Clémentine
added more nuance in ft models
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import json
import os
from datetime import datetime, timezone
import gradio as gr
import numpy as np
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import HfApi
from transformers import AutoConfig
from src.auto_leaderboard.get_model_metadata import apply_metadata
from src.assets.text_content import *
from src.auto_leaderboard.load_results import get_eval_results_dicts, make_clickable_model
from src.assets.hardcoded_evals import gpt4_values, gpt35_values, baseline
from src.assets.css_html_js import custom_css, get_window_url_params
from src.utils_display import AutoEvalColumn, EvalQueueColumn, fields, styled_error, styled_warning, styled_message
from src.init import get_all_requested_models, load_all_info_from_hub
# clone / pull the lmeh eval data
H4_TOKEN = os.environ.get("H4_TOKEN", None)
QUEUE_REPO = "open-llm-leaderboard/requests"
RESULTS_REPO = "open-llm-leaderboard/results"
PRIVATE_QUEUE_REPO = "open-llm-leaderboard/private-requests"
PRIVATE_RESULTS_REPO = "open-llm-leaderboard/private-results"
IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", True))
EVAL_REQUESTS_PATH = "eval-queue"
EVAL_RESULTS_PATH = "eval-results"
EVAL_REQUESTS_PATH_PRIVATE = "eval-queue-private"
EVAL_RESULTS_PATH_PRIVATE = "eval-results-private"
api = HfApi()
def restart_space():
api.restart_space(
repo_id="HuggingFaceH4/open_llm_leaderboard", token=H4_TOKEN
)
eval_queue, requested_models, eval_results = load_all_info_from_hub(QUEUE_REPO, RESULTS_REPO, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH)
if not IS_PUBLIC:
eval_queue_private, requested_models_private, eval_results_private = load_all_info_from_hub(PRIVATE_QUEUE_REPO, PRIVATE_RESULTS_REPO, EVAL_REQUESTS_PATH_PRIVATE, EVAL_RESULTS_PATH_PRIVATE)
else:
eval_queue_private, eval_results_private = None, None
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
COLS_LITE = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
TYPES_LITE = [c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
if not IS_PUBLIC:
COLS.insert(2, AutoEvalColumn.precision.name)
TYPES.insert(2, AutoEvalColumn.precision.type)
EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
BENCHMARK_COLS = [c.name for c in [AutoEvalColumn.arc, AutoEvalColumn.hellaswag, AutoEvalColumn.mmlu, AutoEvalColumn.truthfulqa]]
def has_no_nan_values(df, columns):
return df[columns].notna().all(axis=1)
def has_nan_values(df, columns):
return df[columns].isna().any(axis=1)
def get_leaderboard_df():
if eval_results:
print("Pulling evaluation results for the leaderboard.")
eval_results.git_pull()
if eval_results_private:
print("Pulling evaluation results for the leaderboard.")
eval_results_private.git_pull()
all_data = get_eval_results_dicts(IS_PUBLIC)
if not IS_PUBLIC:
all_data.append(gpt4_values)
all_data.append(gpt35_values)
all_data.append(baseline)
apply_metadata(all_data) # Populate model type based on known hardcoded values in `metadata.py`
df = pd.DataFrame.from_records(all_data)
df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
df = df[COLS]
# filter out if any of the benchmarks have not been produced
df = df[has_no_nan_values(df, BENCHMARK_COLS)]
return df
def get_evaluation_queue_df():
if eval_queue:
print("Pulling changes for the evaluation queue.")
eval_queue.git_pull()
if eval_queue_private:
print("Pulling changes for the evaluation queue.")
eval_queue_private.git_pull()
entries = [
entry
for entry in os.listdir(EVAL_REQUESTS_PATH)
if not entry.startswith(".")
]
all_evals = []
for entry in entries:
if ".json" in entry:
file_path = os.path.join(EVAL_REQUESTS_PATH, entry)
with open(file_path) as fp:
data = json.load(fp)
data["# params"] = "unknown"
data["model"] = make_clickable_model(data["model"])
data["revision"] = data.get("revision", "main")
all_evals.append(data)
elif ".md" not in entry:
# this is a folder
sub_entries = [
e
for e in os.listdir(f"{EVAL_REQUESTS_PATH}/{entry}")
if not e.startswith(".")
