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 |
|---|---|---|---|---|---|---|---|---|
2,888,319,388 | Add Structured Knowledge Accumulation (SKA) Layer to PyTorch | BouarfaMahi | open | [
"module: nn",
"triaged"
] | 0 | NONE | 🚀 The feature, motivation and pitch
### Description
We propose adding **Structured Knowledge Accumulation (SKA)** layers as native subclasses to PyTorch, introducing **forward-only, entropy-driven learning** without backpropagation. SKA enables self-organizing neural networks that learn **without a loss function**... | true |
2,888,313,637 | [export] Fix logging so that it doesn't result in max recursion error | angelayi | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"fx",
"ciflow/inductor",
"release notes: export"
] | 7 | CONTRIBUTOR | Test Plan:
buck2 run mode/dev-nosan sigmoid/inference/ts_migration:pt2i_readiness_main -- --model_id=487493491 --test_suite ads_all --mode test_full_model
Produces https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/.tmp2wsjQH/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutM... | true |
2,888,293,720 | [triton 3.3] Fix inductor/test_profiler.py test | davidberard98 | 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):
* __->__ #148230
test_inductor_profiling_kernel_names_pointwise is checking that the profiler correctly records the input shapes to the kernel. After triton 3.3, we get a different number of args (because the constexpr args are passed in, from... | true |
2,888,291,030 | [cutlass backend] Add main tests for mm, addmm and bmm - step 1 | henrylhtsang | 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):
* #148236
* #148234
* #148233
* __->__ #148229
This adds very good coverage for normal mm tests {aoti x torch.compile} x {default, dynamic}.
There are some parts that are less tested. For example:
* different layout combo
* shapes t... | true |
2,888,259,491 | ROCm: Disable torch check for Multiplication of two Float8_e5m2 matrices | jagadish-amd | closed | [
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"rocm",
"ciflow/rocm"
] | 5 | CONTRIBUTOR | ROCm supports Multiplication of two Float8_e5m2 matrices.
Hence disabling the torch check for ROCm.
Test command (on ROCm h/w supporting fp8)
python test/test_matmul_cuda.py TestFP8MatmulCudaCUDA.test_float8_basics_cuda -v
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo ... | true |
2,888,240,713 | Significantly speed up save_cache_artifacts | oulgen | 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):
* __->__ #148227
* #148226
While using save_cache_artifacts on internal workloads, we have noticed that repeatedly calling this function after every batch is incredibly expensive. This PR significantly speeds up this function call by opting o... | true |
2,888,240,628 | Add AppendingByteSerializer class | oulgen | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148227
* __->__ #148226
This PR adds a new util class that enables efficient appending of sequential byte data with custom serialization and deserialization. | true |
2,888,218,329 | [Inductor-CPU] ATen SDPA kernel runtime is not captured in profiling results | sanchitintel | closed | [
"oncall: cpu inductor"
] | 3 | COLLABORATOR | ### 🐛 Describe the bug
With `torch.compile`, SDPA op runtime is not being captured in PyTorch profiling results.
The profiling results may have an item such as `graph_0_cpp_fused__scaled_dot_product_flash_attentio.....`, but it doesn't correspond to SDPA, and instead corresponds to a kernel whose output may be input... | true |
2,888,209,250 | [EZ][BE] Increase tolerances for interpolate op | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148224
* #148211
* #148187
* #148154
Not sure why tolerances were set like that, this logic was added in https://github.com/pytorch/pytorch/pull/104181 without much explanation
But if I'm to make a guess, it's likely due to th... | true |
2,888,190,711 | [inductor][ck] add kBatch_sweep to config.rocm | coconutruben | closed | [
"module: rocm",
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ciflow/rocm"
] | 6 | CONTRIBUTOR | Summary:
# Why
enable testing and users to specify a set of kBatches to try rather than relying on our hand written heuristic
# What
add rocm.kBatch_sweep as a list of kBatches to try out. These will generate a product of CK instances, one per kBatch for each existing op, though they are often filtered out if they a... | true |
2,888,181,103 | make saved_tensor_hooks work better in compile for doing activation compression | bdhirsh | open | [
"module: activation checkpointing",
"module: autograd",
"triaged",
"oncall: pt2",
"module: pt2-dispatcher"
] | 4 | CONTRIBUTOR | One potential use case of quantized dtype (e.g. float8) is in compressing activations: you run the model forward, autograd saves some activations for backward. Rather than saving these activations in higher precision, you may want to compress them (say to float8), and decompress them when they are later needed in the ... | true |
2,888,144,994 | [ci] disable cudagraph for tts_angular on dashboard | BoyuanFeng | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | tts_angular with cudagraph is flaky. Its speedup varies from .05 to 1.01. This PR disables cudagraph for tts_angular to avoid the noise. Since tts_angular shows ~1x speedup while other torchbench models show ~2x speedup, skipping tts_angular would wrongly bump the cudagraph speedup. So this PR only disables cudagraph f... | true |
2,888,140,251 | [dynamo] rename test_graph_break_messages -> test_error_messages | williamwen42 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 3 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148401
* __->__ #148220
* #148205
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,888,128,252 | MPS vs Metal vs CPU performance comparison | manuelcandales | open | [
"module: performance",
"triaged",
"module: mps"
] | 0 | CONTRIBUTOR | The following numbers are averages over 1000 runs, produced on an M1 Pro (16GB RAM), using the script at the bottom of this issue.
