--- license: llama2 datasets: - bigcode/starcoderdata - togethercomputer/RedPajama-Data-1T - tiiuae/falcon-refinedweb metrics: - code_eval - accuracy pipeline_tag: text-generation tags: - code - text-generation-inference model-index: - name: long_llama_code_7b results: - task: name: Code Generation type: code-generation dataset: name: "HumanEval" type: openai_humaneval metrics: - name: pass@1 type: pass@1 value: 0.286 verified: false --- # LongLLaMA: Focused Transformer Training for Context Scaling

LongLLaMA

{ LongLLaMA Code-7B }
[TLDR](#TLDR) | [Overview](#Overview) | [Usage](#Usage) | [LongLLaMA performance](#LongLLaMA-performance) | [Authors](#Authors) | [Citation](#Citation) | [License](#License) | [Acknowledgments](#Acknowledgments) [FoT continued pretraining](fot_continued_pretraining) | [Instruction tuning](instruction_fine_tuning)
## TLDR This repository contains the research preview of **LongLLaMA, a large language model capable of handling long contexts of 256k tokens or even more**. LongLLaMA is built upon the foundation of [OpenLLaMA](https://github.com/openlm-research/open_llama) and fine-tuned using the [Focused Transformer (FoT)](https://arxiv.org/abs/2307.03170) method. LongLLaMA Code is built upon the foundation of [Code Llama](https://huggingface.co/codellama/CodeLlama-7b-hf). ## Overview ### Base models [Focused Transformer: Contrastive Training for Context Scaling](https://arxiv.org/abs/2307.03170) (FoT) presents a simple method for endowing language models with the ability to handle context consisting possibly of millions of tokens while training on significantly shorter input. FoT permits a subset of attention layers to access a memory cache of (key, value) pairs to extend the context length. The distinctive aspect of FoT is its training procedure, drawing from contrastive learning. Specifically, we deliberately expose the memory attention layers to both relevant and irrelevant keys (like negative samples from unrelated documents). This strategy incentivizes the model to differentiate keys connected with semantically diverse values, thereby enhancing their structure. This, in turn, makes it possible to extrapolate the effective context length much beyond what is seen in training. **LongLLaMA** is an [OpenLLaMA](https://github.com/openlm-research/open_llama) model finetuned with the FoT method, with three layers used for context extension. **Crucially, LongLLaMA is able to extrapolate much beyond the context length seen in training: 8k. E.g., in the passkey retrieval task, it can handle inputs of length 256k**. **LongLLaMA Code** is a [Code Llama](https://huggingface.co/codellama/CodeLlama-7b-hf) model finetuned with the FoT method.
| | [LongLLaMA-3B](https://huggingface.co/syzymon/long_llama_3b) | [LongLLaMA-3Bv1.1](https://huggingface.co/syzymon/long_llama_3b_v1_1) | [LongLLaMA Code-7B](https://huggingface.co/syzymon/long_llama_code_7b) | |----------------|----------|----------|-----------| | Source model | [OpenLLaMA-3B](https://huggingface.co/openlm-research/open_llama_3b_easylm) | [OpenLLaMA-3Bv2](https://huggingface.co/openlm-research/open_llama_3b_v2_easylm) | [CodeLLaMA-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | | Source model tokens | 1T | 1 T | 2T + 0.5 T | | Fine-tuning tokens | 10B | 5B | 35B | - | | Memory layers | 6, 12, 18 | 6, 12, 18 | 8, 16, 24 |
## Results ## Usage See also: * [Colab with LongLLaMA-Instruct-3Bv1.1](https://colab.research.google.com/github/CStanKonrad/long_llama/blob/main/long_llama_instruct_colab.ipynb). * [Colab with an example usage of base LongLLaMA](https://colab.research.google.com/github/CStanKonrad/long_llama/blob/main/long_llama_colab.ipynb). ### Requirements ``` pip install --upgrade pip pip install transformers==4.30 sentencepiece accelerate ``` ### Loading model ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("syzymon/long_llama_code_7b") model = AutoModelForCausalLM.from_pretrained("syzymon/long_llama_code_7b", torch_dtype=torch.float32, trust_remote_code=True) ``` ### Input handling and generation LongLLaMA uses the Hugging Face interface, the long input given to the model will be split into context windows and loaded into the memory cache. ```python prompt = "My name is Julien and I like to" input_ids = tokenizer(prompt, return_tensors="pt").input_ids outputs = model(input_ids=input_ids) ``` During the model call, one can provide the parameter `last_context_length` which specifies the number of tokens left in the last context window. Tuning this parameter can improve generation as the first layers do not have access to memory. See