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CHANGELOG.md

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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

v0.6.0 - 2024-12-17

Added

  • A bunch of annealing configs
  • constant_with_warmup learning rate schedule
  • one_in_eight configuration for activation checkpointing
  • New tokenizer in the source instead of from huggingface
  • Improved support for GCS
  • torch.compile() now only compiles each block, not the whole model.
  • Support for torch.compile() with dynamic=True
  • Resetting the torch.compile() after every evaluation, because evaluation messes with the compiled versions
  • Added more in-loop evaluation tasks to pick from, mostly for scaling law.

v0.5.1 - 2024-10-17

Added

  • Added ability to try loading latest checkpoint from save folder using --try_load_latest_save.
  • Added support for flash attention and gradient checkpointing to hf_olmo.
  • Added to scripts.compare_wandb_configs.py the ability to more easily compare differences in data mixes and evaluation tasks.
  • Added effective_n_kv_heads to OLMoConfig for hacky VLLM support.

v0.5.0 - 2024-08-26

  • Fixed conversion to HuggingFace model for DDP-trained models.
  • Added support for remote source and destination for HuggingFace model conversion.

Added

  • Added support for document masking via flash-attn during training with --data.generate_doc_lengths.
  • Added config options for model.norm_after, model.scale_emb_init, and auxiliary_loss_multiplier (used with zloss).
  • Added scripts for running experiments on qk_norm, norm reordering, and zloss.
  • Added model.rope_theta configuration option.
  • Added model.embedding_layer_norm configuration option for adding a LN to the embeddings.
  • Added model.emb_init_std configuration option to override the standard deviation used to initialize the embeddings.
  • Added downstream eval task for requests dumped from oe-eval tasks
  • Added CosLinearEnvelope scheduler, which is a pointwise product of a cosine schedule and a linear decay.
  • Added ability to save outputs of submodules for debugging purposes.
  • Added a number of tasks from oe-eval to the downstream eval tasks.
  • Version dolma flan change in named_data_mix.py

Changed

  • Changed default distributed training strategy from single-GPU to FSDP
  • Fixed behavior of effective_memmap_dtype to prevent unrecognized dtypes to be parsed as uint16.

Fixed

  • Fixed restarting a training run in later epochs so that we no longer need to set the flag --epoch=INT.
  • Swapped in correct flan data mix.
  • Fix bug where the attention norm, when applied before the attention block, was modifying the residual stream.
  • Fixed OLMo.from_checkpoint() so that it correctly loads olmo_core and torch_new style checkpoints.
  • Fixed preserve_rng_state being incorrectly set to False when doing gradient checkpointing with dropout

v0.4.0 - 2024-07-11

Added

  • Added clipping fix to Optimizer class to make it work with FSDP no_shard and DDP.
  • Added tests to compare grad norm differences between torch optimizer and clipping and OLMo optimizer and clipping on both CPU and GPU.
  • Expose memmap dtype in data config
  • Added support for DDP training.
  • Added caching to disk of HF datasets used in downstream evals
  • Added FLOPs logging
  • Added configs for OLMo tiny set of models
  • Added configuration field optimizer.record_update_metrics, which defaults to False, but when set to True will trigger AdamW to collect the step size norm and absolute max for each parameter.
  • Added configuration field optimizer.selective_updates, which defaults to False, but when set to True will tell the optimizer to skip updating the parameter and state when the corresponding gradient is 0.
  • Added configuration field optimizer.record_update_metrics, which defaults to False, but when set to True will trigger AdamW to collect the step size norm and absolute max for each parameter.
  • Added olmo_data, a package holding data files like tokenizers.
  • Added ability to load tokenizers from olmo_data package data.
  • Added a script that can run a series of models with predictable scaling properties.

Changed

  • Added original legacy unsharding implementation back, as the default. The new shared memory implementation can be used by passing use_legacy_shared_mem_impl to unshard.py.
  • Refactor weight initialization. IMPORTANT: this does not maintain backwards-compatibility with older configs; the jobs will still run, but may produce different outputs.
  • Changed the behavior of the Lion optimizer to only record the update cosine similarity when optimizer.record_update_metrics is True in order to be consistent with the API.
  • Added HF datasets into olmo_data, and changed downstream eval to load from the package.

Fixed

  • Changed from ignored_index to ignore_index for cross_entropy_loss when flash-attn>=2.5.8.
  • Make hf_olmo support AutoModelForCasualLM and similar HF methods again.

v0.3.0 - 2024-04-25

Added

  • Added support for Grouped Query Attention.
  • Added commonsense_qa and social_iqa downstream evaluation tasks
  • Added ce_loss metric, with TriviaQA and NaturalQuestions tasks
  • Makes it possible to read from http/https the same way we read from s3/r2.
  • Added MMLU multiple choice (A/B/C/D) 5-shot variant downstream tasks
  • Tokenizer patch
  • Added option to specify number of model replicas when using hybrid sharding.

Changed

  • Rename Olmo to OLMo everywhere in the codebase
  • Disabled automatic garbage collection during training, instead we run manually at regular intervals to avoid ranks getting out-of-sync with their own gc.

Removed

  • Removed AMDLayerNorm, since the original layer norm bug has been fixed and we don't need this workaround anymore.
  • Removed OLMoParallelBlock.

Fixed

  • Don't log garbage on nodes that aren't rank 0
  • Don't crash in the HF code when we are referring to a tokenizer in a local file
  • Point official training scripts to publicly available URLs
  • Corrected the resize_token_embeddings method in the OLMoForCausalLM class to properly update the token embeddings when resizing the vocabulary.
  • Changed tie_weights method to a no-op as weight tying is handled in olmo/model.py
  • Fixed the size calculation for qk layer norm
  • Fixed pipeline test failure that occurs due to a bug in transformers version 4.39.1
  • Make hf_olmo compatible with transformers versions >=4.40.0

v0.2.5 - 2024-03-06

Fixed

  • Fixed default value of --tokenizer argument to scripts/prepare_tulu_data.py to be an absolute path, not relative path, the script can be run from other directories.
  • Added the option to directly pass input embeddings to OLMo and OLMoForCausalLM.
  • Added support for Python 3.8.
  • Added code to throw an error if output_attentions is set to True in forward call to OLMoForCausalLM. This functionality hasn't been implemented yet.
  • Correct scheme displayed in error messages that come from R2
  • Fixed running with multiple data loading workers in LUMI
  • Minor bug fix: uninitialized prompts variable

Added

  • Added output_hidden_states argument and associated functionality to OLMo and OLMoForCausalLM to return model intermediate hidden states.
  • Ability to read from R2 like we read from S3
  • Added MMLU downstream evaluation tasks, with prompt variations.
  • Added support for PyTorch v2.2.
  • Added ability to show logs from all ranks
  • Added option for QKV clipping.
  • Added basic_arithmetic downstream evaluation task

Changed

  • Changed legacy checkpoint unsharding to use processes and shared memory instead of threads

v0.2.4 - 2024-02-02

Fixed

  • Fixed an issue with the HuggingFace integration where we were inadvertently using a feature that was introduced in Python 3.10, causing an error for older Python versions.

v0.2.3 - 2024-01-31

v0.2.2 - 2023-12-10

v0.2.1 - 2023-12-10

v0.2.0 - 2023-12-08

Added

  • GPT-based model.
  • Tokenizer and data pre-processing pipeline.
  • training script.
  • Triton-based FlashAttention.