exp, tanh and erfinv are operations currently implemented as Metal shaders
Things to notice:
- On the MPS backend, multiplying a 1-element tensor by 1 is 3 to 5 times more expensive than ... | true |
2,888,106,454 | DISABLED test_dynamic_sources_dynamic_override (__main__.MiscTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamo"
] | 15 | NONE | Platforms: asan, linux, mac, macos, rocm, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_dynamic_sources_dynamic_override&suite=MiscTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/37999979503)... | true |
2,888,106,281 | DISABLED test_guard_failure_fn2 (__main__.MiscTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamo"
] | 2 | NONE | Platforms: asan, linux, mac, macos, rocm, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_guard_failure_fn2&suite=MiscTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/37999438720).
Over the pas... | true |
2,888,106,195 | DISABLED test_guard_failure_fn_shape_control_dynamic_shapes (__main__.DynamicShapesMiscTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamo"
] | 4 | NONE | Platforms: mac, macos, linux, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_guard_failure_fn_shape_control_dynamic_shapes&suite=DynamicShapesMiscTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/r... | true |
2,888,106,144 | DISABLED test_mark_unbacked_strict_dynamic_shapes (__main__.DynamicShapesMiscTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamo"
] | 3 | NONE | Platforms: asan, linux, rocm, slow, mac, macos
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_mark_unbacked_strict_dynamic_shapes&suite=DynamicShapesMiscTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch... | true |
2,888,106,073 | DISABLED test_dynamic_sources_dynamic_override_dynamic_shapes (__main__.DynamicShapesMiscTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamo"
] | 13 | NONE | Platforms: asan, linux, mac, macos, rocm, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_dynamic_sources_dynamic_override_dynamic_shapes&suite=DynamicShapesMiscTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytor... | true |
2,888,086,555 | Expose functions used in custom backend in torch_python dll | wschin | closed | [
"triaged",
"open source",
"Merged",
"topic: bug fixes",
"topic: not user facing"
] | 4 | COLLABORATOR | Fixes #148208. There are solutions for exposing symbols implicitly from inline functions (i.e., inline function A calls non-inline function B in foo.h. Code includes foo.h has to see the symbol B in DLL).
Solution 1: tag the entire struct where the inline functions are defined as member functions with TORCH_PYTHON_A... | true |
2,888,043,173 | [not for merge] [AOTI] selectively build code at O1 | benjaminglass1 | closed | [
"open source",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148212
* #144349
* #144293
* #146928
Still TODO:
1. Port these improvements to `cpp_wrapper` mode, if the speedup is worth it.
2. Remove the now-unneeded `cpp_prefix` include from the shared `cpu.h` AOTI header.
3. Fix CMake pa... | true |
2,888,014,821 | [MPS][BE] Combine two `upsample_kernel_out_template` into one | malfet | closed | [
"Merged",
"release notes: mps",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148224
* __->__ #148211
* #148187
* #148154
- First, by stopp inverting sizes and strides, i.e. passing them as is, but reading them in inverse order in the shader as 1st stride of 4D tensor is one used for batches, 2nd for channels a... | true |
2,887,991,646 | [inductor] Lowerings for max_pool3d | isuruf | closed | [
"open source",
"Merged",
"ciflow/trunk",
"ciflow/mps",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 9 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148210
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,887,991,512 | [inductor] support dilation in max_pool2d lowering | isuruf | closed | [
"open source",
"Merged",
"ciflow/trunk",
"ciflow/mps",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 9 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148209
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,887,981,853 | Regression: Missing Symbols in PyTorch DLL (torch_python) | wschin | closed | [
"module: cpp-extensions",
"module: cpp",
"triaged",
"actionable"
] | 2 | COLLABORATOR | ### 🐛 Describe the bug
We use some functions in python_arg_parser.h for our backend and those symbols are gone after #136743. In python_arg_parsers.h, you will see inline implementation such as
```cpp
inline at::Tensor PythonArgs::tensor(int i) {
if (args[i] && THPVariable_CheckExact(args[i])) {
return THPVari... | true |
2,887,958,210 | Add option to shut down idle async_compile workers after timeout | jamesjwu | open | [
"triaged",
"actionable",
"oncall: pt2",
"module: inductor"
] | 0 | CONTRIBUTOR | We've seen internally that running 32 compile threads across large jobs can lead to significant memory pressure over time, as the workers last for the entire training session. PT2 does not know when we may need to compile again, so we do need the workers at any point, but it should be possible to add a config option th... | true |
2,887,954,654 | [cond] support output the same unbacked symbol from two branches | ydwu4 | open | [
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148206
Previously, we didn't track the unbacked symbols leaked out of true_branch and false_branch if they have the same shape expr. This cause the the fake output of cond operator itself doesn't set up its unbacked_bindings meta ... | true |
