekyondaMeta @AlannaBurke | true |
3,008,720,382 | Deduplicate library deletion | angelayi | open | [
"ciflow/trunk",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Fixes https://github.com/pytorch/pytorch/pull/151299#issuecomment-2807160080
| true |
3,008,680,172 | [BE]: Better cleanup optimized code from #151474 | Skylion007 | closed | [
"open source",
"better-engineering",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 5 | COLLABORATOR | This change addresses the first/second time/mem "spike" observed Improves on #151474 by removing unnecessary stride calculations and unused arguments to the helper function
https://github.com/pytorch/pytorch/issues/151351
Fixes https://github.com/pytorch/pytorch/issues/151351 | true |
3,008,647,092 | Create decomp for searchsorted | justinchuby | open | [
"module: onnx",
"triaged"
] | 0 | COLLABORATOR | In https://github.com/pytorch/pytorch/issues/151648#issuecomment-2817662679 the model cannot be exported to ONNX because a decomp was missing for searchsorted. Looks like a decomp can be created according to the comments. | true |
3,008,571,754 | Add NCCL trafficClass option for QoS support | x41lakazam | closed | [
"oncall: distributed",
"open source",
"release notes: distributed (c10d)"
] | 2 | CONTRIBUTOR | cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k | true |
3,008,534,436 | [MPS] Enable log1p and sigmoid for int64 | malfet | closed | [
"Merged",
"release notes: mps",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151791
* #151790
It works on MacOS-15, but likely will need a skip for MacOS-13
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @much... | true |
3,008,534,331 | [Testing] Unskip expm1 log1p for MPS | 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):
* #151791
* __->__ #151790
But don't test them for unsupported dtypes (which is float64 for MPS)
- Skip int64 for log1p for now (next PR will fix that)
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe... | true |
3,008,337,473 | [Dynamo] Replace `unimplemented` with `unimplemented_v2` in `torch/_dynamo/variables/iter.py` | shink | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo"
] | 9 | CONTRIBUTOR | Part of #147913
Replace `unimplemented` with`unimplemented_v2` in `torch/_dynamo/variables/iter.py`
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
3,008,309,210 | [standalone_compile] Dynamic shape handling | zou3519 | closed | [
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: AO frontend"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151788
standalone_compile needs to get dynamic shape information from
somewhere. We add a new `dynamic_shapes` argument with three options:
1. from the passed-in graph (dynamic="from_graph"). This is the default.
2. from the example... | true |
3,008,229,330 | Fix doc requirements install error | zeshengzong | closed | [
"open source",
"Merged",
"topic: not user facing"
] | 5 | CONTRIBUTOR | Fixes #151786
Change version in requirements of docs consistent with version in [CI version file](https://github.com/pytorch/pytorch/blob/main/.ci/docker/requirements-docs.txt), which changed in #149331
### Test Result
... | true |
3,008,223,002 | Fail to install document dependency locally | zeshengzong | closed | [
"module: docs",
"module: ci",
"triaged"
] | 4 | CONTRIBUTOR | ### 📚 The doc issue
Install dependency of docs has following errors
```bash
# pytorch/doc
pip install -r requirements.txt
```
### Suggest a potential alternative/fix
_No response_
cc @svekars @sekyondaMeta @AlannaBurke @s... | true |
3,008,202,259 | Optimize register_full_backward_hook description when all input no grad | zeshengzong | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: autograd"
] | 4 | CONTRIBUTOR | Fixes #100528
## Test Result
### Before
### After
| true |
3,008,197,361 | Fix the Inconsistency and Description of `device_type` in `torch.random.fork_rng()` | ILCSFNO | closed | [
"triaged",
"module: backend"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
The doc of [torch.random.fork_rng()](https://pytorch.org/docs/stable/random.html#torch.random.fork_rng) shows its description as below:
https://github.com/pytorch/pytorch/blob/bf28d1cafc6ab3ea94856e5891be1b5e8a37d83c/torch/random.py#L146-L147
There are 2 issues that I wonder:
First, less sit... | true |
3,008,038,581 | We could not debug inside the backward function with pdb | BraveDrXuTF | closed | [
"module: autograd",
"triaged"
] | 2 | NONE | ### 🐛 Describe the bug
Even if we use detect_anomaly,
```
loss = output.mean()
with torch.autograd.detect_anomaly():
loss.backward()
print("Backward pass completed.")
