v0.14.0

Changelog

Added

• Support for setuptools based projects in edsnlp.package command
• Pipelines can now be instantiated directly from a config file (instead of having to cast a dict containing their arguments) by putting the @core = "pipeline" or "load" field in the pipeline section)
• edsnlp.load now correctly takes disable, enable and exclude parameters into account
• Pipeline now has a basic repr showing is base langage (mostly useful to know its tokenizer) and its pipes
• New python -m edsnlp.evaluate script to evaluate a model on a dataset
• Sentence detection can now be configured to change the minimum number of newlines to consider a newline-triggered sentence, and disable capitalization checking.
• New eds.split pipe to split a document into multiple documents based on a splitting pattern (useful for training)
• Allow converter argument of edsnlp.data.read/from_... to be a list of converters instead of a single converter
• New revamped and documented edsnlp.train script and API
• Support YAML config files (supported only CFG/INI files before)
• Most of EDS-NLP functions are now clickable in the documentation
• ScheduledOptimizer now accepts schedules directly in place of parameters, and easy parameter selection:

  ScheduledOptimizer(
      optim="adamw",
      module=nlp,
      total_steps=2000,
      groups={
          "^transformer": {
              # lr will go from 0 to 5e-5 then to 0 for params matching "transformer"
              "lr": {"@schedules": "linear", "warmup_rate": 0.1, "start_value": 0 "max_value": 5e-5,},
          },
          "": {
              # lr will go from 3e-4 during 200 steps then to 0 for other params
              "lr": {"@schedules": "linear", "warmup_rate": 0.1, "start_value": 3e-4 "max_value": 3e-4,},
          },
      },
  )
  

Changed

• eds.span_context_getter's parameter context_sents is no longer optional and must be explicitly set to 0 to disable sentence context
• In multi-GPU setups, streams that contain torch components are now stripped of their parameter tensors when sent to CPU Workers since these workers only perform preprocessing and postprocessing and should therefore not need the model parameters.
• The batch_size argument of Pipeline is deprecated and is not used anymore. Use the batch_size argument of stream.map_pipeline instead.

Fixed

• Sort files before iterating over a standoff or json folder to ensure reproducibility
• Sentence detection now correctly match capitalized letters + apostrophe
• We now ensure that the workers pool is properly closed whatever happens (exception, garbage collection, data ending) in the multiprocessing backend. This prevents some executions from hanging indefinitely at the end of the processing.
• Propagate torch sharing strategy to other workers in the multiprocessing backend. This is useful when the system is running out of file descriptors and ulimit -n is not an option. Torch sharing strategy can also be set via an environment variable TORCH_SHARING_STRATEGY (default is file_descriptor, consider using file_system if you encounter issues).

Data API changes

• LazyCollection objects are now called Stream objects
• By default, multiprocessing backend now preserves the order of the input data. To disable this and improve performance, use deterministic=False in the set_processing method
• 🚀 Parallelized GPU inference throughput improvements !
  • For simple {pre-process → model → post-process} pipelines, GPU inference can be up to 30% faster in non-deterministic mode (results can be out of order) and up to 20% faster in deterministic mode (results are in order)
  • For multitask pipelines, GPU inference can be up to twice as fast (measured in a two-tasks BERT+NER+Qualif pipeline on T4 and A100 GPUs)
• The .map_batches, .map_pipeline and .map_gpu methods now support a specific batch_size and batching function, instead of having a single batch size for all pipes
• Readers now have a loop parameter to cycle over the data indefinitely (useful for training)
• Readers now have a shuffle parameter to shuffle the data before iterating over it
• In multiprocessing mode, file based readers now read the data in the workers (was an option before)
• We now support two new special batch sizes
  • "fragment" in the case of parquet datasets: rows of a full parquet file fragment per batch
  • "dataset" which is mostly useful during training, for instance to shuffle the dataset at each epoch.
    These are also compatible in batched writer such as parquet, where each input fragment can be processed and mapped to a single matching output fragment.
• 💥 Breaking change: a map function returning a list or a generator won't be automatically flattened anymore. Use flatten() to flatten the output if needed. This shouldn't change the behavior for most users since most writers (to_pandas, to_polars, to_parquet, ...) still flatten the output
• 💥 Breaking change: the chunk_size and sort_chunks are now deprecated : to sort data before applying a transformation, use .map_batches(custom_sort_fn, batch_size=...)

Training API changes

• We now provide a training script python -m edsnlp.train --config config.cfg that should fit many use cases. Check out the docs !
• In particular, we do not require pytorch's Dataloader for training and can rely solely on EDS-NLP stream/data API, which is better suited for large streamable datasets and dynamic preprocessing (ie different result each time we apply a noised preprocessing op on a sample).
• Each trainable component can now provide a stats field in its preprocess output to log info about the sample (number of words, tokens, spans, ...):
  • these stats are both used for batching (e.g., make batches of no more than "25000 tokens")
  • for logging
  • for computing correct loss means when accumulating gradients over multiple mini-mini-batches
  • for computing correct loss means in multi-GPU setups, since these stats are synchronized and accumulated across GPUs
• Support multi GPU training via hugginface accelerate and EDS-NLP Stream API consideration of env['WOLRD_SIZE'] and env['LOCAL_RANK'] environment variables

Pull Requests

• Improve training tutorials by @percevalw in #331
• Various fixes by @percevalw in #332
• Multiprocessing related fixes by @percevalw in #333
• chore: bump version to 0.14.0 by @percevalw in #334

Full Changelog: v0.13.1...v0.14.0