backend / app.py
sheonhan's picture
Update tab button
b5f5045
raw
history blame
20.9 kB
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, Repository
from transformers import AutoConfig
from content import *
from elo_utils import get_elo_plots, get_elo_results_dicts
from utils import get_eval_results_dicts, make_clickable_model
# clone / pull the lmeh eval data
H4_TOKEN = os.environ.get("H4_TOKEN", None)
LMEH_REPO = "HuggingFaceH4/lmeh_evaluations"
HUMAN_EVAL_REPO = "HuggingFaceH4/scale-human-eval"
GPT_4_EVAL_REPO = "HuggingFaceH4/open_llm_leaderboard_oai_evals"
IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", None))
api = HfApi()
def restart_space():
api.restart_space(repo_id="HuggingFaceH4/open_llm_leaderboard", token=H4_TOKEN)
def get_all_requested_models(requested_models_dir):
depth = 1
file_names = []
for root, dirs, files in os.walk(requested_models_dir):
current_depth = root.count(os.sep) - requested_models_dir.count(os.sep)
if current_depth == depth:
file_names.extend([os.path.join(root, file) for file in files])
return set([file_name.lower().split("./evals/")[1] for file_name in file_names])
repo = None
requested_models = None
if H4_TOKEN:
print("Pulling evaluation requests and results.")
# try:
# shutil.rmtree("./evals/")
# except:
# pass
repo = Repository(
local_dir="./evals/",
clone_from=LMEH_REPO,
use_auth_token=H4_TOKEN,
repo_type="dataset",
)
repo.git_pull()
requested_models_dir = "./evals/eval_requests"
requested_models = get_all_requested_models(requested_models_dir)
human_eval_repo = None
if H4_TOKEN and not os.path.isdir("./human_evals"):
print("Pulling human evaluation repo")
human_eval_repo = Repository(
local_dir="./human_evals/",
clone_from=HUMAN_EVAL_REPO,
use_auth_token=H4_TOKEN,
repo_type="dataset",
)
human_eval_repo.git_pull()
gpt_4_eval_repo = None
if H4_TOKEN and not os.path.isdir("./gpt_4_evals"):
print("Pulling GPT-4 evaluation repo")
gpt_4_eval_repo = Repository(
local_dir="./gpt_4_evals/",
clone_from=GPT_4_EVAL_REPO,
use_auth_token=H4_TOKEN,
repo_type="dataset",
)
gpt_4_eval_repo.git_pull()
# parse the results
BENCHMARKS = ["arc_challenge", "hellaswag", "hendrycks", "truthfulqa_mc"]
METRICS = ["acc_norm", "acc_norm", "acc_norm", "mc2"]
def load_results(model, benchmark, metric):
file_path = os.path.join("evals", model, f"{model}-eval_{benchmark}.json")
if not os.path.exists(file_path):
return 0.0, None
with open(file_path) as fp:
data = json.load(fp)
accs = np.array([v[metric] for k, v in data["results"].items()])
mean_acc = np.mean(accs)
return mean_acc, data["config"]["model_args"]
COLS = [
"Model",
"Revision",
"Average ⬆️",
"ARC (25-shot) ⬆️",
"HellaSwag (10-shot) ⬆️",
"MMLU (5-shot) ⬆️",
"TruthfulQA (0-shot) ⬆️",
"model_name_for_query", # dummy column to implement search bar (hidden by custom CSS)
]
TYPES = ["markdown", "str", "number", "number", "number", "number", "number", "str"]
if not IS_PUBLIC:
COLS.insert(2, "8bit")
TYPES.insert(2, "bool")
EVAL_COLS = ["model", "revision", "private", "8bit_eval", "is_delta_weight", "status"]
EVAL_TYPES = ["markdown", "str", "bool", "bool", "bool", "str"]
BENCHMARK_COLS = [
"ARC (25-shot) ⬆️",
"HellaSwag (10-shot) ⬆️",
"MMLU (5-shot) ⬆️",
"TruthfulQA (0-shot) ⬆️",
]
ELO_COLS = [
"Model",
"GPT-4 (all)",
"Human (all)",
"Human (instruct)",
"Human (code-instruct)",
]
ELO_TYPES = ["markdown", "number", "number", "number", "number"]
ELO_SORT_COL = "GPT-4 (all)"
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 repo:
print("Pulling evaluation results for the leaderboard.")