2,887,936,065 | [dynamo] remove internal stack trace for fullgraph=True graph breaks | williamwen42 | closed | [
"Merged",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"module: compile ux"
] | 4 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148401
* #148220
* __->__ #148205
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,887,921,732 | [debug] 'No available kernel' error for cudnn on A100 | XilunWu | closed | [
"oncall: distributed",
"topic: not user facing"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148357
* __->__ #148204
* #148125
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,887,906,031 | [fr] Added protection against missing stack frames in fr | VieEeEw | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 9 | CONTRIBUTOR | Summary: We have quite a while failures due to this unprotected access. https://fburl.com/scuba/ai_rca_debug_tracing/qtnb63qf
Test Plan:
Reviewed By: fduwjj
Differential Revision: D70358287
| true |
2,887,879,842 | Move estimate runtime and pick loop order heuristics into choices.py | exclamaforte | open | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Just a warmup; there are several more of these in the scheduler that I'll move to choices in a follow-up PR.
Test Plan:
Existing tests should cover refactor.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng... | true |
2,887,760,901 | Compile breaks flex-attention with jagged tensors | lgienapp | open | [
"triaged",
"oncall: pt2",
"module: dynamo",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention"
] | 1 | NONE | ### 🐛 Describe the bug
I was playing around with the code from this https://pytorch.org/tutorials/intermediate/transformer_building_blocks.html but ran into the error below when combining jagged tensors, flex-attention, and torch.compile.
Error: `torch._dynamo.exc.InternalTorchDynamoError: AttributeError: 'NestedInt... | true |
2,887,741,487 | Fix recompile reason logging | bobrenjc93 | 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):
* __->__ #148200
for the following test case
```
@torch.compile(dynamic=False, backend=cnts)
def fn(x, y, z):
return x * y * z[0]
fn(1, torch.randn(1), {0: torch.randn(1)})
fn(2, tor... | true |
2,887,715,749 | [Inductor-CPU] qlinear_binary output may have undefined strides with dynamic shape support | sanchitintel | open | [
"triaged",
"oncall: pt2",
"module: dynamic shapes",
"module: dynamo",
"oncall: cpu inductor"
] | 1 | COLLABORATOR | ### 🐛 Describe the bug
When `M` dimension of the `qlinear_binary` activation is 1 and dynamic shape support is enabled with `torch.compile`, `qlinear_binary` op's output's outermost dim's stride may be a symbolic value that's undefined. The issue may be related to Dynamo.
### Code to reproduce the issue
https://g... | true |
2,887,706,842 | [XPU] Fix graph partition tests | benjaminglass1 | closed | [
"open source",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ciflow/xpu"
] | 7 | COLLABORATOR | These tests are currently broken in ciflow/xpu due to explicitly calling CUDA tensors.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,887,700,180 | Enable oneDNN dispatch for gemm bf16bf16->bf16 | aditew01 | closed | [
"triaged",
"open source",
"module: arm",
"Merged",
"topic: not user facing",
"ciflow/binaries_wheel"
] | 11 | COLLABORATOR | Currently, `linear` layers using BF16 are dispatched to OpenBLAS, provided that sbgemm_ is available.
However, profiling on AArch64 shows that dispatching to oneDNN results in a significant speedup. This PR updates the dispatch logic to leverage oneDNN for improved performance.
Attaching some benchmark results. In... | true |
2,887,691,103 | [inductor][triton] Decide how to deprecate "old triton versions" | davidberard98 | open | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 0 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
Right now we have a mess of at least 3 "versions" of Triton - i.e. commit ranges that we are compatible with.
This is beneficial for a few reasons:
* Ability to bisect old versions of Triton
* Compatibility with users who have different (i.e. old) versions of Triton installed ... | true |
2,887,674,334 | ci: move xpu triton build to manylinux 2.28 | chuanqi129 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 9 | COLLABORATOR | Follow PR #148129 to remove manylinux builds for triton xpu | true |
2,887,655,430 | PyTorch nightly MPS SDPA op is unusable | malfet | closed | [
"high priority",
"triage review",
"module: regression",
"module: mps"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
Attempt to run https://github.com/malfet/llm_experiments/blob/main/run_llama.py resulted in crash
```
% python run_llama.py --device mps
Loaded stories15M.pt in 0.20 seconds
Once upon a time/AppleInternal/Library/BuildRoots/d187755d-b9a3-11ef-83e5-aabfac210453/Library/Caches/com.apple.xbs/Sourc... | true |
2,887,558,434 | [ONNX] torch.matmul() breaks dynamic shapes during export | morozovve | closed | [
"module: onnx",
"triaged",
"oncall: pt2",
"module: dynamic shapes"
] | 2 | NONE | ### 🐛 Describe the bug
When exporting model, that
1) contains torch.matmul() operation on Nd matrices
2) has dynamically-shaped input
3) shapes need to be broadcasted during matmul()
exporter decides, that possible range of dimension sizes is just one number -- the one, that input value has.
