`libshm.so` depends on the torch library exclusively for `at::RefcountedMapAllocator`,
so it makes sense to move it to c10 along with the other memory allocators.
This means `libshm.so` only depends on `c10` and we don't need to relink
`libshm.so` for every ATen change.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109881
Approved by: https://github.com/albanD
For some weird reason, the batch file gets rid of the `exit /b 1` inside the for loop, so failures never actually get surfaced. Add skips for the tests that were failing.
Also don't run the windows cpu build on main since it's in trunk. This is what currently works for the rocm build.
The temp file failure originates from https://github.com/pytorch/pytorch/pull/108508 (got fixed before I merged this PR)
I'm not sure when the ChunkRecordIteratorTest started failing, but it was after the above.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109393
Approved by: https://github.com/malfet
Enables two ruff rules derived from pylint:
* PLR1722 replaces any exit() calls with sys.exit(). exit() is only designed to be used in repl contexts as may not always be imported by default. This always use the version in the sys module which is better
* PLW3301 replaces nested min / max calls with simplified versions (ie. `min(a, min(b, c))` => `min(a, b. c)`). The new version is more idiomatic and more efficient.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109461
Approved by: https://github.com/ezyang
Summary: As pointed out in https://github.com/pytorch/pytorch/pull/107479, using a set prevents collisions like "a" => "a", "a" => "a_1", "a_1" => "a_1" (but should go to "a_1_1"). We can combine using counters and a set to avoid this problem. Still gets us the performance benefit in the case of collisions with a very minor penalty in a case with no collision.
Test Plan:
Extract this code and run:
```
# New version
from typing import Dict, Set
class Net:
_net_names_used_counters: Dict[str, int] = {}
_net_names_used: Set[str] = set()
staticmethod
def current_prefix():
return "test_prefix"
staticmethod
def _get_next_net_name(basename):
basename = "/".join(x for x in [Net.current_prefix(), basename] if x)
idx = Net._net_names_used_counters.get(basename, 0)
while (name := basename if idx == 0 else f"{basename}_{idx}") in Net._net_names_used:
idx += 1
Net._net_names_used_counters[basename] = idx + 1
Net._net_names_used.add(name)
return name
print(Net._get_next_net_name("basename"))
print(Net._get_next_net_name("x_basename"))
print(Net._get_next_net_name("basename"))
print(Net._get_next_net_name("basename"))
print(Net._get_next_net_name("x_basename"))
print(Net._get_next_net_name("basename_1"))
> test_prefix/basename
> test_prefix/x_basename
> test_prefix/basename_1
> test_prefix/basename_2
> test_prefix/x_basename_1
> test_prefix/basename_1_1
```
Differential Revision: D48576516
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107743
Approved by: https://github.com/zdevito
Reland of PR #94924. The purpose of this PR is to deal with the complicated interactions between MKL and OpenMP.
There are two improvements:
1. It uses a flag to avoid infinite mutual recursion in calling find_package(MKL) and find_package(OpenMP) in some cases.
2. The logic of finding iomp5 is improved and now we can test MKLDNN under ASAN.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104224
Approved by: https://github.com/malfet
**Summary**
Update onednn from v2.7.3 to v3.1.1.
It is bc-breaking as some APIs are changed on oneDNN side. Changes include:
- PyTorch code where oneDNN is directly called
- Submodule `third_party/ideep` to adapt to oneDNN's new API.
- CMAKE files to fix build issues.
**Test plan**
Building issues and correctness are covered by CI checks.
For performance, we have run TorchBench models to ensure there is no regression. Below is the comparison before and after oneDNN update.
Note:
- Base commit of PyTorch: da322ea
- CPU: Intel(R) Xeon(R) Platinum 8380 CPU @ 2.30GHz (Ice Lake)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97957
Approved by: https://github.com/jgong5, https://github.com/jerryzh168
PR #90689 replaces NVTX with NVTX3. However, the torch::nvtoolsext is created only when the third party NVTX is used.