repo.git_pull()
all_data = get_eval_results_dicts(IS_PUBLIC)
if not IS_PUBLIC:
gpt4_values = {
"Model": f'<a target="_blank" href=https://arxiv.org/abs/2303.08774 style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">gpt4</a>',
"Revision": "tech report",
"8bit": None,
"Average ⬆️": 84.3,
"ARC (25-shot) ⬆️": 96.3,
"HellaSwag (10-shot) ⬆️": 95.3,
"MMLU (5-shot) ⬆️": 86.4,
"TruthfulQA (0-shot) ⬆️": 59.0,
"model_name_for_query": "GPT-4",
}
all_data.append(gpt4_values)
gpt35_values = {
"Model": f'<a target="_blank" href=https://arxiv.org/abs/2303.08774 style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">gpt3.5</a>',
"Revision": "tech report",
"8bit": None,
"Average ⬆️": 71.9,
"ARC (25-shot) ⬆️": 85.2,
"HellaSwag (10-shot) ⬆️": 85.5,
"MMLU (5-shot) ⬆️": 70.0,
"TruthfulQA (0-shot) ⬆️": 47.0,
"model_name_for_query": "GPT-3.5",
}
all_data.append(gpt35_values)
base_line = {
"Model": "<p>Baseline</p>",
"Revision": "N/A",
"8bit": None,
"Average ⬆️": 25.0,
"ARC (25-shot) ⬆️": 25.0,
"HellaSwag (10-shot) ⬆️": 25.0,
"MMLU (5-shot) ⬆️": 25.0,
"TruthfulQA (0-shot) ⬆️": 25.0,
"model_name_for_query": "baseline",
}
all_data.append(base_line)
df = pd.DataFrame.from_records(all_data)
df = df.sort_values(by=["Average ⬆️"], 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 repo:
print("Pulling changes for the evaluation queue.")
repo.git_pull()
entries = [
entry
for entry in os.listdir("evals/eval_requests")
if not entry.startswith(".")
]
all_evals = []
for entry in entries:
if ".json" in entry:
file_path = os.path.join("evals/eval_requests", 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)
else:
# this is a folder
sub_entries = [
e
for e in os.listdir(f"evals/eval_requests/{entry}")
if not e.startswith(".")
]
for sub_entry in sub_entries:
file_path = os.path.join("evals/eval_requests", 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"] == "PENDING"]
running_list = [e for e in all_evals if e["status"] == "RUNNING"]
finished_list = [e for e in all_evals if e["status"] == "FINISHED"]
df_pending = pd.DataFrame.from_records(pending_list)
df_running = pd.DataFrame.from_records(running_list)
df_finished = pd.DataFrame.from_records(finished_list)
return df_finished[EVAL_COLS], df_running[EVAL_COLS], df_pending[EVAL_COLS]
def get_elo_leaderboard(df_instruct, df_code_instruct, tie_allowed=False):
if human_eval_repo:
print("Pulling human_eval_repo changes")
human_eval_repo.git_pull()
all_data = get_elo_results_dicts(df_instruct, df_code_instruct, tie_allowed)
dataframe = pd.DataFrame.from_records(all_data)
dataframe = dataframe.sort_values(by=ELO_SORT_COL, ascending=False)
dataframe = dataframe[ELO_COLS]
return dataframe
def get_elo_elements():
df_instruct = pd.read_json("human_evals/without_code.json")
df_code_instruct = pd.read_json("human_evals/with_code.json")
elo_leaderboard = get_elo_leaderboard(
df_instruct, df_code_instruct, tie_allowed=False
)
elo_leaderboard_with_tie_allowed = get_elo_leaderboard(
df_instruct, df_code_instruct, tie_allowed=True
)
plot_1, plot_2, plot_3, plot_4 = get_elo_plots(
df_instruct, df_code_instruct, tie_allowed=False
)
return (
elo_leaderboard,
elo_leaderboard_with_tie_allowed,
plot_1,
plot_2,
plot_3,
plot_4,
)
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()
(
elo_leaderboard,
elo_leaderboard_with_tie_allowed,
plot_1,
plot_2,
plot_3,
plot_4,
) = get_elo_elements()
def is_model_on_hub(model_name, revision) -> bool:
try:
config = AutoConfig.from_pretrained(model_name, revision=revision)
return True
except Exception as e:
print("Could not get the model config from the hub.")