When broadcasting (unsqu... | true |
2,887,549,991 | Add cache size to error message | kevmo314 | closed | [
"triaged",
"open source",
"module: dynamo",
"release notes: dynamo"
] | 5 | CONTRIBUTOR | Adds the configured limit size to the cache size limit error message.
When the limit is hit right now, it only tells you that the cache size limit has been reached, not what the limit is. That's not too helpful if you want to bump the limit.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSupe... | true |
2,887,545,435 | [BE][Ez]: Use itertools.chain.from_iterable when possible | Skylion007 | closed | [
"oncall: distributed",
"triaged",
"open source",
"better-engineering",
"Merged",
"ciflow/trunk",
"release notes: quantization",
"topic: not user facing",
"fx",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"release notes: AO frontend"
] | 3 | COLLABORATOR | Often makes the code more readable, more efficient, and adds support for infinite iterables.
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv @voznesenskym @penguinwu @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @jiayisu... | true |
2,887,492,014 | Support huggingface reading and writing for multi rank case | ankitageorge | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"release notes: distributed (checkpoint)",
"oncall: distributed checkpointing"
] | 12 | CONTRIBUTOR | Summary: This diff adds the ability for HF reader/writer to read/write in a distributed way. We do this by sending all the tensors meant for the same file to the same rank.
Test Plan:
ensure existing tests pass
I also ran a full end to end test on my devserver to read/write from my HF repo
Differential Revision: D700... | true |
2,887,474,790 | [inductor][cpu] Fix error with FlexibleLayout weights in BMM | frost-intel | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 21 | COLLABORATOR | Fixes #148074
When node A is reshaped (is a `ReinterpretView`) and node B has a `FlexibleLayout`, then the layout of node B *may* be changed during the `kernel.select(options["W"], 0, self.b_index)` call, which could cause the assertion in `kernel.select` to fail.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @... | true |
2,887,470,990 | [MPS][BE][EZ] Aggregate macros | malfet | closed | [
"Merged",
"topic: not user facing",
"release notes: mps",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148211
* __->__ #148187
* #148154
Refactor `INSTANTIATE_UPSAMPLE_BILINEAR2D(DTYPE)`, `INSTANTIATE_UPSAMPLE_BICUBIC2D(DTYPE)` and `INSTANTIATE_UPSAMPLE_BILINEAR2DAA(DTYPE)` use common `INSTANTIATE_UPSAMPLE2D`
Then combine multiple invocation... | true |
2,887,464,637 | [BE][PYFMT] migrate PYFMT for `test/inductor/` to `ruff format` | XuehaiPan | open | [
"open source",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144556
* __->__ #148186
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,887,463,917 | [BE][PYFMT] migrate PYFMT for `torch/ao/` to `ruff format` | XuehaiPan | open | [
"open source",
"Stale",
"release notes: quantization",
"topic: not user facing",
"fx",
"release notes: AO frontend",
"no-stale"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148185
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,887,448,318 | Add cuda 11.8 guard for cufile preload | atalman | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Follow up after https://github.com/pytorch/pytorch/pull/148137
Make sure we don't try to load cufile on CUDA 11.8
Test:
```
>>> import torch
/usr/local/lib64/python3.9/site-packages/torch/_subclasses/functional_tensor.py:276: UserWarning: Failed to initialize NumPy: No module named 'numpy' (Triggered internally... | true |
2,887,030,399 | Implement batching rule for masked_fill_ | LeanderK | open | [
"triaged",
"module: functorch"
] | 0 | NONE | ### 🚀 The feature, motivation and pitch
Very simple, i need a batching-rule for masked_fill_ and the warning encourages me to write an issue
UserWarning: There is a performance drop because we have not yet implemented the batching rule for aten::masked_fill_.Tensor. Please file us an issue on GitHub so that we can p... | true |
2,886,998,885 | [Profiler] Add profiler activity for HPU devices | wdziurdz | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 5 | CONTRIBUTOR | Fixes #148181
| true |
2,886,998,175 | [Profiler] Add profiler activity for HPU devices | wdziurdz | closed | [
"feature",
"oncall: profiler"
] | 1 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
It is necessary to separate HPU devices from other devices to correctly profile HPU devices. Sometimes, it is necessary to collect only traces from HPU devices. Without this capability, profiling becomes very difficult.
