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[tune](deps): Bump pytorch-lightning from 1.0.3 to 1.2.0 in /python/requirements#13

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[tune](deps): Bump pytorch-lightning from 1.0.3 to 1.2.0 in /python/requirements#13
dependabot[bot] wants to merge 1 commit intomasterfrom
dependabot/pip/python/requirements/pytorch-lightning-1.2.0

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@dependabot dependabot bot commented on behalf of github Feb 20, 2021

Bumps pytorch-lightning from 1.0.3 to 1.2.0.

Release notes

Sourced from pytorch-lightning's releases.

Pruning & Quantization & SWA

[1.2.0] - 2021-02-18

Added

  • Added DataType, AverageMethod and MDMCAverageMethod enum in metrics (#5657)
  • Added support for summarized model total params size in megabytes (#5590)
  • Added support for multiple train loaders (#1959)
  • Added Accuracy metric now generalizes to Top-k accuracy for (multi-dimensional) multi-class inputs using the top_k parameter (#4838)
  • Added Accuracy metric now enables the computation of subset accuracy for multi-label or multi-dimensional multi-class inputs with the subset_accuracy parameter (#4838)
  • Added HammingDistance metric to compute the hamming distance (loss) (#4838)
  • Added max_fpr parameter to auroc metric for computing partial auroc metric (#3790)
  • Added StatScores metric to compute the number of true positives, false positives, true negatives and false negatives (#4839)
  • Added R2Score metric (#5241)
  • Added LambdaCallback (#5347)
  • Added BackboneLambdaFinetuningCallback (#5377)
  • Accelerator all_gather supports collection (#5221)
  • Added image_gradients functional metric to compute the image gradients of a given input image. (#5056)
  • Added MetricCollection (#4318)
  • Added .clone() method to metrics (#4318)
  • Added IoU class interface (#4704)
  • Support to tie weights after moving model to TPU via on_post_move_to_device hook
  • Added missing val/test hooks in LightningModule (#5467)
  • The Recall and Precision metrics (and their functional counterparts recall and precision) can now be generalized to Recall@K and Precision@K with the use of top_k parameter (#4842)
  • Added ModelPruning Callback (#5618, #5825, #6045)
  • Added PyTorchProfiler (#5560)
  • Added compositional metrics (#5464)
  • Added Trainer method predict(...) for high performence predictions (#5579)
  • Added on_before_batch_transfer and on_after_batch_transfer data hooks (#3671)
  • Added AUC/AUROC class interface (#5479)
  • Added PredictLoop object (#5752)
  • Added QuantizationAwareTraining callback (#5706, #6040)
  • Added LightningModule.configure_callbacks to enable the definition of model-specific callbacks (#5621)
  • Added dim to PSNR metric for mean-squared-error reduction (#5957)
  • Added promxial policy optimization template to pl_examples (#5394)
  • Added log_graph to CometLogger (#5295)
  • Added possibility for nested loaders (#5404)
  • Added sync_step to Wandb logger (#5351)
  • Added StochasticWeightAveraging callback (#5640)
  • Added LightningDataModule.from_datasets(...) (#5133)
  • Added PL_TORCH_DISTRIBUTED_BACKEND env variable to select backend (#5981)
  • Added Trainer flag to activate Stochastic Weight Averaging (SWA) Trainer(stochastic_weight_avg=True) (#6038)
  • Added DeepSpeed integration (#5954, #6042)

Changed

  • Changed stat_scores metric now calculates stat scores over all classes and gains new parameters, in line with the new StatScores metric (#4839)
  • Changed computer_vision_fine_tunning example to use BackboneLambdaFinetuningCallback (#5377)
  • Changed automatic casting for LoggerConnector metrics (#5218)
  • Changed iou [func] to allow float input (#4704)

... (truncated)

Changelog

Sourced from pytorch-lightning's changelog.