print(e)
return False
def add_new_eval(
model: str,
base_model: str,
revision: str,
is_8_bit_eval: bool,
private: bool,
is_delta_weight: bool,
):
current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
# check the model actually exists before adding the eval
if revision == "":
revision = "main"
if is_delta_weight and not is_model_on_hub(base_model, revision):
error_message = f'Base model "{base_model}" was not found on hub!'
print(error_message)
return f"<p style='color: red; font-size: 20px; text-align: center;'>{error_message}</p>"
if not is_model_on_hub(model, revision):
error_message = f'Model "{model}"was not found on hub!'
return f"<p style='color: red; font-size: 20px; text-align: center;'>{error_message}</p>"
print("adding new eval")
eval_entry = {
"model": model,
"base_model": base_model,
"revision": revision,
"private": private,
"8bit_eval": is_8_bit_eval,
"is_delta_weight": is_delta_weight,
"status": "PENDING",
"submitted_time": current_time,
}
user_name = ""
model_path = model
if "/" in model:
user_name = model.split("/")[0]
model_path = model.split("/")[1]
OUT_DIR = f"eval_requests/{user_name}"
os.makedirs(OUT_DIR, exist_ok=True)
out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{is_8_bit_eval}_{is_delta_weight}.json"
# Check for duplicate submission
if out_path.lower() in requested_models:
duplicate_request_message = "This model has been already submitted."
return f"<p style='color: orange; font-size: 20px; text-align: center;'>{duplicate_request_message}</p>"
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,
repo_id=LMEH_REPO,
token=H4_TOKEN,
repo_type="dataset",
)
success_message = "Your request has been submitted to the evaluation queue!"
return f"<p style='color: green; font-size: 20px; text-align: center;'>{success_message}</p>"
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, query):
filtered_df = df[df["model_name_for_query"].str.contains(query, case=False)]
return filtered_df
custom_css = """
#changelog-text {
font-size: 16px !important;
}
#changelog-text h2 {
font-size: 18px !important;
}
.markdown-text {
font-size: 16px !important;
}
#citation-button span {
font-size: 16px !important;
}
#citation-button textarea {
font-size: 16px !important;
}
#citation-button > label > button {
margin: 6px;
transform: scale(1.3);
}
#leaderboard-table {
margin-top: 15px
}
#search-bar-table-box > div:first-child {
background: none;
border: none;
}
#search-bar {
padding: 0px;
width: 30%;
}
/* Hides the final column */
#llm-benchmark-tab-table table td:last-child,
#llm-benchmark-tab-table table th:last-child {
display: none;
}
/* Limit the width of the first column so that names don't expand too much */
table td:first-child,
table th:first-child {
max-width: 400px;
overflow: auto;
white-space: nowrap;
}
.tab-buttons button {
font-size: 16px;
}
#scale-logo {
border-style: none !important;
box-shadow: none;
display: block;
margin-left: auto;
margin-right: auto;
max-width: 600px;
}
#scale-logo .download {
display: none;
}
"""
demo = gr.Blocks(css=custom_css)
with demo:
gr.HTML(TITLE)
with gr.Row():
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
with gr.Row():
with gr.Column():
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)
with gr.Column():
with gr.Accordion("✨ CHANGELOG", open=False):
