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| import os | |
| import pathlib | |
| import random | |
| import string | |
| import tempfile | |
| import time | |
| from concurrent.futures import ThreadPoolExecutor | |
| from typing import Iterable, List | |
| import gradio as gr | |
| import huggingface_hub | |
| import torch | |
| import yaml | |
| from gradio_logsview.logsview import Log, LogsView, LogsViewRunner | |
| from mergekit.config import MergeConfiguration | |
| from clean_community_org import garbage_collect_empty_models | |
| has_gpu = torch.cuda.is_available() | |
| # Running directly from Python doesn't work well with Gradio+run_process because of: | |
| # Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method | |
| # Let's use the CLI instead. | |
| # | |
| # import mergekit.merge | |
| # from mergekit.common import parse_kmb | |
| # from mergekit.options import MergeOptions | |
| # | |
| # merge_options = ( | |
| # MergeOptions( | |
| # copy_tokenizer=True, | |
| # cuda=True, | |
| # low_cpu_memory=True, | |
| # write_model_card=True, | |
| # ) | |
| # if has_gpu | |
| # else MergeOptions( | |
| # allow_crimes=True, | |
| # out_shard_size=parse_kmb("1B"), | |
| # lazy_unpickle=True, | |
| # write_model_card=True, | |
| # ) | |
| # ) | |
| cli = "mergekit-yaml config.yaml merge --copy-tokenizer" + ( | |
| " --cuda --low-cpu-memory" if has_gpu else " --allow-crimes --out-shard-size 1B --lazy-unpickle" | |
| ) | |
| MARKDOWN_DESCRIPTION = """ | |
| # mergekit-gui | |
| The fastest way to perform a model merge 🔥 | |
| Specify a YAML configuration file (see examples below) and a HF token and this app will perform the merge and upload the merged model to your user profile. | |
| """ | |
| MARKDOWN_ARTICLE = """ | |
| ___ | |
| ## Merge Configuration | |
| [Mergekit](https://github.com/arcee-ai/mergekit) configurations are YAML documents specifying the operations to perform in order to produce your merged model. | |
| Below are the primary elements of a configuration file: | |
| - `merge_method`: Specifies the method to use for merging models. See [Merge Methods](https://github.com/arcee-ai/mergekit#merge-methods) for a list. | |
| - `slices`: Defines slices of layers from different models to be used. This field is mutually exclusive with `models`. | |
| - `models`: Defines entire models to be used for merging. This field is mutually exclusive with `slices`. | |
| - `base_model`: Specifies the base model used in some merging methods. | |
| - `parameters`: Holds various parameters such as weights and densities, which can also be specified at different levels of the configuration. | |
| - `dtype`: Specifies the data type used for the merging operation. | |
| - `tokenizer_source`: Determines how to construct a tokenizer for the merged model. | |
| ## Merge Methods | |
| A quick overview of the currently supported merge methods: | |
| | Method | `merge_method` value | Multi-Model | Uses base model | | |
| | -------------------------------------------------------------------------------------------- | -------------------- | ----------- | --------------- | | |
| | Linear ([Model Soups](https://arxiv.org/abs/2203.05482)) | `linear` | ✅ | ❌ | | |
| | SLERP | `slerp` | ❌ | ✅ | | |
| | [Task Arithmetic](https://arxiv.org/abs/2212.04089) | `task_arithmetic` | ✅ | ✅ | | |
| | [TIES](https://arxiv.org/abs/2306.01708) | `ties` | ✅ | ✅ | | |
| | [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) | `dare_ties` | ✅ | ✅ | | |
| | [DARE](https://arxiv.org/abs/2311.03099) [Task Arithmetic](https://arxiv.org/abs/2212.04089) | `dare_linear` | ✅ | ✅ | | |
| | Passthrough | `passthrough` | ❌ | ❌ | | |
| | [Model Stock](https://arxiv.org/abs/2403.19522) | `model_stock` | ✅ | ✅ | | |
| ## Citation | |
| This GUI is powered by [Arcee's MergeKit](https://arxiv.org/abs/2403.13257). | |
| If you use it in your research, please cite the following paper: | |
| ``` | |
| @article{goddard2024arcee, | |
| title={Arcee's MergeKit: A Toolkit for Merging Large Language Models}, | |
| author={Goddard, Charles and Siriwardhana, Shamane and Ehghaghi, Malikeh and Meyers, Luke and Karpukhin, Vlad and Benedict, Brian and McQuade, Mark and Solawetz, Jacob}, | |
| journal={arXiv preprint arXiv:2403.13257}, | |
| year={2024} | |
| } | |
| ``` | |
| This Space is heavily inspired by LazyMergeKit by Maxime Labonne (see [Colab](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb)). | |
| """ | |
| examples = [[str(f)] for f in pathlib.Path("examples").glob("*.yml")] | |
| # Do not set community token as `HF_TOKEN` to avoid accidentally using it in merge scripts. | |
| # `COMMUNITY_HF_TOKEN` is used to upload models to the community organization (https://huggingface.co/mergekit-community) | |