```
we can only get such an abstract error info,
```
with torch.autograd.detect_anomaly():
Traceback (most recent call last):
... | true |
3,007,955,984 | JVP: Option to Disable Gradient Caching for Tangents | qsh-zh | open | [
"triaged",
"module: functorch"
] | 0 | NONE | ### 🚀 The feature, motivation and pitch
I'm requesting a new option for `torch.func.jvp` to disable gradient caching and tracking specifically for the tangent output without affecting the primal output.
Currently, when using `torch.func.jvp(fn, primals, tangents)`, the JVP output requires gradients by default, which... | true |
3,007,912,715 | [MPS] Move ops modifiers to testing utils so other tests can reuse | qqaatw | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/mps"
] | 6 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151177
* __->__ #151781
Test collection check on macOS 13.7.1:
```
python -m pytest test/test_mps.py --collect-only
python -m pytest -v test/test_mps.py::TestConsistencyCPU
```
Before:
```
6390 tests collected in 8.34s
3936 pass... | true |
3,007,897,385 | Horizontal | sunjiweiswift | open | [
"triaged",
"open source",
"module: inductor"
] | 2 | NONE | Fixes #ISSUE_NUMBER
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
3,007,833,710 | [Indcutor Remote Cache] Raise an exception if redis module is required but not available | ChuanqiXu9 | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 13 | CONTRIBUTOR | If we need redis but redis is not available, it is better to tell the user to install redis instead of continue silently.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
3,007,766,608 | Normalize dynamic size symbols in template codegen cache key. | laithsakka | open | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150869
* __->__ #151778
* #151773
* #151764
if we have the following tensors (s0, 1)*( 1, s0) and (s1, 1)*( 1, s1), then currently we generate the same code
for during mm auto-tuning when expanding the mm_template. Eventhough the... | true |
3,007,713,127 | enable windows inductor UT in CI | yuchengliu1 | open | [
"open source",
"ciflow/trunk",
"release notes: releng",
"module: dynamo",
"ciflow/inductor",
"ciflow/xpu"
] | 4 | NONE | cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
3,007,694,022 | [dynamo] Some inefficiencies around handling __torch_function__ | anijain2305 | open | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
I was looking at reducing compile time for a GGUF SD model (https://github.com/pytorch/pytorch/issues/150706), and I found some inefficiencies around `__torch_function__`. The model heavily relies on torch function.
Testing on a single transformer layer, I was able to reduce Dynamo time from... | true |
3,007,684,847 | [Inductor] Modify TritonTemplate store_output function to support TMA stores | NikhilAPatel | open | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151775
* #151774
Summary:
The `store_output` macro -- used in Triton templates to generate triton kernel code for storing output using `tl.store` -- has been modified to support TMA based stores.
This now allows functions using TMA ... | true |
3,007,684,760 | [Inductor] Modify persistent+TMA template for Triton mm and admm to use new TMA API | NikhilAPatel | open | [
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151775
* __->__ #151774
Summary:
This PR modifies the Triton template for persisten+TMA mm and admm to use the new functional API for TMA introduced here: https://github.com/triton-lang/triton/pull/6248/
This also involves setting a global... | true |
3,007,662,223 | Cache code generation during triton template expansion and enable it for mm_template. | laithsakka | open | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151773
In a model, we see ~~ 40% of the time in mm/addmm tuning. The model have 2000 mm,
many of which receives the same input shapes.
with autotune enabled, this become expensive, while we already cache auto tuning results, we
... | true |
3,007,660,921 | [Inductor] Modify persistent+TMA template for Triton mm and admm to use new TMA API | NikhilAPatel | closed | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151772
Summary:
This PR modifies the Triton template for persisten+TMA mm and admm to use the new functional API for TMA introduced here: https://github.com/triton-lang/triton/pull/6248/
This also involves setting a global Triton al... | true |
3,007,629,416 | Graph break on .t() when Tensor._make_subclass | KareemMusleh | open | [
"triaged",
"oncall: pt2",
"dynamo-triage-jan2025"
] | 2 | NONE | ### 🐛 Describe the bug
this is similar to #150265
```python
from torch import nn
import torch
torch_compile_options = {
"epilogue_fusion" : True,
"max_autotune" : True,
"shape_padding" : True,
"trace.enabled" : True,
"triton.cudagraphs" : False,
}
class a(nn.Linear):
def __init_... | true |
3,007,575,819 | [2/n][Optimus][Auto-AC] Support activation quantization with scaling | mengluy0125 | open | [
"fb-exported",
"ciflow/trunk",
"release notes: fx",
"fx",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 13 | CONTRIBUTOR | Summary:
Previously, we only support non-scaling quantization, which may lead to overflow, here we support scaling quantization, and set it as the default version.
