This section contains documentation related to changes/additions made to the lm-evaluation-harness. The original readme for the evaluation harness is in the subsequent section.
See eval_scripts
for example scripts. We will walk through a simple case:
Example command to evaluate Pythia-1.4b-deduped on the lila_addsub
task:
MODEL="EleutherAI/pythia-1.4b-deduped"
NAME="pythia-1.4b-deduped"
BASE_DIR="./"
OUTPUT_DIR="./output/lila"
mkdir -p ${OUTPUT_DIR}
FEWSHOT=5
BATCH_SIZE=1
TASKS="lila_addsub"
python ${BASE_DIR}/main.py --model_args pretrained=${MODEL} \
--description_dict_path ${BASE_DIR}/configs/config_lila.json \
--num_fewshot ${FEWSHOT} \
--model hf-causal \
--use_accelerate \
--accelerate_dtype float32 \
--tasks ${TASKS} \
--output_path ${OUTPUT_DIR}/${NAME}.json \
--batch_size ${BATCH_SIZE}
You can see a full example script at:
bash eval_scripts/eval_lila_accelerate.sh
NOTE: the --accelerate_dtype float32
flag is needed to match the performance of the non-accelerate
code.
NOTE: the --description_dict_path
flag provides a config file (configs/config_lila.json
) file.
See configs/config_math.json
for a non-trivial config file that enables a custom prompt and majority voting.
Additional generation options can be specified through a configuration file and the --description_dict_path
argument.
For example, to enable majority voting with temperature 0.3 on the math_algebra
task, we create a config.json
file containing a params
field:
{
"math_algebra": {
"params": {"majority_voting": 16, "sampling_temperature":0.5, "eval_batch_size":4},
}
}
then pass the file through the --description_dict_path
argument:
python main.py --model gpt2 \
--tasks math_algebra
--description_dict_path config.json
--device cuda
--num_fewshot 3
Warning: Currently only the tasks defined in hendrycks_math.py
support these options. If you are interested in adding this functionality to other tasks, see this guide.
In the config
file, you can add a description
field containing a string. The string will be prepended to each prompt during evaluation.
Continuing the example from above, we have a config.json
file containing:
{
"math_algebra": {
"params": {"majority_voting": 16, "sampling_temperature":0.5, "eval_batch_size":4},
"description": "You will solve a mathematical problem. Here are some examples:",
}
}
You can use the HuggingFace accelerate
library. To do so, use the --use_accelerate
flag along with a hf-causal
model.
Here is an example command:
python main.py \
--model hf-causal \
--use_accelerate \
--model_args pretrained=EleutherAI/pythia-2.8b-deduped \
--num_fewshot 5 \
--tasks lila_addsub
NOTE: With default settings, --model hf-causal
may have different performance than --model gpt2
. One known discrepancy is that hf-causal
may use float16
by default, while --model gpt2
uses float32
. Add the command-line argument --accelerate_dtype float32
to prevent this discrepancy.
NOTE: we do not yet support hf-seq2seq
.
The ProofNet informalization task (proofnet_informalize_statements
) consists of mapping a formal theorem statement to an informal statement.
We provide an experiment GPT-based evaluation as a proxy of correctness.
Given a (formal theorem statement, gold informal statement, generated informal statement)
triple, a gpt-3.5-turbo
or gpt-4
model is prompted to decide whether the generated informal statement is correct, and to provide a reason for the decision.
To enable:
- Set your openai api key as an environment variable:
export OPENAI_API_KEY="..."
- Enable it in the config file (e.g.
configs/config_proofnet.json
):
{
"proofnet_autoformalize_statements" : {
"description": "",
"params": {}
},
"proofnet_informalize_statements" : {
"description": "",
"params": {
"gpt_eval" : {
"enabled": true,
"settings": {
"engine": "gpt-3.5-turbo",
"max_tokens": 512
}
}
}
}
}
A full example script is in eval_scripts/eval_proofnet_accelerate.sh
.
- This evaluation costs money, since it calls the openai api.
- This evaluation is not fully reproducible since it depends on the openai api.
- This evaluation has not been extensively validated as an evaluation methodology. We hypothesize that it may serve as a proxy of correctness that is useful for relative comparison of models.
This project provides a unified framework to test autoregressive language models (GPT-2, GPT-3, GPTNeo, etc) on a large number of different evaluation tasks.
Features:
- 200+ tasks implemented. See the task-table for a complete list.
- Support for GPT-2, GPT-3, GPT-Neo, GPT-NeoX, and GPT-J, with flexible tokenization-agnostic interface.
- Task versioning to ensure reproducibility.
pip install lm-eval
To install additional multlingual tokenization and text segmenation packages, you must install the package with the multilingual
extra:
pip install "lm-eval[multilingual]"
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 section for more info.
To evaluate a model hosted on the HuggingFace Hub (e.g. GPT-J-6B) you can use the following command:
python main.py \
--model hf-causal \
--model_args pretrained=EleutherAI/gpt-j-6B \
--tasks lambada_openai,hellaswag \
--device 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 partialy trained checkpoints:
python main.py \
--model hf-causal \
--model_args pretrained=EleutherAI/pythia-160m,revision=step100000 \
--tasks lambada_openai,hellaswag \
--device 0
To evaluate models that are called via AutoSeq2SeqLM
, you instead use hf-seq2seq
.
Warning: Choosing the wrong model may result in erroneous outputs despite not erroring.
Our library also supports 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 mantained for the official OpenAI API, it tends to also work for other hosting services that use the same API such as 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
To evaluate mesh-transformer-jax models that are not available on HF, please invoke eval harness through this script.
💡 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.
To implement a new task in the eval harness, see this guide.
To help improve reproducibility, all tasks have a VERSION
field. When run from the command line, this is reported in a column in the table, or in the "version" field in the evaluator return dict. The purpose of the version is so that if the task definition changes (i.e to fix a bug), then we can know exactly which metrics were computed using the old buggy implementation to avoid unfair comparisons. To enforce this, there are unit tests that make sure the behavior of all tests remains the same as when they were first implemented. Task versions start at 0, and each time a breaking change is made, the version is incremented by one.
When reporting eval harness results, please also report the version of each task. This can be done either with a separate column in the table, or by reporting the task name with the version appended as such: taskname-v0.
For details on text decontamination, see the decontamination guide.
Note that the directory provided to the --decontamination_ngrams_path
argument should contain the ngram files and info.json. See the above guide for ngram generation for the pile, this could be adapted for other training sets.
python main.py \
--model gpt2 \
--tasks sciq \
--decontamination_ngrams_path path/containing/training/set/ngrams \
--device 0
@software{eval-harness,
author = {Gao, Leo and
Tow, Jonathan and
Biderman, Stella and
Black, Sid and
DiPofi, Anthony and
Foster, Charles and
Golding, Laurence and
Hsu, Jeffrey and
McDonell, Kyle and
Muennighoff, Niklas and
Phang, Jason and
Reynolds, Laria and
Tang, Eric and
Thite, Anish and
Wang, Ben and
Wang, Kevin and
Zou, Andy},
title = {A framework for few-shot language model evaluation},
month = sep,
year = 2021,
publisher = {Zenodo},
version = {v0.0.1},
doi = {10.5281/zenodo.5371628},
url = {https://doi.org/10.5281/zenodo.5371628}
}