launch lighteval using lighteval --args (#152)

---------

Co-authored-by: Nathan Habib <[email protected]>
Co-authored-by: Clémentine Fourrier <[email protected]>

Author: 3 people

README.md

@@ -78,8 +78,8 @@ pre-commit install
 
 We provide two main entry points to evaluate models:
 
-* `run_evals_accelerate.py`: evaluate models on CPU or one or more GPUs using [🤗 Accelerate](https://github.com/huggingface/accelerate).
-* `run_evals_nanotron.py`: evaluate models in distributed settings using [⚡️ Nanotron](https://github.com/huggingface/nanotron).
+* `lighteval accelerate`: evaluate models on CPU or one or more GPUs using [🤗 Accelerate](https://github.com/huggingface/accelerate).
+* `lighteval nanotron`: evaluate models in distributed settings using [⚡️ Nanotron](https://github.com/huggingface/nanotron).
 
 For most users, we recommend using the 🤗 Accelerate backend - see below for specific commands.
 
@@ -94,7 +94,8 @@ accelerate config
 You can then evaluate a model using data parallelism as follows:
 
 ```shell
-accelerate launch --multi_gpu --num_processes=<num_gpus> run_evals_accelerate.py \
+accelerate launch --multi_gpu --num_processes=<num_gpus> -m \
+    lighteval accelerate \
     --model_args="pretrained=<path to model on the hub>" \
     --tasks <task parameters> \
     --output_dir output_dir
@@ -109,7 +110,8 @@ suite|task|num_few_shot|{0 or 1 to automatically reduce `num_few_shot` if prompt
 or a file path like [`examples/tasks/recommended_set.txt`](./examples/tasks/recommended_set.txt) which specifies multiple task configurations. For example, to evaluate GPT-2 on the Truthful QA benchmark run:
 
 ```shell
-accelerate launch --multi_gpu --num_processes=8 run_evals_accelerate.py \
+accelerate launch --multi_gpu --num_processes=8 -m \
+    lighteval accelerate \
     --model_args "pretrained=gpt2" \
     --tasks "lighteval|truthfulqa:mc|0|0" \
     --override_batch_size 1 \
@@ -119,7 +121,8 @@ accelerate launch --multi_gpu --num_processes=8 run_evals_accelerate.py \
 Here, `--override_batch_size` defines the _batch size per device_, so the effective batch size will be `override_batch_size x num_gpus`. To evaluate on multiple benchmarks, separate each task configuration with a comma, e.g.
 
 ```shell
-accelerate launch --multi_gpu --num_processes=8 run_evals_accelerate.py \
+accelerate launch --multi_gpu --num_processes=8 -m \
+    lighteval accelerate \
     --model_args "pretrained=gpt2" \
     --tasks "leaderboard|truthfulqa:mc|0|0,leaderboard|gsm8k|0|0" \
     --override_batch_size 1 \
@@ -133,7 +136,8 @@ See the [`examples/tasks/recommended_set.txt`](./examples/tasks/recommended_set.
 If you want to evaluate a model by spinning up inference endpoints, use adapter/delta weights, or more complex configuration options, you can load models using a configuration file. This is done as follows:
 
 ```shell
-accelerate launch --multi_gpu --num_processes=<num_gpus> run_evals_accelerate.py \
+accelerate launch --multi_gpu --num_processes=<num_gpus> -m \
+    lighteval accelerate \
     --model_config_path="<path to your model configuration>" \
     --tasks <task parameters> \
     --output_dir output_dir
@@ -147,13 +151,15 @@ To evaluate models larger that ~40B parameters in 16-bit precision, you will nee
 
 ```shell
 # PP=2, DP=4 - good for models < 70B params
-accelerate launch --multi_gpu --num_processes=4 run_evals_accelerate.py \
+accelerate launch --multi_gpu --num_processes=4 -m \
+    lighteval accelerate \
     --model_args="pretrained=<path to model on the hub>,model_parallel=True" \
     --tasks <task parameters> \
     --output_dir output_dir
 
 # PP=4, DP=2 - good for huge models >= 70B params
-accelerate launch --multi_gpu --num_processes=2 run_evals_accelerate.py \
+accelerate launch --multi_gpu --num_processes=2 -m \
+    lighteval accelerate \
     --model_args="pretrained=<path to model on the hub>,model_parallel=True" \
     --tasks <task parameters> \
     --output_dir output_dir
@@ -164,7 +170,8 @@ accelerate launch --multi_gpu --num_processes=2 run_evals_accelerate.py \
 To evaluate a model on all the benchmarks of the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) using a single node of 8 GPUs, run:
 