Here, we quantize activation nodes based on the size_in_mb, the default value is 100, i.e., as long as the node has at least 100MB size, we will quantiz... | true |
3,007,487,842 | Add adaptive_avg_pool2d input and output_size check | zeshengzong | open | [
"triaged",
"open source",
"topic: not user facing"
] | 4 | CONTRIBUTOR | Fixes #126673
## Test Result
```python
import torch
import torch.nn as nn
batch_size = 10
channels = 3
length = 32
input_tensor = torch.randn([batch_size, channels, length])
adaptive_avg_pool = nn.AdaptiveAvgPool2d(output_size=16)
output_tensor = adaptive_avg_pool(input_tensor)
print(output_tensor.shap... | true |
3,007,410,439 | Run standalone compile tests on cpu/gpu | oulgen | closed | [
"Merged",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151603
* #151609
* __->__ #151768
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
3,007,362,018 | [Don't merge] Upgrade oneDNN to v3.8 for XPU build | mengfei25 | open | [
"module: mkldnn",
"open source",
"ciflow/binaries_wheel",
"ciflow/xpu"
] | 7 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
cc @gujinghui @PenghuiCheng @XiaobingSuper @jianyuh @jgong5 @mingfeima @sanchitintel @ashokei @jingxu10 @min-jean-cho @yanbing-j @Guobing-Chen @Xia-Weiwen @snadampal | true |
3,007,360,311 | Support regexes in dynamic sources allowlist | bobrenjc93 | 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):
* #151885
* __->__ #151766
As requested by Shuai. I also included an additional refactor to capture
changes in the whitelist over time since previously the first time it
was set, it was impossible override when a new config was set.
cc @vozne... | true |
3,007,358,085 | Upgrade oneDNN to v3.8 for XPU build | mengfei25 | closed | [
"module: mkldnn",
"open source"
] | 2 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
cc @gujinghui @PenghuiCheng @XiaobingSuper @jianyuh @jgong5 @mingfeima @sanchitintel @ashokei @jingxu10 @min-jean-cho @yanbing-j @Guobing-Chen @Xia-Weiwen @snadampal | true |
3,007,348,292 | Refactor TritonTemplate.generate and move codgen part to generate_and_load | laithsakka | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 9 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151764
Splitting https://github.com/pytorch/pytorch/pull/149267/ .
This first PR just refactor the code without adding any caching functionality.
The logic of generating the code and loading it is moved to generate_and_load() + so... | true |
3,007,338,023 | Eagerly guard when dealing with float32 scalar tensor item calls | bobrenjc93 | closed | [
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151763
* #151766
Fixes #151470
SymFloats implicitly only supports float64 as we can see in code like
this:
https://github.com/pytorch/pytorch/blob/main/torch/_subclasses/fake_tensor.py#L479.
This PR fixes the above issue by ea... | true |
3,007,173,059 | Support for grouped query attention in ONNX export | cyanic-selkie | open | [
"module: onnx",
"triaged"
] | 3 | NONE | ### 🚀 The feature, motivation and pitch
Hi, when using `enabled_gqa` with `scaled_dot_product_attention`, the ONNX export fails - this is documented.
However, since QGA is very popular currently, and the Attention ONNX op already supports it, I was wondering if there is any plan to add support for it in the exporter... | true |
3,007,117,746 | Inconsistent `sum`/`dot`/`norm` behavior | melnikovsky | open | [
"triaged",
"module: linear algebra"
] | 10 | CONTRIBUTOR | ### 🐛 Describe the bug
Summation of huge `float32` arrays is admittedly a sensitive subject, but different routines use inconsistent (and seemingly undocumented?) approaches. Particularly, `torch.sum` is the most precise, while `linalg.norm` on 10 CPU cores is as slow but has inferior accuracy. Would it be possible t... | true |
3,007,061,212 | [MPS] Implement upsample_nearest3d_vec operator | donghao1393 | open | [
"triaged",
"open source",
"release notes: mps"
] | 3 | NONE | # MPS Implementation of upsample_nearest3d_vec
This PR adds a Metal Performance Shaders (MPS) implementation of the `upsample_nearest3d_vec` operator for PyTorch on macOS. This implementation enables 3D nearest neighbor upsampling to run natively on Apple Silicon GPUs.
