allenai/allennlp v1.2.0rc1

on GitHub

What's new

Added 🎉

• Added a warning when batches_per_epoch for the validation data loader is inherited from
  the train data loader.
• Added a build-vocab subcommand that can be used to build a vocabulary from a training config file.
• Added tokenizer_kwargs argument to PretrainedTransformerMismatchedIndexer.
• Added tokenizer_kwargs and transformer_kwargs arguments to PretrainedTransformerMismatchedEmbedder.
• Added official support for Python 3.8.
• Added a script: scripts/release_notes.py, which automatically prepares markdown release notes from the
  CHANGELOG and commit history.
• Added a flag --predictions-output-file to the evaluate command, which tells AllenNLP to write the
  predictions from the given dataset to the file as JSON lines.
• Added the ability to ignore certain missing keys when loading a model from an archive. This is done
  by adding a class-level variable called authorized_missing_keys to any PyTorch module that a Model uses.
  If defined, authorized_missing_keys should be a list of regex string patterns.
• Added FBetaMultiLabelMeasure, a multi-label Fbeta metric. This is a subclass of the existing FBetaMeasure.
• Added ability to pass additional key word arguments to cached_transformers.get(), which will be passed on to AutoModel.from_pretrained().
• Added an overrides argument to Predictor.from_path().
• Added a cached-path command.
• Added a function inspect_cache to common.file_utils that prints useful information about the cache. This can also
  be used from the cached-path command with allennlp cached-path --inspect.
• Added a function remove_cache_entries to common.file_utils that removes any cache entries matching the given
  glob patterns. This can used from the cached-path command with allennlp cached-path --remove some-files-*.
• Added logging for the main process when running in distributed mode.
• Added a TrainerCallback object to support state sharing between batch and epoch-level training callbacks.
• Added support for .tar.gz in PretrainedModelInitializer.
• Added classes: nn/samplers/samplers.py with MultinomialSampler, TopKSampler, and TopPSampler for
  sampling indices from log probabilities
• Made BeamSearch registrable.
• Added top_k_sampling and type_p_sampling BeamSearch implementations.
• Pass serialization_dir to Model and DatasetReader.
• Added an optional include_in_archive parameter to the top-level of configuration files. When specified, include_in_archive should be a list of paths relative to the serialization directory which will be bundled up with the final archived model from a training run.

Changed ⚠️

• Subcommands that don't require plugins will no longer cause plugins to be loaded or have an --include-package flag.
• Allow overrides to be JSON string or dict.
• transformers dependency updated to version 3.1.0.
• When cached_path is called on a local archive with extract_archive=True, the archive is now extracted into a unique subdirectory of the cache root instead of a subdirectory of the archive's directory. The extraction directory is also unique to the modification time of the archive, so if the file changes, subsequent calls to cached_path will know to re-extract the archive.
• Removed the truncation_strategy parameter to PretrainedTransformerTokenizer. The way we're calling the tokenizer, the truncation strategy takes no effect anyways.
• Don't use initializers when loading a model, as it is not needed.
• Distributed training will now automatically search for a local open port if the master_port parameter is not provided.
• In training, save model weights before evaluation.
• allennlp.common.util.peak_memory_mb renamed to peak_cpu_memory, and allennlp.common.util.gpu_memory_mb renamed to peak_gpu_memory,
  and they both now return the results in bytes as integers. Also, the peak_gpu_memory function now utilizes PyTorch functions to find the memory
  usage instead of shelling out to the nvidia-smi command. This is more efficient and also more accurate because it only takes
  into account the tensor allocations of the current PyTorch process.
• Make sure weights are first loaded to the cpu when using PretrainedModelInitializer, preventing wasted GPU memory.
• Load dataset readers in load_archive.
• Updated AllenNlpTestCase docstring to remove reference to unittest.TestCase

Removed 👋

• Removed common.util.is_master function.

Fixed ✅

• Fixed a bug where the reported batch_loss metric was incorrect when training with gradient accumulation.
• Class decorators now displayed in API docs.
• Fixed up the documentation for the allennlp.nn.beam_search module.
• Ignore *args when constructing classes with FromParams.
• Ensured some consistency in the types of the values that metrics return.
• Fix a PyTorch warning by explicitly providing the as_tuple argument (leaving
  it as its default value of False) to Tensor.nonzero().
• Remove temporary directory when extracting model archive in load_archive
  at end of function rather than via atexit.
• Fixed a bug where using cached_path() offline could return a cached resource's lock file instead
  of the cache file.
• Fixed a bug where cached_path() would fail if passed a cache_dir with the user home shortcut ~/.
• Fixed a bug in our doc building script where markdown links did not render properly
  if the "href" part of the link (the part inside the ()) was on a new line.
• Changed how gradients are zeroed out with an optimization. See this video from NVIDIA
  at around the 9 minute mark.
• Fixed a bug where parameters to a FromParams class that are dictionaries wouldn't get logged
  when an instance is instantiated from_params.
• Fixed a bug in distributed training where the vocab would be saved from every worker, when it should have been saved by only the local master process.
• Fixed a bug in the calculation of rouge metrics during distributed training where the total sequence count was not being aggregated across GPUs.
• Fixed allennlp.nn.util.add_sentence_boundary_token_ids() to use device parameter of input tensor.
• Be sure to close the TensorBoard writer even when training doesn't finish.
• Fixed the docstring for PyTorchSeq2VecWrapper.