]
for sub_entry in sub_entries:
file_path = os.path.join(EVAL_REQUESTS_PATH, entry, sub_entry)
with open(file_path) as fp:
data = json.load(fp)
# data["# params"] = get_n_params(data["model"])
data["model"] = make_clickable_model(data["model"])
all_evals.append(data)
pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]]
running_list = [e for e in all_evals if e["status"] == "RUNNING"]
finished_list = [e for e in all_evals if e["status"].startswith("FINISHED")]
df_pending = pd.DataFrame.from_records(pending_list, columns=EVAL_COLS)
df_running = pd.DataFrame.from_records(running_list, columns=EVAL_COLS)
df_finished = pd.DataFrame.from_records(finished_list, columns=EVAL_COLS)
return df_finished[EVAL_COLS], df_running[EVAL_COLS], df_pending[EVAL_COLS]
original_df = get_leaderboard_df()
leaderboard_df = original_df.copy()
(
finished_eval_queue_df,
running_eval_queue_df,
pending_eval_queue_df,
) = get_evaluation_queue_df()
def is_model_on_hub(model_name, revision) -> bool:
try:
AutoConfig.from_pretrained(model_name, revision=revision)
return True, None
except ValueError as e:
return False, "needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard."
except Exception as e:
print(f"Could not get the model config from the hub.: {e}")
return False, "was not found on hub!"
def add_new_eval(
model: str,
base_model: str,
revision: str,
precision: str,
private: bool,
weight_type: str,
model_type: str,
):
precision = precision.split(" ")[0]
current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
if model_type is None or model_type == "":
return styled_error("Please select a model type.")
# check the model actually exists before adding the eval
if revision == "":
revision = "main"
if weight_type in ["Delta", "Adapter"]:
base_model_on_hub, error = is_model_on_hub(base_model, revision)
if not base_model_on_hub:
return styled_error(f'Base model "{base_model}" {error}')
if not weight_type == "Adapter":
model_on_hub, error = is_model_on_hub(model, revision)
if not model_on_hub:
return styled_error(f'Model "{model}" {error}')
print("adding new eval")
eval_entry = {
"model": model,
"base_model": base_model,
"revision": revision,
"private": private,
"precision": precision,
"weight_type": weight_type,
"status": "PENDING",
"submitted_time": current_time,
"model_type": model_type,
}
user_name = ""
model_path = model
if "/" in model:
user_name = model.split("/")[0]
model_path = model.split("/")[1]
OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
os.makedirs(OUT_DIR, exist_ok=True)
out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{precision}_{weight_type}.json"
# Check for duplicate submission
if out_path.split("eval-queue/")[1].lower() in requested_models:
return styled_warning("This model has been already submitted.")
with open(out_path, "w") as f:
f.write(json.dumps(eval_entry))
api.upload_file(
path_or_fileobj=out_path,
path_in_repo=out_path.split("eval-queue/")[1],
repo_id=QUEUE_REPO,
token=H4_TOKEN,
repo_type="dataset",
commit_message=f"Add {model} to eval queue",
)
# remove the local file
os.remove(out_path)
return styled_message("Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list.")
def refresh():
leaderboard_df = get_leaderboard_df()
(
finished_eval_queue_df,
running_eval_queue_df,
pending_eval_queue_df,
) = get_evaluation_queue_df()
return (
leaderboard_df,
finished_eval_queue_df,
running_eval_queue_df,
pending_eval_queue_df,
)
def search_table(df, leaderboard_table, query):
if AutoEvalColumn.model_type.name in leaderboard_table.columns:
filtered_df = df[
(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))
| (df[AutoEvalColumn.model_type.name].str.contains(query, case=False))
]
else:
filtered_df = df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))]
return filtered_df[leaderboard_table.columns]
def select_columns(df, columns):
always_here_cols = [AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name]
# We use COLS to maintain sorting
filtered_df = df[always_here_cols + [c for c in COLS if c in df.columns and c in columns] + [AutoEvalColumn.dummy.name]]
return filtered_df
#TODO allow this to filter by values of any columns
def filter_items(df, leaderboard_table, query):
if query == "all":
return df[leaderboard_table.columns]
else:
query = query[0] #take only the emoji character
if AutoEvalColumn.model_type_symbol.name in leaderboard_table.columns:
filtered_df = df[(df[AutoEvalColumn.model_type_symbol.name] == query)]
else:
return leaderboard_table.columns
return filtered_df[leaderboard_table.columns]
def change_tab(query_param):
query_param = query_param.replace("'", '"')
query_param = json.loads(query_param)
if (
isinstance(query_param, dict)
and "tab" in query_param
and query_param["tab"] == "evaluation"
):
