Lightning-AI/lm-evaluation-harness

Lightning-AI/lm-evaluation-harness

Description: A framework for few-shot evaluation of autoregressive language models.

Language: Python

License: MIT

Stars: 3

Forks: 0

Open issues: 0

Created: 2023-05-02T17:02:05Z

Pushed: 2023-06-05T15:01:54Z

Default branch: master

Fork: yes

Parent repository: EleutherAI/lm-evaluation-harness

Archived: yes

README:

Language Model Evaluation Harness

Overview

This project provides a unified framework to test generative language models on a large number of different evaluation tasks.

Features:

• 200+ tasks implemented. See the [task-table](./docs/task_table.md) for a complete list.
• Support for models loaded via transformers (including quantization via AutoGPTQ), GPT-NeoX, and Megatron-DeepSpeed, with a flexible tokenization-agnostic interface.
• Support for commercial APIs including OpenAI, goose.ai, and TextSynth.
• Support for evaluation on adapters (e.g. LoRa) supported in HuggingFace's PEFT library.
• Evaluating with publicly available prompts ensures reproducibility and comparability between papers.
• Task versioning to ensure reproducibility when tasks are updated.

Install

To install lm-eval from the github repository main branch, run:

git clone https://github.com/EleutherAI/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .

To install additional multilingual tokenization and text segmentation packages, you must install the package with the multilingual extra:

pip install -e ".[multilingual]"

To support loading GPTQ quantized models, install the package with the auto-gptq extra:

pip install -e ".[auto-gptq]"

Basic Usage

> Note: When reporting results from eval harness, please include the task versions (shown in results["versions"]) for reproducibility. This allows bug fixes to tasks while also ensuring that previously reported scores are reproducible. See the [Task Versioning](#task-versioning) section for more info.

Hugging Face transformers

To evaluate a model hosted on the HuggingFace Hub (e.g. GPT-J-6B) on hellaswag you can use the following command:

python main.py \
--model hf-causal \
--model_args pretrained=EleutherAI/gpt-j-6B \
--tasks hellaswag \
--device cuda:0

Additional arguments can be provided to the model constructor using the --model_args flag. Most notably, this supports the common practice of using the revisions feature on the Hub to store partially trained checkpoints, or to specify the datatype for running a model:

python main.py \
--model hf-causal \
--model_args pretrained=EleutherAI/pythia-160m,revision=step100000,dtype="float" \
--tasks lambada_openai,hellaswag \
--device cuda:0

To evaluate models that are loaded via AutoSeq2SeqLM in Huggingface, you instead use hf-seq2seq. *To evaluate (causal) models across multiple GPUs, use --model hf-causal-experimental*

> Warning: Choosing the wrong model may result in erroneous outputs despite not erroring.

Commercial APIs

Our library also supports language models served via the OpenAI API:

export OPENAI_API_SECRET_KEY=YOUR_KEY_HERE
python main.py \
--model gpt3 \
--model_args engine=davinci \
--tasks lambada_openai,hellaswag

While this functionality is only officially maintained for the official OpenAI API, it tends to also work for other hosting services that use the same API such as [goose.ai](goose.ai) with minor modification. We also have an implementation for the TextSynth API, using --model textsynth.

To verify the data integrity of the tasks you're performing in addition to running the tasks themselves, you can use the --check_integrity flag:

python main.py \
--model gpt3 \
--model_args engine=davinci \
--tasks lambada_openai,hellaswag \
--check_integrity

Other Frameworks

A number of other libraries contain scripts for calling the eval harness through their library. These include GPT-NeoX, Megatron-DeepSpeed, and mesh-transformer-jax.

💡 Tip: You can inspect what the LM inputs look like by running the following command:

python write_out.py \
--tasks all_tasks \
--num_fewshot 5 \
--num_examples 10 \
--output_base_path /path/to/output/folder

This will write out one text file for each task.

Advanced Usage

For models loaded with the HuggingFace transformers library, any arguments provided via --model_args get passed to the relevant constructor directly. This means that anything you can do with AutoModel can be done with our library. For example, you can pass a local path via pretrained= or use models finetuned with PEFT by taking the call you would run to evaluate the base model and add ,peft=PATH to the model_args argument:

python main.py \
--model hf-causal-experimental \
--model_args pretrained=EleutherAI/gpt-j-6b,peft=nomic-ai/gpt4all-j-lora \
--tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq \
--device cuda:0

GPTQ quantized models can be loaded by specifying their file names in ,quantized=NAME (or ,quantized=True for default names) in the model_args argument:

python main.py \
--model hf-causal-experimental \
--model_args pretrained=model-name-or-path,quantized=model.safetensors,gptq_use_triton=True \
--tasks hellaswag

We support wildcards in task names, for example you can run all of the machine-translated lambada tasks via --task lambada_openai_mt_*.

We currently only support one prompt per task, which we strive to make the "standard" as defined by the benchmark's authors. If you would like to study how varying prompts causes changes in the evaluation score, check out the BigScience fork of this repo. We are currently working on upstreaming this capability to main.

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