### Alternatives
_No response_
### Additional context
... | true |
2,886,832,964 | [pytree] add another simplified pytree module `torch.pytree` | XuehaiPan | open | [
"open source",
"ciflow/trunk",
"topic: not user facing",
"module: pytree",
"ci-test-showlocals"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148328
* __->__ #148180
* #137400
* #152624
Differences between `torch.pytree` and `torch.utils.pytree`:
1. APIs in `torch.utils.pytree` have a `tree_` prefix:
```python
leaves, treespec = torch.utils.pytree.tree_flatten(tre... | true |
2,886,599,403 | Dynamo failure on handling list comparisons | CaoE | closed | [
"high priority",
"triaged",
"oncall: pt2",
"module: dynamo",
"dynamo-polyfill"
] | 3 | COLLABORATOR | ### 🐛 Describe the bug
This should be caused by https://github.com/pytorch/pytorch/pull/144485.
When Dynamo processes list comparison via `torch.export.export_for_training`:
`if m.f != -1: ` https://github.com/WongKinYiu/yolov7/blob/main/models/yolo.py#L604
Possible values of `m.f`: [-1, -2, -3, -4, -5, -6] or -1 ... | true |
2,886,596,580 | [Break XPU][Inductor] Generalize device-bias code and fix test_graph_partition for XPU | etaf | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"keep-going",
"ciflow/xpu"
] | 6 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147727
* __->__ #148178
* #148155
This PR generalized the device-bias code introduced by #147038 . And align the behavior between XPU and CUDA on add + mm + pointwise pattern (for XPU, from addmm + pointwise to mm + fused_add_pointwise) , ... | true |
2,886,578,173 | [Dynamo] Replace `unimplemented` with`unimplemented_v2` in `torch/_dynamo/variables/base.py` | shink | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo"
] | 11 | CONTRIBUTOR | Part of #147913
Replace `unimplemented` with`unimplemented_v2` in `torch/_dynamo/variables/base.py`
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,886,560,117 | Fix addbmm & addmv & baddbmm out dtype check | FFFrog | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 9 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148176
* #148174
----
- torch.addbmm
- torch.addmv
- torch.baddbmm
ISSUE related:
https://github.com/pytorch/pytorch/issues/138399 | true |
2,886,512,098 | The unevenness of torch.randint() during large range(3e9) sampling. | nerymrjr | closed | [
"module: random"
] | 1 | NONE | ### 🐛 Describe the bug
When the sample range is around 3,000,000,000, the performance of torch.randint() becomes very uneven. Specifically, the probability of samples in the first 2/5 of the range is roughly twice that of the last 1/2:
```python
>>> from collections import defaultdict
>>> n = 3000000000
>>> size_ = ... | true |
2,886,510,116 | Fix torch.matmul related out dtype check | FFFrog | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 7 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148174
----
- torch.matmul -> CompositeImplicitAutograd -> dot_out (when left_dim == 1 & right_dim == 1)
-> mv_out (when left_dim == 2 & right_dim == 1)
... | true |
2,886,505,615 | [Don't merge]Upgrade submodule oneDNN to v3.7 (#147498)(ZI) | xuhancn | open | [
"module: mkldnn",
"open source",
"Stale",
"ciflow/binaries",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td",
"ciflow/linux-aarch64"
] | 2 | COLLABORATOR | Fixes #ISSUE_NUMBER
cc @gujinghui @PenghuiCheng @XiaobingSuper @jianyuh @jgong5 @mingfeima @sanchitintel @ashokei @jingxu10 @min-jean-cho @yanbing-j @Guobing-Chen @Xia-Weiwen @snadampal @voznesenskym @penguinwu @EikanWang @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjam... | true |
2,886,472,245 | [Inductor] Layout created with non-sympy.Expr sizes | DDEle | open | [
"triaged",
"oncall: pt2"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
It seems that many variables hinted as `sympy.Expr` is actually not in torch inductor. To expose the problem easily, just add runtime assert like below
```diff
diff --git a/torch/_inductor/ir.py b/torch/_inductor/ir.py
index 17f896d8f1c..8fc703fb2d5 100644
--- a/torch/_inductor/ir.py
+++ b/torc... | true |
2,886,448,919 | Inference llama after Export PTQ | mhs4670go | open | [
"oncall: quantization"
] | 0 | NONE | ### 🐛 Describe the bug
Hello. I'm trying to run LLama 3.2 1B model after [Export PTQ](https://pytorch.org/tutorials/prototype/pt2e_quant_ptq.html). This means the model is already exported when it's run. The reason why I do this is because I want to evaluate quantized model with lm-harness-evaluation or something.
