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Releases: mosaicml/llm-foundry

v0.13.0

15 Oct 06:23
@irenedea irenedea
v0.13.0
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v0.13.0 Latest
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🚀 LLM Foundry v0.13.0

🛠️ Bug Fixes & Cleanup

Pytorch 2.4 Checkpointing (#1569, #1581, #1583)

Resolved issues related to checkpointing for Curriculum Learning (CL) callbacks.

🔧 Dependency Updates

Bumped tiktoken from 0.4.0 to 0.8.0 (#1572)
Updated onnxruntime from 1.19.0 to 1.19.2 (#1590)

What's Changed

Full Changelog: v0.12.0...v0.13.0

Contributors

sashaDoubov, gupta-abhay, and 10 other contributors
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v0.12.0

26 Sep 03:52
@dakinggg dakinggg
v0.12.0
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v0.12.0

🚀 LLM Foundry v0.12.0

New Features

PyTorch 2.4 (#1505)

This release updates LLM Foundry to the PyTorch 2.4 release, bringing with it support for the new features and optimizations in PyTorch 2.4

Extensibility improvements (#1450, #1449, #1468, #1467, #1478, #1493, #1495, #1511, #1512, #1527)

Numerous improvements to the extensibility of the modeling and data loading code, enabling easier reuse for subclassing and extending. Please see the linked PRs for more details on each change.

Improved error messages (#1457, #1459, #1519, #1518, #1522, #1534, #1548, #1551)

Various improved error messages, making debugging user errors more clear.

Sliding window in torch attention (#1455)

We've added support for sliding window attention to the reference attention implementation, allowing easier testing and comparison against more optimized attention variants.

Bug fixes

Extra BOS token for llama 3.1 with completion data (#1476)

A bug resulted in an extra BOS token being added between prompt and response during finetuning. This is fixed so that the prompt and response supplied by the user are concatenated without any extra tokens put between them.

What's Changed

New Contributors

Full Changelog: v0.11.0...v0.12.0

Contributors

gupta-abhay, eldarkurtic, and 13 other contributors
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v0.11.0

13 Aug 17:16
@irenedea irenedea
v0.11.0
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v0.11.0

🚀 LLM Foundry v0.11.0

New Features

LLM Foundry CLI Commands (#1337, #1345, #1348, #1354)

We've added CLI commands for our commonly used scripts.

For example, instead of calling composer llm-foundry/scripts/train.py parameters.yaml, you can now do composer -c llm-foundry train parameters.yaml.

Docker Images Contain All Optional Dependencies (#1431)

LLM Foundry Docker images now have all optional dependencies.

Support for Llama3 Rope Scaling (#1391)

To use it, you can add the following to your parameters:

model:
    name: mpt_causal_lm
    attn_config:
      rope: true
      ...
      rope_impl: hf
      rope_theta: 500000
      rope_hf_config:
        type: llama3
        ...

Tokenizer Registry (#1386)

We now have a tokenizer registry so you can easily add custom tokenizers.

LoadPlanner and SavePlanner Registries (#1358)

We now have LoadPlanner and SavePlanner registries so you can easily add custom checkpoint loading and saving logic.

Faster Auto-packing (#1435)

The auto packing startup is now much faster. To use auto packing with finetuning datasets, you can add packing_ratio: auto to your config like so:

  train_loader:
    name: finetuning
    dataset:
      ...
      packing_ratio: auto

What's Changed

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bfontain, gupta-abhay, and 18 other contributors
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v0.10.0

02 Jul 13:31
@b-chu b-chu
v0.10.0
742f340
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dakinggg Daniel King
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v0.10.0

🚀 LLM Foundry v0.10.0

New Features

Registry for ICL datasets (#1252)

ICL datasets have now been added as a registry.

Curriculum Learning Callback (#1256)

You can now switch dataloaders while training which enables curriculum learning.

train_loader:
  <dataloader parameters>
callback:
  curriculum_learning:
  - duration: <number>tok
    train_loader:  # matches top level train_loader
      <dataloader parameters>
  - duration: <number>tok
    train_loader:
      <dataloader parameters>
  - duration: <number>tok
    train_loader:
      <dataloader parameters>

[Experimental] Interweave Attention Layers (#1299)

You can now override default block configs for certain layers, allowing for different sliding window sizes, reusing the previous layer's kv cache, etc.

model:
    ...
    (usual model configs)
    ...
    block_overrides:
        order:
        - name: default
        - order:
          - name: sliding_window_layer
          - name: sliding_window_layer_reuse
          - name: sliding_window_layer
          - repeat: 2
            name: sliding_window_layer_reuse
          - name: reuse_kv_layer
          repeat: 2
        overrides:
            sliding_window_layer:
                attn_config:
                    sliding_window_size: 1024
            sliding_window_layer_reuse:
                attn_config:
                    sliding_window_size: 1024
                    reuse_kv_layer_idx: -1 # Relative index of the layer whose kv cache to reuse
            reuse_kv_layer:
                attn_config:
                    reuse_kv_layer_idx: -6 # Relative index of the layer whose kv cache to reuse