Commits

01644ca Pass serialization_dir to Model, DatasetReader, and support include_in_archive (#4713)
1f29f35 Update transformers requirement from <3.4,>=3.1 to >=3.1,<3.5 (#4741)
6bb9ce9 warn about batches_per_epoch with validation loader (#4735)
00bb6c5 Be sure to close the TensorBoard writer (#4731)
3f23938 Update mkdocs-material requirement from <6.1.0,>=5.5.0 to >=5.5.0,<6.2.0 (#4738)
10c11ce Fix typo in PretrainedTransformerMismatchedEmbedder docstring (#4737)
0e64b4d fix docstring for PyTorchSeq2VecWrapper (#4734)
006bab4 Don't use PretrainedModelInitializer when loading a model (#4711)
ce14bdc Allow usage of .tar.gz with PretrainedModelInitializer (#4709)
c14a056 avoid defaulting to CPU device in add_sentence_boundary_token_ids() (#4727)
24519fd fix typehint on checkpointer method (#4726)
d3c69f7 Bump mypy from 0.782 to 0.790 (#4723)
cccad29 Updated AllenNlpTestCase docstring (#4722)
3a85e35 add reasonable timeout to gpu checks job (#4719)
1ff0658 Added logging for the main process when running in distributed mode (#4710)
b099b69 Add top_k and top_p sampling to BeamSearch (#4695)
bc6f15a Fixes rouge metric calculation corrected for distributed training (#4717)
ae7cf85 automatically find local open port in distributed training (#4696)
321d4f4 TrainerCallback with batch/epoch/end hooks (#4708)
001e1f7 new way of setting env variables in GH Actions (#4700)
c14ea40 Save checkpoint before running evaluation (#4704)
40bb47a Load weights to cpu with PretrainedModelInitializer (#4712)
327188b improve memory helper functions (#4699)
90f0037 fix reported batch_loss (#4706)
39ddb52 CLI improvements (#4692)
edcb6d3 Fix a bug in saving vocab during distributed training (#4705)
3506e3f ensure parameters that are actual dictionaries get logged (#4697)
eb7f256 Add StackOverflow link to README (#4694)
17c3b84 Fix small typo (#4686)
e0b2e26 display class decorators in API docs (#4685)
b9a9284 Update transformers requirement from <3.3,>=3.1 to >=3.1,<3.4 (#4684)
d9bdaa9 add build-vocab command (#4655)
ce604f1 Update mkdocs-material requirement from <5.6.0,>=5.5.0 to >=5.5.0,<6.1.0 (#4679)
c3b5ed7 zero grad optimization (#4673)
9dabf3f Add missing tokenizer/transformer kwargs (#4682)
9ac6c76 Allow overrides to be JSON string or dict (#4680)
55cfb47 The truncation setting doesn't do anything anymore (#4672)
990c9c1 clarify conda Python version in README.md
97db538 official support for Python 3.8 🐍 (#4671)
1e381bb Clean up the documentation for beam search (#4664)
11def8e Update bug_report.md
97fe88d Cached path command (#4652)
c9f376b Update transformers requirement from <3.2,>=3.1 to >=3.1,<3.3 (#4663)
e5e3d02 tick version for nightly releases
b833f90 fix multi-line links in docs (#4660)
d7c06fe Expose from_pretrained keyword arguments (#4651)
175c76b fix confusing distributed logging info (#4654)
fbd2ccc fix numbering in RELEASE_GUIDE
2d5f24b improve how cached_path extracts archives (#4645)
824f97d smooth out release process (#4648)
c7b7c00 Feature/prevent temp directory retention (#4643)
de5d68b Fix tensor.nonzero() function overload warning (#4644)
e8e89d5 add flag for saving predictions to 'evaluate' command (#4637)
e4fd5a0 Multi-label F-beta metric (#4562)
f0e7a78 Create Dependabot config file (#4635)
0e33b0b Return consistent types from metrics (#4632)
2df364f Update transformers requirement from <3.1,>=3.0 to >=3.0,<3.2 (#4621)
6d480aa Improve handling of **kwargs in FromParams (#4629)
bf3206a Workaround for Python not finding imports in spawned processes (#4630)