H... | true |
2,886,428,777 | Improvement with comprehensive docstrings and implementation of class method for the code. | kaushik701 | open | [
"open source",
"Stale"
] | 4 | NONE | The code was improved by adding comprehensive docstrings, using proper type annotations, implementing a class method for context retrieval, removing redundant checks, and enhancing overall code organization while maintaining all existing functionality.
Fixes #ISSUE_NUMBER
| true |
2,886,402,170 | PyTorch's nightly version no longer includes the CU118, CU124, and CU121 versions | 1556900941lizerui | open | [
"needs reproduction",
"module: binaries",
"module: cuda",
"triaged"
] | 4 | NONE | I tried to download the CUDA version of Pytorch nightly for CU124 and CU121, but was prompted that the relevant version information could not be found. I can only download the CU126 version, but this version only supports CUDA compute capability 9.0 and is not suitable for my graphics card. Is there a way to download t... | true |
2,886,367,799 | Add note to get start xpu | ZhaoqiongZ | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | CONTRIBUTOR | Installing PyTorch from binaries will automatically install the runtime packages of Intel® Deep Learning Essentials. In this case, if we activate oneAPI in a standalone installation of Intel® Deep Learning Essentials, there will be an environment issue. Therefore, add a note to remind users to avoid this situation. | true |
2,886,363,153 | Build pytorch for rocm failed | FlintWangacc | open | [
"module: build",
"module: rocm",
"triaged"
] | 15 | NONE | ### 🐛 Describe the bug
Build pytorch for rocm failed.
```shell
[6907/7754] Building HIPCC object caffe2/CMakeFiles/torch_hip.dir/__/aten/src/ATen/native/hip/torch_hip_generated_AdaptiveAveragePooling.hip.o ... | true |
2,887,587,631 | [Request Help] “torch._dynamo.exc.UserError: Dynamic control flow is not supported at the moment.” “torch._dynamo.exc.UncapturedHigherOrderOpError: Cond doesn't work unless it is captured completely with torch.compile.” | liye0626 | open | [
"triaged",
"oncall: pt2",
"oncall: export"
] | 4 | NONE | Background: I tried to integrate EAGLE-2 into ExecuTorch, but encountered some errors.
Relate code:
```python
if i not in noleaf_index: # An error occurred at this branch
cid = i
depth = position_ids_list[i]
for j in reversed(range(depth + 1)):
... | true |
2,886,345,345 | DISABLED test_nonstrict_trace_pre_existing_custom_class (__main__.DecoratorTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamo"
] | 4 | NONE | Platforms: linux, mac, macos
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_nonstrict_trace_pre_existing_custom_class&suite=DecoratorTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/37962003305).
O... | true |
2,886,289,057 | Excessive memory usage during compilation start up for (atleast some) in place ops | amouldon | closed | [
"triaged",
"oncall: pt2"
] | 4 | NONE | ### 🐛 Describe the bug
When using torch.compile with this (no gradients/backwards involved):
```
class MyModule(nn.Module):
def __init__(self):
super().__init__()
self.accum_tensor = torch.zeros((64,2048, 2048), device='cuda')
def forward(self, x):
self.accum_tensor.mul_(torch.rand_... | true |
2,886,279,974 | Revert D70262395 | wdvr | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)",
"module: dynamo",
"ciflow/inductor",
"module: compiled autograd"
] | 7 | CONTRIBUTOR | Summary:
This reverts #147804 due to internal revert.
---
This diff reverts D70262395
Reviewed By: RossMcKenzie
Differential Revision: D70318024
@diff-train-skip-merge
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @voznesenskym @penguinwu @EikanWang @jgong5 @Gu... | true |
2,886,279,237 | [Don't merge]Upgrade submodule oneDNN to v3.7 (#147498)(Z7) | xuhancn | open | [
"module: mkldnn",
"open source",
"Stale",
"ciflow/binaries",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td",
"ciflow/linux-aarch64"
] | 2 | COLLABORATOR | Fixes #ISSUE_NUMBER
cc @gujinghui @PenghuiCheng @XiaobingSuper @jianyuh @jgong5 @mingfeima @sanchitintel @ashokei @jingxu10 @min-jean-cho @yanbing-j @Guobing-Chen @Xia-Weiwen @snadampal @voznesenskym @penguinwu @EikanWang @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjam... | true |
2,886,248,356 | Specifying device_id in init_process_group causes tensor parallel + pipeline parallel to fail | seanxwzhang | open | [
"oncall: distributed",
"triaged"
] | 2 | NONE | ### 🐛 Describe the bug
When specifying `device_id` in `init_process_group`, my distributed training script (which uses tensor parallel and pipeline parallel) will either hang indefinitely or fail without meaningful error message.