This is clear a logical error. We now move the creation code out of the branch to cover all cases. This should fix the issues reported in the comments of #90689.
It would be better to move configurations of the failed FRL jobs to CI tests so that we can find such issues early before merging.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97582
Approved by: https://github.com/peterbell10
Summary: Adding an enforce gives better error information than raising SIGFPE when division by zero happens. We'll get the actual BlobRef names as well as the error categories.
Test Plan:
Ran a local worker and client using DPP session with empty tensors and checked the error:
`../buck-out/v2/gen/fbcode/data_preproc/perf_test/client --sr2_event_base_pool_size=24`
`../buck-out/v2/gen/fbcode/data_preproc/perf_test/worker --dpp_session_id=5D49F56C98CC95BD97027BC0DDB38D8F`
```{dpp_internal_errorcategory : user_error,
ONCALL : MLDP_CONTROL,
CATEGORY : INPUT_ERROR,
errorsubsystemtags : [DPP_WORKER],
errorcause : USER_ERROR,
RETRYABILITY : 0}F0806 17:47:52.607200 2280375 SchedRuntimeEnv.cpp:385] facebook::data_preproc::NonRetryableGenericUser
Error: User preprocessing error c10::Error: [enforce fail at utility_ops.h:730] input.numel() > 0. 0 vs 0. tensor has t
o be nonempty (Error from operator:
input: "preproc_data_pipeline/preproc/features/default_feature_preproc/normalization/dper_feature_normalization/sparse_
features_processor_1/sparse_feature_transform/F3_ADFINDER_USER_ADS_COFFEE_LSF_FLEXIBLE_BATCH_USER_FB_UIP_FEATURE_IDSCOR
ELIST_ENCODED_FB_UIP_TOP100_IDSCORELIST_ENCODED_1/sequential_1019/id_score_list_quantization_decode_1/Concat:0" input:
"preproc_data_pipeline/preproc/features/default_feature_preproc/normalization/dper_feature_normalization/sparse_feature
s_processor_1/sparse_feature_transform/F3_ADFINDER_USER_ADS_COFFEE_LSF_FLEXIBLE_BATCH_USER_FB_UIP_FEATURE_IDSCORELIST_E
NCODED_FB_UIP_TOP100_IDSCORELIST_ENCODED_1/sequential_1019/id_score_list_quantization_decode_1/Mul_2" input: "preproc_d
ata_pipeline/preproc/features/default_feature_preproc/normalization/dper_feature_normalization/sparse_features_processo
r_1/sparse_feature_transform/F3_ADFINDER_USER_ADS_COFFEE_LSF_FLEXIBLE_BATCH_USER_FB_UIP_FEATURE_IDSCORELIST_ENCODED_FB_UIP_TOP100_IDSCORELIST_ENCODED_1/sequential_1019/id_score_list_quantization_decode_1/encoded_id_lengths" output: "preproc_data_pipeline/preproc/features/default_feature_preproc/normalization/dper_feature_normalization/sparse_features_processor_1/sparse_feature_transform/F3_ADFINDER_USER_ADS_COFFEE_LSF_FLEXIBLE_BATCH_USER_FB_UIP_FEATURE_IDSCORELIST_ENCODED_FB_UIP_TOP100_IDSCORELIST```
Differential Revision: D48104430
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106882
Approved by: https://github.com/kit1980
Summary: Rename static tracepoint macros to better describe their targeted usage.
Test Plan:
Same as for D47159249:
Tested the following macros on test scripts with libbpf USDTs:
* `CAFFE_SDT`
* `CAFFE_DISABLE_SDT`
* `CAFFE_SDT_WITH_SEMAPHORE`
Reviewed By: chaekit
Differential Revision: D47727339
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106380
Approved by: https://github.com/chaekit
Summary:
This stack of PR's integrates cuSPARSELt into PyTorch.
This PR adds support for cuSPARSELt into the build process.
It adds in a new flag, USE_CUSPARSELT that defaults to false.
When USE_CUSPASRELT=1 is specified, the user can also specify
CUSPASRELT_ROOT, which defines the path to the library.
