This PR enables `-Winconsistent-missing-destructor-override` and `-Winconsistent-missing-override`
and fixes violations.
<!--
copilot:summary
-->
### <samp>🤖 Generated by Copilot at 47e904e</samp>
This pull request updates the code of various classes and operators in the `caffe2` and `aten` subdirectories to use the `override` specifier instead of the `virtual` keyword for destructors and other virtual functions that override a base class function. This improves the code readability, quality, and consistency with C++ best practices. It also modifies the `./CMakeLists.txt` file to enable warnings for these specifiers, but disable errors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104032
Approved by: https://github.com/malfet
Disable tests using quantized operators if QNNPACK is not available
Two disabled tests use Int8FC operators
which are not available if QNNPACK is not available,
and fail only due to that.
Disable cpuid_test on s390x
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99871
Approved by: https://github.com/albanD
This PR introduces some modifications:
1. We find out some const function parameters that can be passed by reference and add the reference.
2. We find more opportunists of passing by value and change them accordingly.
3. Some use-after-move errors are fixed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95942
Approved by: https://github.com/Skylion007
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72492
Having same blob name present in external inputs and external outputs is a valid case, so relaxing the validation for that.
Reviewed By: yyetim
Differential Revision: D34062055
fbshipit-source-id: 6772ef9c3259da221207d14e5cc93a7777002ef2
(cherry picked from commit 0de66a2941)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70248
Modified loops in files under fbsource/fbcode/caffe2/ from the format
```
for(TYPE var=x0;var<x_max;x++)
```
to the format
```
for(const auto var: irange(xmax))
```
This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand.
Test Plan: Sandcastle
Reviewed By: malfet
Differential Revision: D32813863
fbshipit-source-id: 527244b4a2b220fdfe7f17dee3599603f492a2ca
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66743
Modified loops in files under fbsource/fbcode/caffe2/ from the format
`for(TYPE var=x0;var<x_max;x++)`
to the format
`for(const auto var: irange(xmax))`
This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand.
Test Plan: Sandcastle
Reviewed By: malfet
Differential Revision: D31705359
fbshipit-source-id: c9ea2fbc0f9cd29e97a52dcb203addc5f2abb09b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66234
Modified loops in files under fbsource/fbcode/caffe2/ from the format
`for(TYPE var=x0;var<x_max;x++)`
to the format
`for(const auto var: irange(xmax))`
This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand.
bypass_size_limit
allow-large-files
Test Plan: Sandcastle
Reviewed By: ngimel
Differential Revision: D30652629
fbshipit-source-id: 0ae6c4bbbb554bad42e372792a6430e1acf15e3e
Summary:
Delete `-Wno-unused-variable` from top level `CMakeLists.txt`
Still suppress those warnings for tests and `torch_python`
Delete number of unused variables from caffe2 code
Use `(void)var;` to suppress unused variable in range loops
Use `C10_UNUSED` for global constructors and use `constexpr` instead of `static` for global constants
Do not delete `caffe2::OperatorBase::Output` calls as they have side effects
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66041
Reviewed By: ngimel
Differential Revision: D31360142
Pulled By: malfet
fbshipit-source-id: 6fdfb9f91efdc49ca984a2f2a17ee377d28210c8
Summary:
Delete `-Wno-unused-variable` from top level `CMakeLists.txt`
Still suppress those warnings for tests and `torch_python`
Delete number of unused variables from caffe2 code
Use `(void)var;` to suppress unused variable in range loops
Use `C10_UNUSED` for global constructors and use `constexpr` instead of `static` for global constants
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65954
Reviewed By: ngimel
Differential Revision: D31326599
Pulled By: malfet
fbshipit-source-id: 924155f1257a2ba1896c50512f615e45ca1f61f3
Summary:
Add `-Wno-writable-strings`(which is clang's flavor of `-Wwrite-strings`) to list of warnings ignored while compiling torch_python.