```python
import os
from typing import Optional
import torch
import torch.nn.function... | true |
2,886,225,041 | [inductor][cutlass] Environment variables for allow/denylist | bertmaher | 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):
* __->__ #148161
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,886,194,107 | draft | pianpwk | open | [
"Stale",
"release notes: fx",
"fx",
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv @voznesenskym @penguinwu @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,886,190,695 | Remove unnecessary tensor clone | cyyever | closed | [
"oncall: distributed",
"oncall: jit",
"open source",
"Merged",
"NNC",
"ciflow/trunk",
"release notes: distributed (c10d)",
"ciflow/mps"
] | 7 | COLLABORATOR | Fixes #ISSUE_NUMBER
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @EikanWang @jgong5 @wenzhe-nrv @sanchitintel | true |
2,886,139,258 | Replace unimplemented with unimplemented_v2 for dynamo | FFFrog | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 4 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148158
torch/_dynamo/variables/constant.py
https://github.com/pytorch/pytorch/issues/147913
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng ... | true |
2,886,135,400 | Use Python 3.9 typing | cyyever | closed | [
"oncall: distributed",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: dataloader",
"topic: not user facing",
"ciflow/inductor",
"release notes: distributed (checkpoint)",
"oncall: distributed checkpointing"
] | 3 | COLLABORATOR | Fixes #ISSUE_NUMBER
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @LucasLLC @MeetVadakkanchery @mhorowitz @pradeepfn @ekr0 | true |
2,886,114,938 | [MPS][Complex] Conjugations are broken | KexinFeng | closed | [
"triaged",
"module: complex",
"module: correctness (silent)",
"module: mps"
] | 3 | NONE | ## Intro
Basically, this issue is the incorrect result of complex unit `1j` computation. It maybe a trivial bug but it **completely cancels** all the complex number support on MPS. It would be great to have it solved.
### 🐛 Describe the bug
The cpu result is real and correct while the apple mps result is complex wh... | true |
2,886,089,665 | [Break XPU][Inductor UT] Avoid custom op registration conflicts in test_auto_functionalize.py. | etaf | closed | [
"open source",
"Merged",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #147727
* #148178
* __->__ #148155
Fix #148148
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aak... | true |
2,886,085,960 | [MPS] Implement linear1d as shader | malfet | closed | [
"Merged",
"topic: bug fixes",
"topic: performance",
"release notes: mps",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148187
* __->__ #148154
And get rid of MPS call, as for some reason implementation via MPSGraph
API call is 100x+ times slower that Metal shader, at least according to
the following benchmark
```python
import torch
import time
import subpro... | true |
2,886,075,789 | [inductor] [cuda] [MultiheadAttention] `nn.MultiheadAttention-torch.reciprocal` outputs a big difference with eager | shaoyuyoung | closed | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
**symptom description**: It seems that cuda outputs a bigger difference (compared with CPU).
**device backend**: only triton
**repro**
```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_en... | true |
2,886,053,122 | [inductor] `F.fractional_max_pool2d` throws `LoweringException: ZeroDivisionError: division by zero` while eager passes the check | shaoyuyoung | closed | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
**symptom**: `F.fractional_max_pool2d` throws `LoweringException: ZeroDivisionError: division by zero` while eager passes the check.
**device backend**: both triton and CPP
**repro**
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch._inductor import config
... | true |
2,886,030,801 | [pytree][fwAD] make `UnpackedDualTensor` a true namedtuple | XuehaiPan | closed | [
"open source",
"ciflow/trunk",
"topic: not user facing"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148151
* #113258
* #113257
* #147880
| true |
2,886,014,767 | [pytorch elastic] [RHEL] multiple processes call to dist.destroy_process_group() cause an RuntimeError: CUDA error: CUDA-capable device(s) is/are busy or unavailable | bbelgodere | open | [
"oncall: distributed",
"triaged"
] | 1 | NONE | ### 🐛 Describe the bug
While working on another piece of code, we've run into an issue which causes an `RuntimeError: CUDA error: CUDA-capable device(s) is/are busy or unavailable` error to occur when at the end of a program when `dist.destroy_process_group()` is called by multiple processes.
Whichever rank calls t... | true |
2,886,010,081 | Enable kineto for XPU | xuhancn | closed | [
"open source",
"ciflow/binaries",
"ciflow/trunk",
"topic: not user facing",
"ciflow/xpu"
] | 1 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,885,979,402 | [Inductor UT] RuntimeError: Tried to register an operator with the same name and overload name multiple times. | etaf | closed | [] | 0 | COLLABORATOR | ### 🐛 Describe the bug
When running python test/inductor/test_auto_functionalize.py in latest PyTorch main (6ccbff1450bb3936636377d3910906f5666ddcfa), we can find some case failures like:
```
======================================================================
ERROR: test_recompile (__main__.AutoFunctionalizeTests... | true |
2,885,969,735 | [Submodule][FlashAttention] Bump to 2.7.4 | drisspg | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148147
# Summary
This makes me happy | true |
2,885,966,027 | Unify OpOverload._get_dispatch and HigherOrderOperator.dispatch | zou3519 | open | [
"triaged",
"oncall: pt2",
"module: aotdispatch"
] | 0 | CONTRIBUTOR | It's not clear to me why these have diverged
cc @chauhang @penguinwu @bdhirsh | true |
2,885,948,693 | Fix code descriptions in the test package. | threewebcode | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 15 | CONTRIBUTOR | The parameter and function description have something wrong and make them correct.
