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Changelog#

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

Unreleased#

Added#

Fixed#

  • Fixed a lot of instances where tensors were first created and then sent to a device with .to(device). Instead, these tensors are now created directly on the target device.
  • Fixed issue with GradientDescentTrainer when constructed with validation_data_loader=None and learning_rate_scheduler!=None.
  • Fixed a bug when removing all handlers in root logger.
  • ShardedDatasetReader now inherits parameters from base_reader when required.

v1.2.2 - 2020-11-17#

Added#

  • Added Docker builds for other torch-supported versions of CUDA.
  • Adds allennlp-semparse as an official, default plugin.

Fixed#

  • GumbelSampler now sorts the beams by their true log prob.

v1.2.1 - 2020-11-10#

Added#

  • Added an optional seed parameter to ModelTestCase.set_up_model which sets the random seed for random, numpy, and torch.
  • Added support for a global plugins file at ~/.allennlp/plugins.
  • Added more documentation about plugins.
  • Added sampler class and parameter in beam search for non-deterministic search, with several implementations, including MultinomialSampler, TopKSampler, TopPSampler, and GumbelSampler. Utilizing GumbelSampler will give Stochastic Beam Search.

Changed#

  • Pass batch metrics to BatchCallback.

Fixed#

  • Fixed a bug where forward hooks were not cleaned up with saliency interpreters if there was an exception.
  • Fixed the computation of saliency maps in the Interpret code when using mismatched indexing. Previously, we would compute gradients from the top of the transformer, after aggregation from wordpieces to tokens, which gives results that are not very informative. Now, we compute gradients with respect to the embedding layer, and aggregate wordpieces to tokens separately.
  • Fixed the heuristics for finding embedding layers in the case of RoBERTa. An update in the transformers library broke our old heuristic.
  • Fixed typo with registered name of ROUGE metric. Previously was rogue, fixed to rouge.
  • Fixed default masks that were erroneously created on the CPU even when a GPU is available.
  • Fixed pretrained embeddings for transformers that don't use end tokens.
  • Fixed the transformer tokenizer cache when the tokenizers are initialized with custom kwargs.

v1.2.0 - 2020-10-29#

Changed#

  • Enforced stricter typing requirements around the use of Optional[T] types.
  • Changed the behavior of Lazy types in from_params methods. Previously, if you defined a Lazy parameter like foo: Lazy[Foo] = None in a custom from_params classmethod, then foo would actually never be None. This behavior is now different. If no params were given for foo, it will be None. You can also now set default values for foo like foo: Lazy[Foo] = Lazy(Foo). Or, if you want you want a default value but also want to allow for None values, you can write it like this: foo: Optional[Lazy[Foo]] = Lazy(Foo).
  • Added support for PyTorch version 1.7.

Fixed#

  • Made it possible to instantiate TrainerCallback from config files.
  • Fixed the remaining broken internal links in the API docs.
  • Fixed a bug where Hotflip would crash with a model that had multiple TokenIndexers and the input used rare vocabulary items.
  • Fixed a bug where BeamSearch would fail if max_steps was equal to 1.
  • Fixed BasicTextFieldEmbedder to not raise ConfigurationError if it has embedders that are empty and not in input

v1.2.0rc1 - 2020-10-22#

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.
  • Made BeamSearch instantiable from_params.
  • 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#

  • Fix CUDA/CPU device mismatch bug during distributed training for categorical accuracy metric.
  • 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.
  • Fixed a bug in the cnn_encoder where activations involving masked tokens could be picked up by the max
  • Fix intra word tokenization for PretrainedTransformerTokenizer when disabling fast tokenizer.

v1.1.0 - 2020-09-08#

Fixed#

  • Fixed handling of some edge cases when constructing classes with FromParams where the class accepts **kwargs.
  • Fixed division by zero error when there are zero-length spans in the input to a PretrainedTransformerMismatchedIndexer.
  • Improved robustness of cached_path when extracting archives so that the cache won't be corrupted if a failure occurs during extraction.
  • Fixed a bug with the average and evalb_bracketing_score metrics in distributed training.

Added#

  • Predictor.capture_model_internals() now accepts a regex specifying which modules to capture

v1.1.0rc4 - 2020-08-20#

Added#

  • Added a workflow to GitHub Actions that will automatically close unassigned stale issues and ping the assignees of assigned stale issues.