Avoid unnecessary copies in range loop
Fix number of signed-unsigned comparisons
Found while building locally on M1
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62930
Reviewed By: albanD
Differential Revision: D30171981
Pulled By: malfet
fbshipit-source-id: 25bd43dab5675f927ca707e32737ed178b04651e
Summary:
Replace for loop with for `irange` loop. Also fix some unused variable warnings in range loop cases
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62928
Reviewed By: driazati
Differential Revision: D30171904
Pulled By: malfet
fbshipit-source-id: 1b437a0f7e3515f4a2e324f3450e93312f1933ae
Summary:
As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH`
All changes but the ones to `.clang-tidy` are generated using following script:
```
for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008
Reviewed By: driazati, r-barnes
Differential Revision: D29838584
Pulled By: malfet
fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57366
We often get error messages such as
```
Model failed AOT (glow ahead-of-time compilation) with exception: Error during AOT optimization (non-provisioned addNetwork):
Non-recoverable device error when adding network:
Error code: PARTITIONER_ERROR
Error message: Did not find a partition with an SLS node
Error return stack:
--------------------------------------------------------------------------------
glow/glow/lib/Partitioner/Partitioner.cpp:1244
--------------------------------------------------------------------------------
glow/glow/lib/Runtime/HostManager/HostManager.cpp:375
--------------------------------------------------------------------------------
```
This makes the error message more clear by checking for the number of OnnixifiOp created before going into Glow. The check is enabled with the `verify_only_single_subnet` flag, and is disabled by default.
Test Plan: Unit tests pass
Reviewed By: khabinov
Differential Revision: D28097674
fbshipit-source-id: 0eefd8f6ec1a82546b759be8e541256bf271a673
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58069
We want to tell user 5821 means ONNXIFI_EVENT_STATE_NONSIGNALLED in the error message.
Added that status code to the mapping and the error message output.
Reviewed By: hl475
Differential Revision: D28359864
fbshipit-source-id: 87f50ddd4ded9ced03ec6af6a1a4ef85bd2195d6
Summary:
This diff enabled mapping a selected set of Ads embeddings to the T17 host on hierarchical memory (nvmify). To achieve that the following is implemented:
- Allow fo OTHER net to be both onnxified and nvmified
- For that an allowlist placement policy is added to the nvmify stack
- onnxifi_transform is lightly updated to accept a blacklist of operators based on name
- nvm transform is broken into two parts, op replacement, and blob update.
- A drived class `SeqBlobNVMReader` is defined which adds the functionality to load blobs to the card or nvm.
Test Plan:
* Unit test
* Run predictor replayer: selectively load the following ads embedding to NVM as in `--caffe2_nvm_dram_placement_file=/home/hanli/nvm_allowlist`:
```
SPARSE_AD_ACCOUNT_ID
SPARSE_NEW_AD_ID_COARSE
SPARSE_NEW_AD_ID_REFINED
SPARSE_NEW_CAMPAIGN_ID
SPARSE_NEW_TARGET_ID
SPARSE_NEW_AD_CLUSTER_ID
SPARSE_NEW_PAGE_ID
SPARSE_NEW_STORY_ID
SPARSE_NEW_VIDEO_ID
SPARSE_ENTITY_EQUIVALENCE_KEY
SPARSE_ENTITY_EQUIVALENCE_KEY_NO_CREATIVE
```
major parameter change in sigrid_remote_predictor_glow_nnpi:
```
--caffe2_nets_to_nvmify=DISAGG_ACC_REMOTE_OTHER \
--caffe2_nvm_sls_ops=SparseLengthsSumFused8BitRowwise,SparseLengthsWeightedSumFused8BitRowwise,SparseLengthsSumFused4BitRowwise,SparseLengthsWeightedSumFused4BitRowwise,SparseLengthsSum4BitRowwiseSparse \
--caffe2_nvm_table_path=/home/hanli/tables/225412100_2870/ \
--caffe2_nvm_dram_placement_file=/home/hanli/nvm_allowlist \
--caffe2_nvm_dram_placement_policy=by_file_allowlist \
--caffe2_predictor_nets_to_load=DISAGG_ACC_REMOTE_OTHER
```
In predictor log, observe that the blobs to be NVMified are transformed in op types, skipped in Onnxifi transform, and deferred loaded and do NVM net transform:
```
I0416 09:59:29.550690 662344 Nvmifier.cpp:142] ^[[92mReplacing SparseLengthsSumFused4BitRowwise with NVM variant.^[[0m
I0416 09:59:29.550701 662344 Nvmifier.cpp:142] ^[[92mReplacing SparseLengthsSumFused4BitRowwise with NVM variant.^[[0m
I0416 09:59:29.550705 662344 Nvmifier.cpp:142] ^[[92mReplacing SparseLengthsSumFused4BitRowwise with NVM variant.^[[0m
I0416 09:59:29.550712 662344 Nvmifier.cpp:142] ^[[92mReplacing SparseLengthsSumFused4BitRowwise with NVM variant.^[[0m
I0416 09:59:29.550715 662344 Nvmifier.cpp:142] ^[[92mReplacing SparseLengthsSumFused4BitRowwise with NVM variant.^[[0m
I0416 09:59:29.550721 662344 Nvmifier.cpp:142] ^[[92mReplacing SparseLengthsSumFused4BitRowwise with NVM variant.^[[0m
...