## Changes
- Added MPS implementation of ... | true |
3,007,034,055 | "_get_pg_default_device" deprecated warning in "Getting Started with Distributed Checkpoint (DCP)" | michael080808 | open | [
"oncall: distributed",
"triaged"
] | 0 | NONE | ### 📚 The doc issue
I tried both the "[Saving](https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html#saving)" and "[Loading](https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html#loading)" code from "[Getting Started with Distributed Checkpoint (DCP)](https://pytorch.org/tutorial... | true |
3,006,946,347 | torch.testing._internal.optests - MPS Support | goldfishsound | open | [
"open source",
"topic: not user facing"
] | 3 | NONE | # autograd_registration_check
## Adding support for MPS device
1. Why this PR
The generated test by optests.generate_opcheck_tests() for the "test_autograd_registration " test case will fail for tensors on the mps device.
2. Reason for failure
The current implementation of the function autograd_registration_c... | true |
3,006,793,070 | [logging] Put "everything" WaitCounters in dynamo_timed | masnesral | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151757
* #151749
Summary: The main motivation is to capture the cudagraphs overhead in a WaitCounter. We'll combine that with Triton autotuning, and therefore rename to "compile_runtime_overheads". Since we have a couple WaitCoun... | true |
3,006,756,813 | [dynamo] Call __torch_function__ on only overridable tensor methods or attrs | anijain2305 | open | [
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151620
* #150704
* #151410
* #151409
* __->__ #151756
* #151633
* #151477
* #151357
* #151256
* #151330
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @ka... | true |
3,006,747,720 | [ez] fix typo in comment | bobrenjc93 | 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):
* __->__ #151755
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
3,006,660,272 | [MPS] Add support for hermite_polynomial_he (inductor/eager). | dcci | closed | [
"Merged",
"topic: improvements",
"module: mps",
"release notes: mps",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 4 | MEMBER | cc @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
3,006,638,675 | reroute index to fast implementation for indexing on 0th dimension | ngimel | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: cuda",
"ci-no-td"
] | 6 | COLLABORATOR | Per title, improve x[index] cuda perf for the common case of indexing along the first dim, using vectorized gather kernel
| true |
3,006,629,397 | Refactor duplicate code into a utility function in pytorch/torch/nn/functional.py | aaiss0927 | open | [
"open source",
"ciflow/trunk",
"topic: not user facing"
] | 6 | NONE | Description:
This PR refactors duplicate code for validating dropout probability values into a utility function `probability_checking()` in pytorch/torch/nn/functional.py.
Changes:
- Created a new utility function `probability_checking(p)` that validates if the dropout probability parameter is within valid range (... | true |
3,006,592,786 | Update __init__.py | Mazgagzam | open | [
"triaged",
"open source",
"topic: not user facing"
] | 4 | NONE | Refactor `factory_kwargs` to simplify key validation and merging
- Replaced the manual key checks and dictionary updates with a more efficient and readable approach.
- Simplified the handling of unexpected kwargs using set operations.
- Ensured no conflicts between `kwargs` and `factory_kwargs` using intersection ... | true |
3,006,588,469 | add min/max_seqlen to non_differentiable | sumantro93 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"release notes: nested tensor"
] | 9 | CONTRIBUTOR | Fixes #148988
| true |
3,006,536,462 | [logging] Fix duration logging for dynamo_compile | masnesral | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151757
* __->__ #151749
Summary: There are a few issues I'm solving:.
1. It's too hard to measure total pt2 overhead using the dynamo_compile table because users need to know the columns representing all the top-level events (dynamo_cumul... | true |
3,006,462,091 | [Benchmarking] Add sam and stable_diffusion to MPS benchmarked models | malfet | closed | [
"Merged",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #151748
| true |
3,006,461,263 | [Benchmarking] Run MPS benchmarks for [b]float16 | malfet | closed | [
"Merged",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151748
* __->__ #151747
And implicitly pass `--float32` when collecting results for "notset" option. Speedups for some models are much higher for float16 dtype, but it's important to track accuracy | true |
3,006,435,215 | [AotInductor][Export][Triton] how to export custom triton kernels when use torch.export.export | zzq96 | open | [
"oncall: pt2",
"export-triaged",
"oncall: export",
"module: aotinductor",
"module: user triton"
] | 2 | NONE | ### 🐛 Describe the bug
our framework is based on torch, and includes some custom triton kernels.