Fixed#

  • Fixed a bug in distributed metrics that caused nan values due to repeated addition of an accumulated variable.

v1.1.0rc3 - 2020-08-12#

Fixed#

  • Fixed how truncation was handled with PretrainedTransformerTokenizer. Previously, if max_length was set to None, the tokenizer would still do truncation if the transformer model had a default max length in its config. Also, when max_length was set to a non-None value, several warnings would appear for certain transformer models around the use of the truncation parameter.
  • Fixed evaluation of all metrics when using distributed training.
  • Added a py.typed marker. Fixed type annotations in allennlp.training.util.
  • Fixed problem with automatically detecting whether tokenization is necessary. This affected primarily the Roberta SST model.
  • Improved help text for using the --overrides command line flag.

v1.1.0rc2 - 2020-07-31#

Changed#

  • Upgraded PyTorch requirement to 1.6.
  • Replaced the NVIDIA Apex AMP module with torch's native AMP module. The default trainer (GradientDescentTrainer) now takes a use_amp: bool parameter instead of the old opt_level: str parameter.

Fixed#

  • Removed unnecessary warning about deadlocks in DataLoader.
  • Fixed testing models that only return a loss when they are in training mode.
  • Fixed a bug in FromParams that caused silent failure in case of the parameter type being Optional[Union[...]].
  • Fixed a bug where the program crashes if evaluation_data_loader is a AllennlpLazyDataset.

Added#

  • Added the option to specify requires_grad: false within an optimizer's parameter groups.
  • Added the file-friendly-logging flag back to the train command. Also added this flag to the predict, evaluate, and find-learning-rate commands.
  • Added an EpochCallback to track current epoch as a model class member.
  • Added the option to enable or disable gradient checkpointing for transformer token embedders via boolean parameter gradient_checkpointing.

Removed#

  • Removed the opt_level parameter to Model.load and load_archive. In order to use AMP with a loaded model now, just run the model's forward pass within torch's autocast context.

v1.1.0rc1 - 2020-07-14#

Fixed#

  • Reduced the amount of log messages produced by allennlp.common.file_utils.
  • Fixed a bug where PretrainedTransformerEmbedder parameters appeared to be trainable in the log output even when train_parameters was set to False.
  • Fixed a bug with the sharded dataset reader where it would only read a fraction of the instances in distributed training.
  • Fixed checking equality of ArrayFields.
  • Fixed a bug where NamespaceSwappingField did not work correctly with .empty_field().
  • Put more sensible defaults on the huggingface_adamw optimizer.
  • Simplified logging so that all logging output always goes to one file.
  • Fixed interaction with the python command line debugger.
  • Log the grad norm properly even when we're not clipping it.
  • Fixed a bug where PretrainedModelInitializer fails to initialize a model with a 0-dim tensor
  • Fixed a bug with the layer unfreezing schedule of the SlantedTriangular learning rate scheduler.
  • Fixed a regression with logging in the distributed setting. Only the main worker should write log output to the terminal.
  • Pinned the version of boto3 for package managers (e.g. poetry).
  • Fixed issue #4330 by updating the tokenizers dependency.
  • Fixed a bug in TextClassificationPredictor so that it passes tokenized inputs to the DatasetReader in case it does not have a tokenizer.
  • reg_loss is only now returned for models that have some regularization penalty configured.
  • Fixed a bug that prevented cached_path from downloading assets from GitHub releases.
  • Fixed a bug that erroneously increased last label's false positive count in calculating fbeta metrics.
  • Tqdm output now looks much better when the output is being piped or redirected.
  • Small improvements to how the API documentation is rendered.
  • Only show validation progress bar from main process in distributed training.

Added#

  • Adjust beam search to support multi-layer decoder.
  • A method to ModelTestCase for running basic model tests when you aren't using config files.
  • Added some convenience methods for reading files.
  • Added an option to file_utils.cached_path to automatically extract archives.
  • Added the ability to pass an archive file instead of a local directory to Vocab.from_files.
  • Added the ability to pass an archive file instead of a glob to ShardedDatasetReader.
  • Added a new "linear_with_warmup" learning rate scheduler.
  • Added a check in ShardedDatasetReader that ensures the base reader doesn't implement manual distributed sharding itself.
  • Added an option to PretrainedTransformerEmbedder and PretrainedTransformerMismatchedEmbedder to use a scalar mix of all hidden layers from the transformer model instead of just the last layer. To utilize this, just set last_layer_only to False.
  • cached_path() can now read files inside of archives.
  • Training metrics now include batch_loss and batch_reg_loss in addition to aggregate loss across number of batches.