Bug fixes

What's Changed

New Contributors

Full Changelog: v0.9.1...v0.10.0

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sjawhar, j316chuck, and 11 other contributors
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v0.9.1

24 Jun 23:00
@dakinggg dakinggg
v0.9.1
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v0.9.1

🚀 LLM Foundry v0.9.1

This is a minor patch release to bump the minimum version of mlflow to make sure to buffer writes (mosaicml/composer#3401)

Whats changed

Full Changelog: v0.9.0...v0.9.1

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JackZ-db
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v0.9.0

08 Jun 04:58
@KuuCi KuuCi
v0.9.0
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v0.9.0

🚀 LLM Foundry v0.9.0

New Features

More Token Encoding Types (#1254)

We've expanded the different ways to encode token IDs by allowing uint32 and uint16 formats, which saves significant space for datasets with smaller vocab sizes. We also extended ndarray type support for MDS dataset columns to the generic text dataset and updated conversion scripts accordingly.

Enforced Stricter Configs (#1254, #1225, #1202)

We've implemented stricter enforcement on our Train and Eval configs to further protect users from attempting to train with invalid configs. In conjunction with numerous other PRs, we have stronger error handling to help users use LLM Foundry smoothly.

Previously, this was allowed:

parameters:
   train_dataloader:
      ...
      seed: ${global_seed}
      random_other_key_that's_not_in_the_dataloader_constructor # this is not allowed
   ...
   global_seed: 17 # this is also not allowed

But we've added a variables section. Please do this instead:

parameters:
  variables:
    global_seed: 42
  ...
  train_dataloader:
    seed: ${variables.global_seed}

Chunked text to mds conversion (#1240)

We've updated our text to mds to convertion script to convert files to MDS in chunks. This protects us from loading entire large files at once (potentially causing OOMs), and drastically speeds up converting long sequences.

Breaking Changes and Deprecations

What's Changed

Full Changelog: v0.8.0...v0.9.0

Contributors

j316chuck, irenedea, and 10 other contributors
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v0.8.0

08 May 01:36
@milocress milocress
v0.8.0
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v0.8.0

🚀 LLM Foundry v0.8.0

New Features

Megablocks support (#1102)

Support for training optimized MoE models at large scale.

Check out the megablocks documentation for more information on building state of the art MoE models.

Expanded Registries (#1080, #1093, #1094, #1095, #1096, #1165)

We've expanded support for registries to include, dataloaders, FFN layers, attention layers, norms, and parameter initialization functions.

Check out the README for detailed instructions and code examples!

Support for ShareGPT chat format (#1098)

We now support the ShareGPT format for finetuning.

Breaking Changes and Deprecations

We have updated the minimum supported PyTorch version to torch 2.3 (#1152).

In Context Learning Code Evaluation (#1181)

We've removed the code_evaluation task from the allowed in context learning task types, and we've deleted the InContextLearningCodeEvaluationDataset and InContextLearningCodeEvalAccuracy classes.

Question-Answering

We've removed the question_answering task type. Please use the generation_task_with_answers task instead.

What's Changed

New Contributors

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lupesko, vchiley, and 22 other contributors
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v0.7.0

27 Mar 05:12
@irenedea irenedea
v0.7.0
f044d6c
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v0.7.0

🚀 LLM Foundry v0.7.0

LLM Foundry is an efficient codebase for training, evaluating, and deploying Large Language Models (LLMs) and serves as the foundation for the MPT model series.

In addition to the usual bug fixes and performance improvements, we've made foundry more customizable and extensible!

New Features

Registerable Components (#975, #1043, #1052, #1057)

We've made key components of LLM Foundry registrable, such as models, loggers, and callbacks. You can use the registry to easily customize and extend your training workflows.

This means that you can register new options for these components, and then use them in your yaml config.

Check out the README for detailed instructions and code examples!

Breaking Changes and Deprecations

Deprecated Feature Removals (#1063)

We've removed support for deprecated features: triton attention, Prefix LMs, Llama attention patch, z-loss, and text denoising. These features were little used, and we removed them to focus on the core features that are heavily used.

If you were using these features please let us know how you were using them in a GitHub issue. We're happy to add things back that are in heavy usage.

What's Changed

New Contributors

Full Changelog: v0.6.0...v0.7.0

Contributors

dennyglee, sashaDoubov, and 6 other contributors
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v0.6.0

12 Mar 20:22
@dakinggg dakinggg
v0.6.0
08359b5
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v0.6.0

🚀 LLM Foundry v0.6.0

LLM Foundry is an efficient codebase for training, evaluating, and deploying Large Language Models (LLMs) and serves as the foundation for the MPT model series.

In addition to the usual bug fixes and performance improvements, we've added lots of new features!

New Features

Configurable loss for chat-formatted data (#985)

For chat-formatted data, you can now specify which tokens should be loss-generating in a configurable way.