I0416 09:59:31.665369 662344 onnxifi_transformer.cc:1097] Skipping blocklisted op SparseLengthsSumFused4BitRowwiseNVM at pos 770
I0416 09:59:31.667042 662344 onnxifi_transformer.cc:1097] Skipping blocklisted op SparseLengthsSumFused4BitRowwiseNVM at pos 777
I0416 09:59:31.667294 662344 onnxifi_transformer.cc:1097] Skipping blocklisted op SparseLengthsSumFused4BitRowwiseNVM at pos 779
I0416 09:59:31.668828 662344 onnxifi_transformer.cc:1097] Skipping blocklisted op SparseLengthsSumFused4BitRowwiseNVM at pos 786
I0416 09:59:31.668843 662344 onnxifi_transformer.cc:1097] Skipping blocklisted op SparseLengthsSumFused4BitRowwiseNVM at pos 787
I0416 09:59:31.669909 662344 onnxifi_transformer.cc:1097] Skipping blocklisted op SparseLengthsSumFused4BitRowwiseNVM at pos 792
...
I0416 10:01:09.087282 662344 Nvmifier.cpp:346] found the name: table0
I0416 10:01:09.373975 662344 Nvmifier.cpp:374] ^[[96mSaved /home/hanli/tables/225412100_2870/table0^[[0m
I0416 10:01:09.376008 662344 Nvmifier.cpp:343] filename: sparse_nn_sparse_arch_SPARSE_NEW_AD_ID_COARSE_dedicated_13_w_EmbeddingFusedUint4Quantization
..
I0416 10:11:05.310854 662344 Nvmifier.cpp:161] ^[[95mNVMifying the model.^[[0m
I0416 10:11:05.310887 662344 Nvmifier.cpp:185] found the name: table0 for sparse_nn_sparse_arch_SPARSE_NEW_AD_ID_COARSE_dedicated_13_w_EmbeddingFusedUint4Quantization
I0416 10:11:07.580587 662344 Nvmifier.cpp:185] found the name: table4 for sparse_nn_sparse_arch_SPARSE_AD_ACCOUNT_ID_dedicated_20_w_EmbeddingFusedUint4Quantization
I0416 10:11:07.580648 662344 Nvmifier.cpp:185] found the name: table3 for sparse_nn_sparse_arch_SPARSE_ENTITY_EQUIVALENCE_KEY_dedicated_22_w_EmbeddingFusedUint4Quantization
I0416 10:11:07.580667 662344 Nvmifier.cpp:185] found the name: table5 for sparse_nn_sparse_arch_SPARSE_NEW_TARGET_ID_dedicated_29_w_EmbeddingFusedUint4Quantization
I0416 10:11:07.580682 662344 Nvmifier.cpp:185] found the name: table2 for sparse_nn_sparse_arch_SPARSE_NEW_AD_ID_REFINED_dedicated_30_w_EmbeddingFusedUint4Quantization
I0416 10:11:07.580695 662344 Nvmifier.cpp:185] found the name: table1 for sparse_nn_sparse_arch_SPARSE_NEW_STORY_ID_dedicated_35_w_EmbeddingFusedUint4Quantization
```
Make sure model is properly loaded:
```
I0415 21:42:48.400249 873685 ModelManagerBase.cpp:806] Loaded 225412100_2870 in 730944 ms (63800 ms of IO) memory used 8744167456 byte(s)
```
* Only load user embedding to NVM to make sure baseline use case is not broken by this diff:
```
--caffe2_nets_to_nvmify=DISAGG_ACC_REMOTE_REQUEST_ONLY \
--caffe2_nvm_sls_ops=SparseLengthsSumFused8BitRowwise,SparseLengthsWeightedSumFused8BitRowwise,SparseLengthsSumFused4BitRowwise,SparseLengthsWeightedSumFused4BitRowwise,SparseLengthsSum4BitRowwiseSparse \
--caffe2_nvm_table_path=/home/hanli/tables/225412100_2870/
```
Make sure model is loaded:
```
Loaded 225412100_2870 in 381139 ms (56313 ms of IO) memory used 7043933560 byte(s)
```