 ```shell
-accelerate launch --multi_gpu --num_processes=8 run_evals_accelerate.py \
+accelerate launch --multi_gpu --num_processes=8 -m \
+    lighteval accelerate \
     --model_args "pretrained=<model name>" \
     --tasks examples/tasks/open_llm_leaderboard_tasks.txt \
     --override_batch_size 1 \
@@ -176,7 +183,7 @@ accelerate launch --multi_gpu --num_processes=8 run_evals_accelerate.py \
 You can also use `lighteval` to evaluate models on CPU, although note this will typically be very slow for large models. To do so, run:
 
 ```shell
-python run_evals_accelerate.py \
+lighteval accelerate \
     --model_args="pretrained=<path to model on the hub>"\
     --tasks <task parameters> \
     --output_dir output_dir
@@ -211,7 +218,7 @@ Independently of the default tasks provided in `lighteval` that you will find in
 
 For example, to run an extended task like `ifeval`, you can run:
 ```shell
-python run_evals_accelerate.py \
+lighteval accelerate \
     --model_args "pretrained=HuggingFaceH4/zephyr-7b-beta" \
     --use_chat_template \ # optional, if you want to run the evaluation with the chat template
     --tasks "extended|ifeval|0|0" \
@@ -221,7 +228,7 @@ python run_evals_accelerate.py \
 To run a community or custom task, you can use (note the custom_tasks flag):
 
 ```shell
-python run_evals_accelerate.py \
+lighteval accelerate \
     --model_args="pretrained=<path to model on the hub>"\
     --tasks <task parameters> \
     --custom_tasks <path to your custom or community task> \
@@ -231,7 +238,7 @@ python run_evals_accelerate.py \
 For example, to launch `lighteval` on `arabic_mmlu:abstract_algebra` for `HuggingFaceH4/zephyr-7b-beta`, run:
 
 ```shell
-python run_evals_accelerate.py \
+lighteval accelerate \
     --model_args "pretrained=HuggingFaceH4/zephyr-7b-beta" \
     --use_chat_template \ # optional, if you want to run the evaluation with the chat template
     --tasks "community|arabic_mmlu:abstract_algebra|5|1" \
@@ -464,7 +471,7 @@ source <path_to_your_venv>/activate #or conda activate yourenv
 cd <path_to_your_lighteval>/lighteval
 
 export CUDA_LAUNCH_BLOCKING=1
-srun accelerate launch --multi_gpu --num_processes=8 run_evals_accelerate.py --model_args "pretrained=your model name" --tasks examples/tasks/open_llm_leaderboard_tasks.txt --override_batch_size 1 --save_details --output_dir=your output dir
+srun accelerate launch --multi_gpu --num_processes=8 -m lighteval accelerate --model_args "pretrained=your model name" --tasks examples/tasks/open_llm_leaderboard_tasks.txt --override_batch_size 1 --save_details --output_dir=your output dir
 ```
 
 ## Releases

examples/model_configs/endpoint_model.yaml

@@ -16,7 +16,7 @@ model:
     endpoint_type: "protected"
     namespace: null # The namespace under which to launch the endopint. Defaults to the current user's namespace
     image_url: null # Optionally specify the docker image to use when launching the endpoint model. E.g., launching models with later releases of the TGI container with support for newer models.
-    env_vars: 
+    env_vars:
       null # Optional environment variables to include when launching the endpoint. e.g., `MAX_INPUT_LENGTH: 2048`
   generation:
     add_special_tokens: true

examples/model_configs/tgi_model.yaml

@@ -3,4 +3,4 @@ model:
   instance:
     inference_server_address: ""
     inference_server_auth: null
-    model_id: null # Optional, only required if the TGI container was launched with model_id pointing to a local directory
+    model_id: null # Optional, only required if the TGI container was launched with model_id pointing to a local directory

pyproject.toml

@@ -102,4 +102,4 @@ Issues = "https://github.com/huggingface/lighteval/issues"
 # Changelog = "https://github.com/huggingface/lighteval/blob/master/CHANGELOG.md"
 
 [project.scripts]
-lighteval = "lighteval.commands.lighteval_cli:main"
+lighteval = "lighteval.__main__:cli_evaluate"

run_evals_accelerate.py

@@ -0,0 +1,65 @@
+#!/usr/bin/env python
+
+# MIT License
+
+# Copyright (c) 2024 Taratra D. RAHARISON and The HuggingFace Team
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import argparse
+
+from lighteval.parsers import parser_accelerate, parser_nanotron
+from lighteval.tasks.registry import Registry
+
+
+def cli_evaluate():
+    parser = argparse.ArgumentParser(description="CLI tool for lighteval, a lightweight framework for LLM evaluation")
+    subparsers = parser.add_subparsers(help="help for subcommand", dest="subcommand")
+
+    # create the parser for the "accelerate" command
+    parser_a = subparsers.add_parser("accelerate", help="use accelerate and transformers as backend for evaluation.")
+    parser_accelerate(parser_a)
+
+    # create the parser for the "nanotron" command
+    parser_b = subparsers.add_parser("nanotron", help="use nanotron as backend for evaluation.")
+    parser_nanotron(parser_b)
+
+    parser.add_argument("--list-tasks", action="store_true", help="List available tasks")
+
+    args = parser.parse_args()
+
+    if args.subcommand == "accelerate":
+        from lighteval.main_accelerate import main as main_accelerate
+
+        main_accelerate(args)
+        return
+
+    if args.subcommand == "nanotron":
+        from lighteval.main_nanotron import main as main_nanotron
+
+        main_nanotron(args.checkpoint_config_path, args.lighteval_override, args.cache_dir)
+        return
+
+    if args.list_tasks:
+        Registry(cache_dir="").print_all_tasks()
+        return
+
+
+if __name__ == "__main__":
+    cli_evaluate()