details in [How LongLLaMA handles long inputs](#How-LongLLaMA-handles-long-inputs). ```python generation_output = model.generate( input_ids=input_ids, max_new_tokens=1024, num_beams=1, last_context_length=3072, do_sample=True, temperature=1.0, ) print(tokenizer.decode(generation_output[0])) ``` ### Additional configuration LongLLaMA has several other parameters: * `mem_layers` specifies layers endowed with memory (should be either an empty list or a list of all memory layers specified in the description of the checkpoint). * `mem_dtype` allows changing the type of memory cache * `mem_attention_grouping` can trade off speed for reduced memory usage. When equal to `(4, 2048)`, the memory layers will process at most $4*2048$ queries at once ($4$ heads and $2048$ queries for each head). ```python import torch from transformers import LlamaTokenizer, AutoModelForCausalLM tokenizer = LlamaTokenizer.from_pretrained("syzymon/long_llama_3b_v1_1") model = AutoModelForCausalLM.from_pretrained( "syzymon/long_llama_3b_v1_1", torch_dtype=torch.float32, mem_layers=[], mem_dtype='bfloat16', trust_remote_code=True, mem_attention_grouping=(4, 2048), ) ``` ### Drop-in use with LLaMA code LongLLaMA checkpoints can also be used as a drop-in replacement for LLaMA checkpoints in [Hugging Face implementation of LLaMA](https://huggingface.co/docs/transformers/main/model_doc/llama), but in this case, they will be limited to the original context length. ```python from transformers import LlamaTokenizer, LlamaForCausalLM import torch tokenizer = LlamaTokenizer.from_pretrained("syzymon/long_llama_3b_v1_1") model = LlamaForCausalLM.from_pretrained("syzymon/long_llama_3b_v1_1", torch_dtype=torch.float32) ``` ### How LongLLaMA handles long inputs Inputs over $ctx=2048$ ($ctx=4096$ for LongLLaMA Code) tokens are automatically split into windows $w_1, \ldots, w_m$. The first $m-2$ windows contain $ctx$ tokens each, $w_{m-1}$ has no more than $2048$ tokens, and $w_m$ contains the number of tokens specified by `last_context_length`. The model processes the windows one by one extending the memory cache after each. If `use_cache` is `True`, then the last window will not be loaded to the memory cache but to the local (generation) cache. The memory cache stores $(key, value)$ pairs for each head of the specified memory layers `mem_layers`. In addition to this, it stores attention masks. If `use_cache=True` (which is the case in generation), LongLLaMA will use two caches: the memory cache for the specified layers and the local (generation) cache for all layers. When the local cache exceeds $2048$ elements, its content is moved to the memory cache for the memory layers. For simplicity, context extension is realized with a memory cache and full attention in this repo. Replacing this simple mechanism with a KNN search over an external database is possible with systems like [Faiss](https://github.com/facebookresearch/faiss). This potentially would enable further context length scaling. We leave this as a future work. ## Authors - [Szymon Tworkowski](https://scholar.google.com/citations?user=1V8AeXYAAAAJ&hl=en) - [Konrad Staniszewski](https://scholar.google.com/citations?user=CM6PCBYAAAAJ) - [Mikołaj Pacek](https://scholar.google.com/citations?user=eh6iEbQAAAAJ&hl=en&oi=ao) - [Henryk Michalewski](https://scholar.google.com/citations?user=YdHW1ycAAAAJ&hl=en) - [Yuhuai Wu](https://scholar.google.com/citations?user=bOQGfFIAAAAJ&hl=en) - [Piotr Miłoś](https://scholar.google.pl/citations?user=Se68XecAAAAJ&hl=pl&oi=ao) ## Citation To cite this work please use ```bibtex @misc{tworkowski2023focused, title={Focused Transformer: Contrastive Training for Context Scaling}, author={Szymon Tworkowski and Konrad Staniszewski and Mikołaj Pacek and Yuhuai Wu and Henryk Michalewski and Piotr Miłoś}, year={2023}, eprint={2307.03170}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License For the LongLLaMA Code see [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf/blob/main/LICENSE) license. Some of the examples use external code (see headers of files for copyright notices and licenses). ## Acknowledgments We gratefully acknowledge the TPU Research Cloud program, which was instrumental to our research by providing significant computational resources. We are also grateful to Xinyang Geng and Hao Liu for releasing [OpenLLaMA](https://github.com/openlm-research/open_llama) checkpoints and the [EasyLM](https://github.com/young-geng/EasyLM) library. We would like to thank [Xiaosong,He](https://github.com/hxs91) for suggestions on how to improve the explanations of cross-batch code.