Changed#

  • Not specifying a cuda_device now automatically determines whether to use a GPU or not.
  • Discovered plugins are logged so you can see what was loaded.
  • allennlp.data.DataLoader is now an abstract registrable class. The default implementation remains the same, but was renamed to allennlp.data.PyTorchDataLoader.
  • BertPooler can now unwrap and re-wrap extra dimensions if necessary.
  • New transformers dependency. Only version >=3.0 now supported.

v1.0.0 - 2020-06-16#

Fixed#

  • Lazy dataset readers now work correctly with multi-process data loading.
  • Fixed race conditions that could occur when using a dataset cache.

Added#

  • A bug where where all datasets would be loaded for vocab creation even if not needed.
  • A parameter to the DatasetReader class: manual_multi_process_sharding. This is similar to the manual_distributed_sharding parameter, but applies when using a multi-process DataLoader.

v1.0.0rc6 - 2020-06-11#

Fixed#

  • A bug where TextFields could not be duplicated since some tokenizers cannot be deep-copied. See https://github.com/allenai/allennlp/issues/4270.
  • Our caching mechanism had the potential to introduce race conditions if multiple processes were attempting to cache the same file at once. This was fixed by using a lock file tied to each cached file.
  • get_text_field_mask() now supports padding indices that are not 0.
  • A bug where predictor.get_gradients() would return an empty dictionary if an embedding layer had trainable set to false
  • Fixes PretrainedTransformerMismatchedIndexer in the case where a token consists of zero word pieces.
  • Fixes a bug when using a lazy dataset reader that results in a UserWarning from PyTorch being printed at every iteration during training.
  • Predictor names were inconsistently switching between dashes and underscores. Now they all use underscores.
  • Predictor.from_path now automatically loads plugins (unless you specify load_plugins=False) so that you don't have to manually import a bunch of modules when instantiating predictors from an archive path.
  • allennlp-server automatically found as a plugin once again.

Added#

  • A duplicate() method on Instances and Fields, to be used instead of copy.deepcopy()
  • A batch sampler that makes sure each batch contains approximately the same number of tokens (MaxTokensBatchSampler)
  • Functions to turn a sequence of token indices back into tokens
  • The ability to use Huggingface encoder/decoder models as token embedders
  • Improvements to beam search
  • ROUGE metric
  • Polynomial decay learning rate scheduler
  • A BatchCallback for logging CPU and GPU memory usage to tensorboard. This is mainly for debugging because using it can cause a significant slowdown in training.
  • Ability to run pretrained transformers as an embedder without training the weights
  • Add Optuna Integrated badge to README.md

Changed#

  • Similar to our caching mechanism, we introduced a lock file to the vocab to avoid race conditions when saving/loading the vocab from/to the same serialization directory in different processes.
  • Changed the Token, Instance, and Batch classes along with all Field classes to "slots" classes. This dramatically reduces the size in memory of instances.
  • SimpleTagger will no longer calculate span-based F1 metric when calculate_span_f1 is False.
  • CPU memory for every worker is now reported in the logs and the metrics. Previously this was only reporting the CPU memory of the master process, and so it was only correct in the non-distributed setting.
  • To be consistent with PyTorch IterableDataset, AllennlpLazyDataset no longer implements __len__(). Previously it would always return 1.
  • Removed old tutorials, in favor of the new AllenNLP Guide
  • Changed the vocabulary loading to consider new lines for Windows/Linux and Mac.

v1.0.0rc5 - 2020-05-26#

Fixed#

  • Fix bug where PretrainedTransformerTokenizer crashed with some transformers (#4267)
  • Make cached_path work offline.
  • Tons of docstring inconsistencies resolved.
  • Nightly builds no longer run on forks.
  • Distributed training now automatically figures out which worker should see which instances
  • A race condition bug in distributed training caused from saving the vocab to file from the master process while other processing might be reading those files.
  • Unused dependencies in setup.py removed.

Added#

  • Additional CI checks to ensure docstrings are consistently formatted.
  • Ability to train on CPU with multiple processes by setting cuda_devices to a list of negative integers in your training config. For example: "distributed": {"cuda_devices": [-1, -1]}. This is mainly to make it easier to test and debug distributed training code..
  • Documentation for when parameters don't need config file entries.

Changed#

  • The allennlp test-install command now just ensures the core submodules can be imported successfully, and prints out some other useful information such as the version, PyTorch version, and the number of GPU devices available.
  • All of the tests moved from allennlp/tests to tests at the root level, and allennlp/tests/fixtures moved to test_fixtures at the root level. The PyPI source and wheel distributions will no longer include tests and fixtures.

v1.0.0rc4 - 2020-05-14#

We first introduced this CHANGELOG after release v1.0.0rc4, so please refer to the GitHub release notes for this and earlier releases.