Compiling pytorch with cusparselt support can be done as follows:
``
USE_CUSPARSELT=1
CUSPARSELT_ROOT=/path/to/cusparselt
python setup.py develop
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103700
Approved by: https://github.com/albanD
Summary: Moving static tracepoint macros header to a location where it can be easily used by various PyTorch components (`c10/utill`).
Test Plan:
Same as for D47159249:
Tested the following macros on test scripts with libbpf USDTs:
* `CAFFE_SDT`
* `CAFFE_DISABLE_SDT`
* `CAFFE_SDT_WITH_SEMAPHORE`
Reviewed By: EDG-GH
Differential Revision: D47636258
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105856
Approved by: https://github.com/EDG-GH, https://github.com/chaekit
- BatchLinearAlgebraLib.cpp is now split into one additional file
- BatchLinearAlgebraLib.cpp uses only cusolver APIs
- BatchLinearAlgebraLibBlas.cpp uses only cublas APIs
- hipify operates at the file level and cannot mix cusolver and cublas APIs within the same file
- cmake changes to link against hipblas instead of rocblas
- hipify mappings changes to map cublas -> hipblas instead of rocblas
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105881
Approved by: https://github.com/albanD
Summary:
Fix existing CAFFE static tracepoint macros and make them match the latest FOLLY version.
Per anakryiko, current `CAFE_SDT` definition is broken. Quote:
```
"Arguments: -5@-16(%rbp) -4@$100
Arguments: -8@-16(%rbp) -4@$100
#define FOLLY_SDT_IS_ARRAY_POINTER(x) ((__builtin_classify_type(x) == 14) || \
(__builtin_classify_type(x) == 5))
vs
#define CAFFE_SDT_ISARRAY(x) (__builtin_classify_type(x) == 14)
https://github.com/atgreen/gcc/blob/master/gcc/typeclass.h
that 5 is "pointer_type_class"
so you were right, it's just fixed up version of header
I think it should be 8, not 5
5 is the size of literal, but you don't pass string literal as an argument, you pass its address, so actual argument is a pointer, and so 8 byte long
you can try just fixing up CAFFE_SDT macro
```
{F1048035373}
Test Plan:
Tested the following macros on test scripts with libbpf USDTs:
CAFFE_SDT
CAFFE_DISABLE_SDT
CAFFE_SDT_WITH_SEMAPHORE
Reviewed By: RihamSelim
Differential Revision: D47159249
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105232
Approved by: https://github.com/chaekit, https://github.com/malfet
Summary:
kip_fist_pump
Running any EgoOCR workflow in non-opt modes was breaking with https://fburl.com/strict-weak-ordering
Painstakingly found out that the stable_sort comparator in the generate_proposals caffe2 op was the issue due to numerical imprecision. This was causing Word Detector model to barf with the error. Adding explicit handling for the [irreflexivity property](https://www.boost.org/sgi/stl/StrictWeakOrdering.html) fixes this annoying strict-weak-ordering issue that has bugged me and several others(https://fb.workplace.com/groups/1405155842844877/permalink/7079705785389826/) for a while.
We can finally run all OCR workflows in non-opt mode! :)
Test Plan:
Debugged this with `fdb --disable-auto-breakpoints --secondary-debugger=lldb buck2 run mode/dev-sand ai_demos/server_model_zoo/models/ego_ocr_e2e_prod:ego_ocr_e2e_prod_binary`
and running `breakpoint set -E c++` in the lldb terminal.
Differential Revision: D47446816
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105189
Approved by: https://github.com/malfet, https://github.com/atalman
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Unrelated, to bypass CI failures due to the gcc9 dependency update in Ubuntu-18.04:
- Add hack to squash older libstdc++ from conda environment in favor one from OS to `.ci/docker/install_conda.sh`
- Update bazel cuda builds to focal, as with libstdc++-6.0.32 bazel builds loose the ability to catch exceptions (probably because they link with cupti statically, but I could not found where it is done)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