changelog = gr.Markdown(CHANGELOG_TEXT, elem_id="changelog-text")
with gr.Tabs(elem_classes="tab-buttons"):
with gr.TabItem("📊 LLM Benchmarks", elem_id="llm-benchmark-tab-table"):
with gr.Column():
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
with gr.Box(elem_id="search-bar-table-box"):
search_bar = gr.Textbox(
placeholder="🔍 Search your model and press ENTER...",
show_label=False,
elem_id="search-bar",
)
leaderboard_table = gr.components.Dataframe(
value=leaderboard_df,
headers=COLS,
datatype=TYPES,
max_rows=5,
elem_id="leaderboard-table",
)
# 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=5,
visible=False,
)
search_bar.submit(
search_table,
[hidden_leaderboard_table_for_search, search_bar],
leaderboard_table,
)
with gr.Row():
gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
with gr.Accordion("✅ Finished Evaluations", 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("🔄 Running Evaluation Queue", 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("⏳ Pending Evaluation Queue", 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():
refresh_button = gr.Button("Refresh")
refresh_button.click(
refresh,
inputs=[],
outputs=[
leaderboard_table,
finished_eval_table,
running_eval_table,
pending_eval_table,
],
)
with gr.Accordion("Submit a new model for evaluation"):
with gr.Row():
with gr.Column():
model_name_textbox = gr.Textbox(label="Model name")
revision_name_textbox = gr.Textbox(
label="revision", placeholder="main"
)
with gr.Column():
is_8bit_toggle = gr.Checkbox(
False, label="8 bit eval", visible=not IS_PUBLIC
)
private = gr.Checkbox(
False, label="Private", visible=not IS_PUBLIC
)
is_delta_weight = gr.Checkbox(False, label="Delta weights")
base_model_name_textbox = gr.Textbox(
label="base model (for delta)"
)
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,
is_8bit_toggle,
private,
is_delta_weight,
],
submission_result,
)
with gr.TabItem(
"🧑‍⚖️ Human & GPT-4 Evaluations 🤖", elem_id="human-gpt-tab-table"
):
with gr.Row():
with gr.Column(scale=2):
gr.Markdown(HUMAN_GPT_EVAL_TEXT, elem_classes="markdown-text")
with gr.Column(scale=1):
gr.Image(
"scale-hf-logo.png", elem_id="scale-logo", show_label=False
)
gr.Markdown("## No tie")
elo_leaderboard_table = gr.components.Dataframe(
value=elo_leaderboard,
headers=ELO_COLS,
datatype=ELO_TYPES,
max_rows=5,
)
gr.Markdown("## Tie allowed*")
elo_leaderboard_table_with_tie_allowed = gr.components.Dataframe(
value=elo_leaderboard_with_tie_allowed,
headers=ELO_COLS,
datatype=ELO_TYPES,
max_rows=5,
)
gr.Markdown("\* Results when the scores of 4 and 5 were treated as ties.", elem_classes="markdown-text")
# with gr.Box():
# visualization_title = gr.HTML(VISUALIZATION_TITLE)
# with gr.Row():
# with gr.Column():
# gr.Markdown(f"#### Figure 1: {PLOT_1_TITLE}")
# plot_1 = gr.Plot(plot_1, show_label=False)
# with gr.Column():
# gr.Markdown(f"#### Figure 2: {PLOT_2_TITLE}")
# plot_2 = gr.Plot(plot_2, show_label=False)
# with gr.Row():
# with gr.Column():
# gr.Markdown(f"#### Figure 3: {PLOT_3_TITLE}")
# plot_3 = gr.Plot(plot_3, show_label=False)
# with gr.Column():
# gr.Markdown(f"#### Figure 4: {PLOT_4_TITLE}")
# plot_4 = gr.Plot(plot_4, show_label=False)
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=3600)
scheduler.start()
demo.queue(concurrency_count=40).launch()