[1.2.0] - 2021-02-18

Added

  • Added DataType, AverageMethod and MDMCAverageMethod enum in metrics (#5657
  • Added support for summarized model total params size in megabytes (#5590)
  • Added support for multiple train loaders (#1959)
  • Added Accuracy metric now generalizes to Top-k accuracy for (multi-dimensional) multi-class inputs using the top_k parameter (#4838)
  • Added Accuracy metric now enables the computation of subset accuracy for multi-label or multi-dimensional multi-class inputs with the subset_accuracy parameter (#4838)
  • Added HammingDistance metric to compute the hamming distance (loss) (#4838)
  • Added max_fpr parameter to auroc metric for computing partial auroc metric (#3790)
  • Added StatScores metric to compute the number of true positives, false positives, true negatives and false negatives (#4839)
  • Added R2Score metric (#5241)
  • Added LambdaCallback (#5347)
  • Added BackboneLambdaFinetuningCallback (#5377)
  • Accelerator all_gather supports collection (#5221)
  • Added image_gradients functional metric to compute the image gradients of a given input image. (#5056)
  • Added MetricCollection (#4318)
  • Added .clone() method to metrics (#4318)
  • Added IoU class interface (#4704)
  • Support to tie weights after moving model to TPU via on_post_move_to_device hook
  • Added missing val/test hooks in LightningModule (#5467)
  • The Recall and Precision metrics (and their functional counterparts recall and precision) can now be generalized to Recall@K and Precision@K with the use of top_k parameter (#4842)
  • Added ModelPruning Callback (#5618, #5825, #6045)
  • Added PyTorchProfiler (#5560)
  • Added compositional metrics (#5464)
  • Added Trainer method predict(...) for high performence predictions (#5579)
  • Added on_before_batch_transfer and on_after_batch_transfer data hooks (#3671)
  • Added AUC/AUROC class interface (#5479)
  • Added PredictLoop object (#5752)
  • Added QuantizationAwareTraining callback (#5706, #6040)
  • Added LightningModule.configure_callbacks to enable the definition of model-specific callbacks (#5621)
  • Added dim to PSNR metric for mean-squared-error reduction (#5957)
  • Added promxial policy optimization template to pl_examples (#5394)
  • Added log_graph to CometLogger (#5295)
  • Added possibility for nested loaders (#5404)
  • Added sync_step to Wandb logger (#5351)
  • Added StochasticWeightAveraging callback (#5640)
  • Added LightningDataModule.from_datasets(...) (#5133)
  • Added PL_TORCH_DISTRIBUTED_BACKEND env variable to select backend (#5981)
  • Added Trainer flag to activate Stochastic Weight Averaging (SWA) Trainer(stochastic_weight_avg=True) (#6038)
  • Added DeepSpeed integration (#5954, #6042)

Changed

  • Changed stat_scores metric now calculates stat scores over all classes and gains new parameters, in line with the new StatScores metric (#4839)

... (truncated)

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@dependabot dependabot bot added the dependencies Pull requests that update a dependency file label Feb 20, 2021
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dependabot bot commented on behalf of github Feb 24, 2021

Superseded by #14.

@dependabot dependabot bot closed this Feb 24, 2021
@dependabot dependabot bot deleted the dependabot/pip/python/requirements/pytorch-lightning-1.2.0 branch February 24, 2021 23:40
rkooo567 pushed a commit that referenced this pull request Jul 27, 2022
We encountered SIGSEGV when running Python test `python/ray/tests/test_failure_2.py::test_list_named_actors_timeout`. The stack is:

```
#0  0x00007fffed30f393 in std::basic_string<char, std::char_traits<char>, std::allocator<char> >::basic_string(std::string const&) ()
   from /lib64/libstdc++.so.6
#1  0x00007fffee707649 in ray::RayLog::GetLoggerName() () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#2  0x00007fffee70aa90 in ray::SpdLogMessage::Flush() () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#3  0x00007fffee70af28 in ray::RayLog::~RayLog() () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#4  0x00007fffee2b570d in ray::asio::testing::(anonymous namespace)::DelayManager::Init() [clone .constprop.0] ()
   from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#5  0x00007fffedd0d95a in _GLOBAL__sub_I_asio_chaos.cc () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#6  0x00007ffff7fe282a in call_init.part () from /lib64/ld-linux-x86-64.so.2
#7  0x00007ffff7fe2931 in _dl_init () from /lib64/ld-linux-x86-64.so.2
#8  0x00007ffff7fe674c in dl_open_worker () from /lib64/ld-linux-x86-64.so.2
#9  0x00007ffff7b82e79 in _dl_catch_exception () from /lib64/libc.so.6
#10 0x00007ffff7fe5ffe in _dl_open () from /lib64/ld-linux-x86-64.so.2
#11 0x00007ffff7d5f39c in dlopen_doit () from /lib64/libdl.so.2
#12 0x00007ffff7b82e79 in _dl_catch_exception () from /lib64/libc.so.6
#13 0x00007ffff7b82f13 in _dl_catch_error () from /lib64/libc.so.6
#14 0x00007ffff7d5fb09 in _dlerror_run () from /lib64/libdl.so.2
#15 0x00007ffff7d5f42a in dlopen@@GLIBC_2.2.5 () from /lib64/libdl.so.2
#16 0x00007fffef04d330 in py_dl_open (self=<optimized out>, args=<optimized out>)
    at /tmp/python-build.20220507135524.257789/Python-3.7.11/Modules/_ctypes/callproc.c:1369
```

The root cause is that when loading `_raylet.so`, `static DelayManager _delay_manager` is initialized and `RAY_LOG(ERROR) << "RAY_testing_asio_delay_us is set to " << delay_env;` is executed. However, the static variables declared in `logging.cc` are not initialized yet (in this case, `std::string RayLog::logger_name_ = "ray_log_sink"`).

It's better not to rely on the initialization order of static variables in different compilation units because it's not guaranteed. I propose to change all `RAY_LOG`s to `std::cerr` in `DelayManager::Init()`.

The crash happens in Ant's internal codebase. Not sure why this test case passes in the community version though.

BTW, I've tried different approaches:

1. Using a static local variable in `get_delay_us` and remove the global variable. This doesn't work because `init()` needs to access the variable as well.
2. Defining the global variable as type `std::unique_ptr<DelayManager>` and initialize it in `get_delay_us`. This works but it requires a lock to be thread-safe.
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