* Run feed replayer: `buck-out/gen/sigrid/feed/prediction_replayer/fully_remote_replayer_main --use_new_encoding_for_ads_services --use_new_encoding_from_model_id_to_shard_id --request_file_path /data/users/hanli/f266405843.requests --model_id=265540157_0 --replayer_thread_count=30 --sigrid_predictor_single_host=2401:db00:272c:602e:face:0:10:0 --sigrid_predictor_single_port=7444 --num_iterations=5 --qps=100 --client_name=predictor_v1` (load predictor as in P411172400)
Output:
```
I0428 21:20:25.106635 1396182 FullyRemoteReplayer.cpp:107] Loading requests from /data/users/hanli/f266405843.requests
I0428 21:20:25.547982 1396182 FullyRemoteReplayer.cpp:109] Requests size : 6699
I0428 21:20:25.548146 1396182 Client.cpp:274] V1 tier name: V2 tier name: sigrid.predictor.fully_remote_test V2 fully remote tier name:
I0428 21:20:25.548153 1396182 Client.cpp:282] [MF] Migration Framework (traffic routing) enabled: false
I0428 21:20:25.548172 1396182 ModelRemoteStatus.cpp:206] Selection probabilities znode path: /configerator-gz/.prn
I0428 21:20:25.674162 1396265 ModelRemoteStatus.cpp:612] Found 0 host, 0 shards in predictor tier
I0428 21:20:25.674181 1396182 ModelRemoteStatus.cpp:557] Refresh sigrid model succeeded: 1
I0428 21:21:26.252820 1396265 ModelRemoteStatus.cpp:612] Found 0 host, 0 shards in predictor tier
I0428 21:21:26.252851 1396265 ModelRemoteStatus.cpp:557] Refresh sigrid model succeeded: 1
I0428 21:22:22.225976 1396182 PredictionReplayer.cpp:67] Previous request took too long, not reaching target QPS
I0428 21:22:26.252643 1396265 ModelRemoteStatus.cpp:612] Found 0 host, 0 shards in predictor tier
I0428 21:22:26.252678 1396265 ModelRemoteStatus.cpp:557] Refresh sigrid model succeeded: 1
I0428 21:23:26.252959 1396265 ModelRemoteStatus.cpp:612] Found 0 host, 0 shards in predictor tier
I0428 21:23:26.252987 1396265 ModelRemoteStatus.cpp:557] Refresh sigrid model succeeded: 1
I0428 21:24:26.253135 1396265 ModelRemoteStatus.cpp:612] Found 0 host, 0 shards in predictor tier
I0428 21:24:26.253166 1396265 ModelRemoteStatus.cpp:557] Refresh sigrid model succeeded: 1
I0428 21:25:27.252734 1396265 ModelRemoteStatus.cpp:612] Found 0 host, 0 shards in predictor tier
I0428 21:25:27.252763 1396265 ModelRemoteStatus.cpp:557] Refresh sigrid model succeeded: 1
I0428 21:26:03.172894 1396182 FullyRemoteReplayer.cpp:59] cpu time p25, p50, p75, p95, p99 9570 13011 16218 20788 24840
I0428 21:26:03.172927 1396182 FullyRemoteReplayer.cpp:61] wait time p25, p50, p75, p95, p99 11845 15958 19946 26579 31842
I0428 21:26:03.172940 1396182 FullyRemoteReplayer.cpp:63] wall time p25, p50, p75, p95, p99 16194 20888 25303 31692 37387
```
Reviewed By: ehsanardestani
Differential Revision: D27701121
fbshipit-source-id: e898abc6957c839e402a9763172cf85d9bb84cbd
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
Summary: ATT, to ensure output has the same dim type with the input. We need to find a more generic way though...
Test Plan: unit test
Reviewed By: ipiszy, khabinov
Differential Revision: D27690748
fbshipit-source-id: e53832c67b8ac86973c288d2d6b76ef8e5db14b9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55188
We need to make sure dim types are preserved after applying Transpose.