This can be specified in the train_loader.dataset section of your yaml as follows:

...
train_loader:
  dataset:
    ...
    target_prompts: <FILL IN>
    target_reseponses: <FILL IN>

See the docstring for a description of the options.

Olmo support (#1016)

We've added support for the OLMo model from AI2.

To use OLMo, there are a few configuration parameters you need to set. First of all, you will need to install LLM Foundry with the extra package for OLMo (pip install .[gpu,olmo]).

Then you will need to adjust the tokenizer section of your config as follows:

tokenizer:
  name: allenai/OLMo-7B
  kwargs:
    revision: main
    model_max_length: 2048
    model_input_names:
    - input_ids
    - attention_mask
    trust_remote_code: true

Token accuracy (#983)

We've added a new, on-by-default metric to compute token accuracy in addition to cross entropy and perplexity.

Configurable activation checkpointing (#951)

More configurable activation checkpointing for MPT allows finer granular control over memory usage when training MPT. See the docstring for more details.

Finetuning with multiple streams, and pretokenized data (#933, #945, #946)

We've brought the finetuning dataloader up to speed with the pretraining dataloader to support mixing multiple streams, and pretokenizing finetuning data. See the yaml for a full example.

Eval Gauntlet v0.3 (#824)

We've release v0.3 of our Evaluation gauntlet. See the README for a full description.

Breaking changes and deprecations

Flash attention v1 removal (#1023)

Support for flash attention v1 has now been removed.

Extra BOS token removed (#1003)

When tokenizing prompt/response and chat data, for some tokenizers, we were mistakenly adding an extra BOS token between the prompt and the response. This has now been removed.

Deprecation of triton flash attention, prefixLM, and text denoising (#1007)

We've deprecated use of the triton version of flash attention, prefixLM, and text denoising, as these features were not heavily used or actively maintained.

What's Changed

New Contributors

Full Changelog: v0.5.0...v0.6.0

Contributors

bigning, alextrott16, and 13 other contributors
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v0.5.0

08 Feb 00:01
@irenedea irenedea
v0.5.0
a667ebf
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v0.5.0

🚀 LLM Foundry v0.5.0

LLM Foundry is an efficient codebase for training, evaluating, and deploying Large Language Models (LLMs) and serves as the foundation for the MPT model series.

In addition to the usual bug fixes and performance improvements, we've added lots of new features!

New Features

LoRA Support (with FSDP!) (#886)

LLM Foundry now supports LoRA via an integration with the PEFT library. Within LLM Foundry, run train.py, adding peft_config arguments to the model section of the config .yaml, like so:

model:
  ...
  peft_config:
      r: 16
      peft_type: LORA
      task_type: CAUSAL_LM
      lora_alpha: 32
      lora_dropout: 0.05
      target_modules:
      - q_proj
      - k_proj

Read more about it in the tutorial.

ALiBi for Flash Attention (#820)

We've added support for using ALiBi with Flash Attention (v2.4.2 or higher).

model:
     ...
     attn_config:
         attn_impl: flash
         alibi: True

Chat Data for Finetuning (#884)

We now support finetuning on chat data, with automatic formatting applied using Hugging Face tokenizer chat templates.

Each sample requires a single key "messages" that maps to an array of message objects. Each message object in the array represents a single message in the conversation and must contain the following keys:

  • role : A string indicating the author of the message. Possible values are "system" ,"user" , and "assistant" .
  • content : A string containing the text of the message.

We require that there must be at least one message with the role "assistant", and the last message in the "messages" array must have the role "assistant" .

Here's an example .jsonl with chat data:


{ "messages": [ { "role": "user", "content": "Hi, MPT!" }, { "role": "assistant", "content": "Hi, user!" } ]}
{ "messages": [ 
  { "role": "system": "A conversation between a user and a helpful and honest assistant"}
  { "role": "user", "content": "Hi, MPT!" }, 
  { "role": "assistant", "content": "Hi, user!" },
  { "role": "user", "content": "Is multi-turn chat supported?"},
  { "role": "assistant", "content": "Yes, we can chat for as long as my context length allows." }
]}
...

Safe Load for HuggingFace Datasets (#798)

We now provide a safe_load option when loading HuggingFace datasets for finetuning.

This restricts loaded files to .jsonl, .csv, or .parquet extensions to prevent arbitrary code execution.

To use, set safe_load to true in your dataset configuration:

  train_loader:
    name: finetuning
    dataset:
      safe_load: true
      ...

New PyTorch, Composer, Streaming, and Transformers versions

As always, we've updated to new versions of the core dependencies of LLM Foundry, bringing better performance, new features, and support for new models (mixtral in particular).

Deprecations

Support for Flash Attention v1 (#921)

Will be removed in v0.6.0.

Breaking Changes

Removed support for PyTorch versions before 2.1 (#787)

We no longer support PyTorch versions before 2.1.

Removed Deprecated Features (#948)

We've removed features that have been deprecated for at least one release.

What's Changed

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Contributors

Skylion007, sashaDoubov, and 23 other contributors
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