| true |
2,885,942,166 | Torch export does not preserve original edges between nodes | corehalt | open | [
"triaged",
"oncall: pt2",
"module: dynamo",
"oncall: export"
] | 5 | NONE | ### 🐛 Describe the bug
Please refer to this issue on Executorch side where the issue was originally reported:
https://github.com/pytorch/executorch/issues/8758
I thought the problem might be on Executorch side but the problem actually happens on torch.export side.
For quick reference, this code:
```
class SPPF(nn.... | true |
2,885,929,747 | test_reference_numerics_normal fails with certain versions of numpy/scipy | nWEIdia | closed | [
"module: tests",
"triaged",
"module: numpy",
"module: scipy compatibility"
] | 1 | COLLABORATOR | ### 🐛 Describe the bug
We would encounter errors like the following:
` File "/opt/pytorch/pytorch/test/test_unary_ufuncs.py", line 247, in _test_reference_numeric
expected = op.ref(a, **numpy_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^ ... | true |
2,885,916,296 | Remove outdated CUDA version check | cyyever | closed | [
"oncall: distributed",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 6 | COLLABORATOR | Since Torch requires CUDA>=11, some checks can be removed.
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,885,876,344 | Applying online softmax patterns on joint_graph cause 1.2x peak memory regression for TB hf_T5_base model | shunting314 | open | [
"triaged",
"oncall: pt2"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
Command
```time python benchmarks/dynamo/torchbench.py --backend inductor --amp --performance --only hf_T5_base --training```
tlparse for joint-graph pattern: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/.tmpk7zTmz/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&w... | true |
2,885,864,466 | Disable cudnn to avoid creating guards that denies exporting | yushangdi | open | [
"fb-exported",
"Stale",
"ciflow/trunk",
"topic: not user facing"
] | 7 | CONTRIBUTOR | Summary:
Fixes https://github.com/pytorch/pytorch/issues/147623
This code https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/Normalization.cpp#L504-L518 produces guards that raise ConstraintViolation error in batchnorm op.
We disable cudnn in export tracing to avoid creating such guards
Depen... | true |
2,885,864,445 | [canary] force_nn_module_property_static_shapes=False | bobrenjc93 | closed | [
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148139
As part of the dynamic shapes roadmap this half, we want to reduce the number of unrolled out flags. This is one that limits dynamism and doesn't seem to affect compile time or correctness. Let's flip it to False by default... | true |
2,885,864,391 | [canary] force_parameter_static_shapes=False | bobrenjc93 | closed | [
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148139
* __->__ #148138
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,885,858,134 | Add cufile to list of libraries to preload | atalman | closed | [
"Merged",
"release notes: releng",
"topic: bug fixes"
] | 3 | CONTRIBUTOR | Fixes: https://github.com/pytorch/pytorch/issues/148120
Test with almalinux/9-base:latest :
```
>>> import torch
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib64/python3.9/site-packages/torch/__init__.py", line 401, in <module>
from torch._C import * # noqa... | true |
2,885,850,636 | Checks kv pair indexing in OrderedPreservingDictTest.test_range_insert | redwrasse | open | [
"triaged",
"open source",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 12 | CONTRIBUTOR | `OrderedPreservingDictTest.test_range_insert` has an [unused loop variable `j`](https://github.com/pytorch/pytorch/blob/main/c10/test/util/ordered_preserving_dict_test.cpp#L186), I think taken from the [inspired project](https://github.com/pytorch/pytorch/blob/main/c10/test/util/ordered_preserving_dict_test.cpp#L165) t... | true |
2,885,836,943 | Remove manylinux 2014 artifacts | atalman | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | CONTRIBUTOR | 1. Switch Magma build to Manylinux 2.28 base
2. Use manylinux 2.28 as default in populate_binary_env.sh
3. Remove manylinux 2014 docker builds | true |
2,885,831,144 | add skips to test_notifies_oom and test_set_per_process_memory_fraction | Fuzzkatt | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | COLLABORATOR | Tests fail in NVIDIA internal CI since we do not support nvml on Jetson, but nvml is required for OOM reporting to work properly, so we are skipping the failing tests for now.
cc @nWEIdia @eqy | true |
2,885,790,600 | [MPS] fix empty place holder error for smooth l1 loss | Isalia20 | closed | [
"open source",
"Merged",
"topic: bug fixes",
"module: mps",
"release notes: mps"
] | 6 | COLLABORATOR | Fixes #123171
And parametrizes the tests for it
cc @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen | true |
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