in inference phase, we try use different gpu type(such as training on H100, inference on L40). so we should load exported model and call aoti_compile_and_package to generate aot model based on inference gpu, but error w... | true |
3,006,395,711 | [inductor] [cuda] [silent incorrectness] `F.softmax-torch.argsort` output silent incorrectness when tensor input is very large | shaoyuyoung | open | [
"triaged",
"oncall: pt2",
"module: inductor",
"topic: fuzzer"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
**symptom**: `F.softmax-torch.argsort` output silent incorrectness when tensor input is very large
**device backend**: only triton
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch._inductor import config
config.fallback_random = True
torch.set_grad_enable... | true |
3,006,385,994 | [inductor] [silent incorrectness] [dtype processing] `torch.clamp` can't implicitly covert `int64` | shaoyuyoung | open | [
"high priority",
"triaged",
"oncall: pt2",
"module: aotdispatch",
"module: inductor",
"module: pt2-dispatcher"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
**symptom**: It's a very interesting edge case. When the range of `torch.clamp` is set to **(-0.5, 0.5)**, given an initial `int64` input, it can be **implicitly converted** into `f32` in eager, but inductor loses this mechanism and still outputs `int64`, subsequently resulting silent incorrect... | true |
3,006,331,537 | [Inductor] Dynamo hangs when processing an operator, seemingly depending on a logical argument value | alexsamardzic | closed | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 2 | COLLABORATOR | ### 🐛 Describe the bug
Here is a reproducer:
```Python
import torch
device = "cuda"
group_size = 4
M, N, K = 16, 32, 64
dtype_AB = torch.float8_e4m3fn
dtype_scale = torch.float32
dtype_offset = torch.int32
dtype_C = torch.bfloat16
A = torch.ones(M, K * group_size, device=device).to(dtype_AB)
B = torch.ones(N, K * ... | true |
3,006,319,425 | Implement avg_pool3d for MPS backend | donghao1393 | open | [
"triaged",
"open source",
"release notes: mps"
] | 7 | NONE | This PR implements the avg_pool3d operation for the MPS backend using a custom Metal shader. This will allow users with Apple Silicon GPUs to use 3D average pooling operations without falling back to CPU.
## Implementation Details
The implementation includes:
1. A custom Metal shader for 3D average pooling
2. C++ in... | true |
3,006,318,804 | Implement avg_pool3d for MPS backend | donghao1393 | closed | [] | 1 | NONE | This PR implements the avg_pool3d operation for the MPS backend using a custom Metal shader. This will allow users with Apple Silicon GPUs to use 3D average pooling operations without falling back to CPU.
The implementation includes:
1. A custom Metal shader for 3D average pooling
2. C++ interface to integrate with Py... | true |
3,006,271,921 | mps and cpu backends produce different training results with FFT and Adam | ChenkaiMao97 | open | [
"needs reproduction",
"triaged",
"module: correctness (silent)",
"module: fft",
"module: mps"
] | 1 | NONE | ### 🐛 Describe the bug
Hi, I have a model that uses 2d FFT operations, and I'm seeing convergent training results on Cuda and cpu, while getting divergent results on mps (loss drops for the first few steps and then explodes).
I'm not sure where the error is coming from, but I've created this minimal example below wi... | true |
3,006,251,332 | [Dynamo][Easy] Remove unreachable code | shink | closed | [
"open source",
"Merged",
"topic: not user facing",
"module: dynamo"
] | 18 | CONTRIBUTOR | This line is unreachable:
https://github.com/pytorch/pytorch/blob/f6c1cf04b5158bac7263e4708f22dab63e7456ad/torch/_dynamo/output_graph.py#L275
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
3,006,192,906 | [inductor] [triton] the generated triton code throws `NameError('rindex is not defined')` when using `torch.cummin` | shaoyuyoung | closed | [
"high priority",
"triaged",
"oncall: pt2",
"module: inductor"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
**symptom**: The triton kernel code generated by inductor throws **variable name undefined error**. I am not sure whether this is the inductor bug or triton bug?
**device backend**: only triton has this issue.
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from tor... | true |
3,006,123,877 | [inductor] [cuda] [fake tensor] `torch.triu_indices` throws `pointer argument` error when using `[0, 0]` | shaoyuyoung | open | [
"triaged",
"actionable",
"oncall: pt2",
"module: fakeTensor",
"module: dynamo",
"dynamo-triage-jan2025"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
**symptom**: if not using `[0, 0]` silice, eager will throw `Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!`. However, if we use `[0, 0]` to get the first element, eager can pass the check, but inductor throws the error.
**device backend**: triton... | true |
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