return gr.Tabs.update(selected=1)
else:
return gr.Tabs.update(selected=0)
demo = gr.Blocks(css=custom_css)
with demo:
gr.HTML(TITLE)
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
with gr.Tabs(elem_classes="tab-buttons") as tabs:
with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
with gr.Row():
shown_columns = gr.CheckboxGroup(
choices = [c for c in COLS if c not in [AutoEvalColumn.dummy.name, AutoEvalColumn.model.name, AutoEvalColumn.model_type_symbol.name]],
value = [c for c in COLS_LITE if c not in [AutoEvalColumn.dummy.name, AutoEvalColumn.model.name, AutoEvalColumn.model_type_symbol.name]],
label="Select columns to show",
elem_id="column-select",
interactive=True,
)
with gr.Column(min_width=320):
search_bar = gr.Textbox(
placeholder="🔍 Search for your model and press ENTER...",
show_label=False,
elem_id="search-bar",
)
filter_columns = gr.Radio(
label="⏚ Filter model types",
choices = [
"all",
ModelType.PT.to_str(),
ModelType.FT.to_str(),
ModelType.IFT.to_str(),
ModelType.RL.to_str(),
],
value="all",
elem_id="filter-columns"
)
leaderboard_table = gr.components.Dataframe(
value=leaderboard_df[[AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name] + shown_columns.value+ [AutoEvalColumn.dummy.name]],
headers=[AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name] + shown_columns.value + [AutoEvalColumn.dummy.name],
datatype=TYPES,
max_rows=None,
elem_id="leaderboard-table",
interactive=False,
visible=True,
)
# Dummy leaderboard for handling the case when the user uses backspace key
hidden_leaderboard_table_for_search = gr.components.Dataframe(
value=original_df,
headers=COLS,
datatype=TYPES,
max_rows=None,
visible=False,
)
search_bar.submit(
search_table,
[hidden_leaderboard_table_for_search, leaderboard_table, search_bar],
leaderboard_table,
)
shown_columns.change(select_columns, [hidden_leaderboard_table_for_search, shown_columns], leaderboard_table)
filter_columns.change(filter_items, [hidden_leaderboard_table_for_search, leaderboard_table, filter_columns], leaderboard_table)
with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
with gr.Column():
with gr.Row():
gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
with gr.Column():
with gr.Accordion(f"✅ Finished Evaluations ({len(finished_eval_queue_df)})", open=False):
with gr.Row():
finished_eval_table = gr.components.Dataframe(
value=finished_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
max_rows=5,
)
with gr.Accordion(f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})", open=False):
with gr.Row():
running_eval_table = gr.components.Dataframe(
value=running_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
max_rows=5,
)
with gr.Accordion(f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})", open=False):
with gr.Row():
pending_eval_table = gr.components.Dataframe(
value=pending_eval_queue_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
max_rows=5,
)
with gr.Row():
gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
with gr.Row():
with gr.Column():
model_name_textbox = gr.Textbox(label="Model name")
revision_name_textbox = gr.Textbox(
label="revision", placeholder="main"
)
private = gr.Checkbox(
False, label="Private", visible=not IS_PUBLIC
)
model_type = gr.Dropdown(
choices=[
ModelType.PT.to_str(" : "),
ModelType.FT.to_str(" : "),
ModelType.IFT.to_str(" : "),
ModelType.RL.to_str(" : "),
],
label="Model type",
multiselect=False,
value=None,
interactive=True,
)
with gr.Column():
precision = gr.Dropdown(
choices=["float16", "bfloat16", "8bit (LLM.int8)", "4bit (QLoRA / FP4)"],
label="Precision",
multiselect=False,
value="float16",
interactive=True,
)
weight_type = gr.Dropdown(
choices=["Original", "Delta", "Adapter"],
label="Weights type",
multiselect=False,
value="Original",
interactive=True,
)
base_model_name_textbox = gr.Textbox(
label="Base model (for delta or adapter weights)"
)
submit_button = gr.Button("Submit Eval")
submission_result = gr.Markdown()
submit_button.click(
add_new_eval,
[
model_name_textbox,
base_model_name_textbox,
revision_name_textbox,
precision,
private,
weight_type,
model_type
],
submission_result,
)
with gr.Row():
refresh_button = gr.Button("Refresh")
refresh_button.click(
refresh,
inputs=[],
outputs=[
leaderboard_table,
finished_eval_table,
running_eval_table,
pending_eval_table,
],
)
with gr.Row():
with gr.Accordion("📙 Citation", open=False):
citation_button = gr.Textbox(
value=CITATION_BUTTON_TEXT,
label=CITATION_BUTTON_LABEL,
elem_id="citation-button",
).style(show_copy_button=True)
dummy = gr.Textbox(visible=False)
demo.load(
change_tab,
dummy,
tabs,
_js=get_window_url_params,
)
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=3600)
scheduler.start()
demo.queue(concurrency_count=40).launch()