| # when user do not provide a token. | |
| COMMUNITY_HF_TOKEN = os.getenv("COMMUNITY_HF_TOKEN") | |
| def merge(yaml_config: str, hf_token: str, repo_name: str) -> Iterable[List[Log]]: | |
| runner = LogsViewRunner() | |
| if not yaml_config: | |
| yield runner.log("Empty yaml, pick an example below", level="ERROR") | |
| return | |
| try: | |
| merge_config = MergeConfiguration.model_validate(yaml.safe_load(yaml_config)) | |
| except Exception as e: | |
| yield runner.log(f"Invalid yaml {e}", level="ERROR") | |
| return | |
| is_community_model = False | |
| if not hf_token: | |
| if "/" in repo_name and not repo_name.startswith("mergekit-community/"): | |
| yield runner.log( | |
| f"Cannot upload merge model to namespace {repo_name.split('/')[0]}: you must provide a valid token.", | |
| level="ERROR", | |
| ) | |
| return | |
| yield runner.log( | |
| "No HF token provided. Your merged model will be uploaded to the https://huggingface.co/mergekit-community organization." | |
| ) | |
| is_community_model = True | |
| if not COMMUNITY_HF_TOKEN: | |
| raise gr.Error("Cannot upload to community org: community token not set by Space owner.") | |
| hf_token = COMMUNITY_HF_TOKEN | |
| api = huggingface_hub.HfApi(token=hf_token) | |
| with tempfile.TemporaryDirectory(ignore_cleanup_errors=True) as tmpdirname: | |
| tmpdir = pathlib.Path(tmpdirname) | |
| merged_path = tmpdir / "merged" | |
| merged_path.mkdir(parents=True, exist_ok=True) | |
| config_path = merged_path / "config.yaml" | |
| config_path.write_text(yaml_config) | |
| yield runner.log(f"Merge configuration saved in {config_path}") | |
| if not repo_name: | |
| yield runner.log("No repo name provided. Generating a random one.") | |
| repo_name = f"mergekit-{merge_config.merge_method}" | |
| # Make repo_name "unique" (no need to be extra careful on uniqueness) | |
| repo_name += "-" + "".join(random.choices(string.ascii_lowercase, k=7)) | |
| repo_name = repo_name.replace("/", "-").strip("-") | |
| if is_community_model and not repo_name.startswith("mergekit-community/"): | |
| repo_name = f"mergekit-community/{repo_name}" | |
| try: | |
| yield runner.log(f"Creating repo {repo_name}") | |
| repo_url = api.create_repo(repo_name, exist_ok=True) | |
| yield runner.log(f"Repo created: {repo_url}") | |
| except Exception as e: | |
| yield runner.log(f"Error creating repo {e}", level="ERROR") | |
| return | |
| # Set tmp HF_HOME to avoid filling up disk Space | |
| tmp_env = os.environ.copy() # taken from https://stackoverflow.com/a/4453495 | |
| tmp_env["HF_HOME"] = f"{tmpdirname}/.cache" | |
| yield from runner.run_command(cli.split(), cwd=merged_path, env=tmp_env) | |
| if runner.exit_code != 0: | |
| yield runner.log("Merge failed. Deleting repo as no model is uploaded.", level="ERROR") | |
| api.delete_repo(repo_url.repo_id) | |
| return | |
| yield runner.log("Model merged successfully. Uploading to HF.") | |
| yield from runner.run_python( | |
| api.upload_folder, | |
| repo_id=repo_url.repo_id, | |
| folder_path=merged_path / "merge", | |
| ) | |
| yield runner.log(f"Model successfully uploaded to HF: {repo_url.repo_id}") | |
| with gr.Blocks() as demo: | |
| gr.Markdown(MARKDOWN_DESCRIPTION) | |
| with gr.Row(): | |
| filename = gr.Textbox(visible=False, label="filename") | |
| config = gr.Code(language="yaml", lines=10, label="config.yaml") | |
| with gr.Column(): | |
| token = gr.Textbox( | |
| lines=1, | |
| label="HF Write Token", | |
| info="https://hf.co/settings/token", | |
| type="password", | |
| placeholder="Optional. Will upload merged model to MergeKit Community if empty.", | |
| ) | |
| repo_name = gr.Textbox( | |
| lines=1, | |
| label="Repo name", | |
| placeholder="Optional. Will create a random name if empty.", | |
| ) | |
| button = gr.Button("Merge", variant="primary") | |
| logs = LogsView(label="Terminal output") | |
| gr.Examples( | |
| examples, | |
| fn=lambda s: (s,), | |
| run_on_click=True, | |
| label="Examples", | |
| inputs=[filename], | |
| outputs=[config], | |
| ) | |
| gr.Markdown(MARKDOWN_ARTICLE) | |
| button.click(fn=merge, inputs=[config, token, repo_name], outputs=[logs]) | |
| # Run garbage collection every hour to keep the community org clean. | |
| # Empty models might exists if the merge fails abruptly (e.g. if user leaves the Space). | |
| def _garbage_collect_every_hour(): | |
| while True: | |
| try: | |
| garbage_collect_empty_models(token=COMMUNITY_HF_TOKEN) | |
| except Exception as e: | |
| print("Error running garbage collection", e) | |
| time.sleep(3600) | |
| pool = ThreadPoolExecutor() | |
| pool.submit(_garbage_collect_every_hour) | |
| demo.queue(default_concurrency_limit=1).launch() | |