Test Plan:
```
$ buck build caffe2/caffe2/opt:bound_shape_inference_test && ./buck-out/gen/caffe2/caffe2/opt/bound_shape_inference_test --gtest_filter=*Transpose*
```
Reviewed By: yinghai
Differential Revision: D27514487
fbshipit-source-id: 431b7f2d08664f2ec311a733c926dbb52c63a7d4
Summary:
*Context:* https://github.com/pytorch/pytorch/issues/53406 added a lint for trailing whitespace at the ends of lines. However, in order to pass FB-internal lints, that PR also had to normalize the trailing newlines in four of the files it touched. This PR adds an OSS lint to normalize trailing newlines.
The changes to the following files (made in 54847d0adb9be71be4979cead3d9d4c02160e4cd) are the only manually-written parts of this PR:
- `.github/workflows/lint.yml`
- `mypy-strict.ini`
- `tools/README.md`
- `tools/test/test_trailing_newlines.py`
- `tools/trailing_newlines.py`
I would have liked to make this just a shell one-liner like the other three similar lints, but nothing I could find quite fit the bill. Specifically, all the answers I tried from the following Stack Overflow questions were far too slow (at least a minute and a half to run on this entire repository):
- [How to detect file ends in newline?](https://stackoverflow.com/q/38746)
- [How do I find files that do not end with a newline/linefeed?](https://stackoverflow.com/q/4631068)
- [How to list all files in the Git index without newline at end of file](https://stackoverflow.com/q/27624800)
- [Linux - check if there is an empty line at the end of a file [duplicate]](https://stackoverflow.com/q/34943632)
- [git ensure newline at end of each file](https://stackoverflow.com/q/57770972)
To avoid giving false positives during the few days after this PR is merged, we should probably only merge it after https://github.com/pytorch/pytorch/issues/54967.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54737
Test Plan:
Running the shell script from the "Ensure correct trailing newlines" step in the `quick-checks` job of `.github/workflows/lint.yml` should print no output and exit in a fraction of a second with a status of 0. That was not the case prior to this PR, as shown by this failing GHA workflow run on an earlier draft of this PR:
- https://github.com/pytorch/pytorch/runs/2197446987?check_suite_focus=true
In contrast, this run (after correcting the trailing newlines in this PR) succeeded:
- https://github.com/pytorch/pytorch/pull/54737/checks?check_run_id=2197553241
To unit-test `tools/trailing_newlines.py` itself (this is run as part of our "Test tools" GitHub Actions workflow):
```
python tools/test/test_trailing_newlines.py
```
Reviewed By: malfet
Differential Revision: D27409736
Pulled By: samestep
fbshipit-source-id: 46f565227046b39f68349bbd5633105b2d2e9b19
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53560
If an op like Fused8BitRowwiseQuantizedToFloat ends up on CPU and Tile ends up on an accelerator and only FP16 is supported, then we want to make sure conversion from FP32 to FP16 is done on CPU to save cycles on accelerator.
Reviewed By: ChunliF
Differential Revision: D26862322
fbshipit-source-id: a7af162f2537ee9e4a78e6ef3f587129de410b07
Summary:
Context: https://github.com/pytorch/pytorch/pull/53299#discussion_r587882857
These are the only hand-written parts of this diff:
- the addition to `.github/workflows/lint.yml`
- the file endings changed in these four files (to appease FB-internal land-blocking lints):
- `GLOSSARY.md`
- `aten/src/ATen/core/op_registration/README.md`
- `scripts/README.md`
- `torch/csrc/jit/codegen/fuser/README.md`
The rest was generated by running this command (on macOS):
```
git grep -I -l ' $' -- . ':(exclude)**/contrib/**' ':(exclude)third_party' | xargs gsed -i 's/ *$//'
```
I looked over the auto-generated changes and didn't see anything that looked problematic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53406
Test Plan:
This run (after adding the lint but before removing existing trailing spaces) failed:
- https://github.com/pytorch/pytorch/runs/2043032377
This run (on the tip of this PR) succeeded:
- https://github.com/pytorch/pytorch/runs/2043296348
Reviewed By: walterddr, seemethere
Differential Revision: D26856620
Pulled By: samestep
fbshipit-source-id: 3f0de7f7c2e4b0f1c089eac9b5085a58dd7e0d97
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53332
This is to make sure we don't get `BATCH` dim type for the output.
Reviewed By: ChunliF
Differential Revision: D26836902
fbshipit-source-id: bedbd12330c608406e3466b240015235a28d2c4a
