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import os
os.system("pip install gradio==3.28.0 pydantic==1.10.15")
import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
from gradio_space_ci import enable_space_ci
from src.display.about import (
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
EVALUATION_QUEUE_TEXT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
FAQ_TEXT,
TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
BENCHMARK_COLS,
COLS,
EVAL_COLS,
EVAL_TYPES,
NUMERIC_INTERVALS,
NUMERIC_MODELSIZE,
TYPES,
AutoEvalColumn,
GroupDtype,
ModelType,
fields,
WeightType,
Precision,
ComputeDtype,
WeightDtype,
QuantType
)
from src.envs import API, EVAL_REQUESTS_PATH, DYNAMIC_INFO_REPO, DYNAMIC_INFO_FILE_PATH, DYNAMIC_INFO_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, QUEUE_REPO, REPO_ID, RESULTS_REPO, REPO, GIT_REQUESTS_PATH, GIT_STATUS_PATH, GIT_RESULTS_PATH
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval
from src.scripts.update_all_request_files import update_dynamic_files
from src.tools.collections import update_collections
from src.tools.plots import (
create_metric_plot_obj,
create_plot_df,
create_scores_df,
)
from gradio_modal import Modal
import plotly.graph_objects as go
selected_indices = []
# Start ephemeral Spaces on PRs (see config in README.md)
#enable_space_ci()
precision_to_dtype = {
"2bit": ["int2"],
"3bit": ["int3"],
"4bit": ["int4", "nf4", "fp4"],
"?": ["?"]
}
current_weightDtype = ["All", "int2", "int3", "int4", "nf4", "fp4", "?"]
# Global variable to store the selected dtypes
selected_dtypes = ["All"]
init_select = False
def quant_update_Weight_Dtype(selected_precisions):
global current_weightDtype
if 'β None' in selected_precisions:
if not any(dtype in ['float16', 'bfloat16', 'float32'] for dtype in current_weightDtype):
current_weightDtype += ['float16', 'bfloat16', 'float32']
else:
if any(dtype in ['float16', 'bfloat16', 'float32'] for dtype in current_weightDtype):
current_weightDtype = [dtype for dtype in current_weightDtype if dtype not in ['float16', 'bfloat16', 'float32']]
return gr.Dropdown.update(choices=current_weightDtype, value="All")
def update_Weight_Dtype(selected_precisions):
global selected_dtypes
global current_weightDtype
global init_select
init_select = True
if not selected_precisions: # If no precision is selected, return "All"
selected_dtypes = ["All"]
return gr.Dropdown.update(choices=["All"], value="All")
selected_dtypes_set = set()
for precision in selected_precisions:
if precision in precision_to_dtype:
selected_dtypes_set.update(precision_to_dtype[precision])
# Convert set to sorted list to maintain order
selected_dtypes = sorted(selected_dtypes_set)
if any(dtype in ['float16', 'bfloat16', 'float32'] for dtype in current_weightDtype) and not any(dtype in ['float16', 'bfloat16', 'float32'] for dtype in selected_dtypes):
selected_dtypes += ['float16', 'bfloat16', 'float32']
# Add "All" to the beginning of the list for display purposes
display_choices = ["All"] + selected_dtypes
current_weightDtype = display_choices
return gr.Dropdown.update(choices=display_choices, value="All")
def restart_space():
API.restart_space(repo_id=REPO_ID, token=H4_TOKEN)
def init_space(full_init: bool = True):
if full_init:
try:
branch = REPO.active_branch.name
REPO.remotes.origin.pull(branch)
except Exception as e:
print(str(e))
restart_space()
try:
print(DYNAMIC_INFO_PATH)
snapshot_download(
repo_id=DYNAMIC_INFO_REPO, local_dir=DYNAMIC_INFO_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
)
except Exception:
restart_space()
raw_data, original_df = get_leaderboard_df(
results_path=GIT_RESULTS_PATH,
requests_path=GIT_STATUS_PATH,
dynamic_path=DYNAMIC_INFO_FILE_PATH,
cols=COLS,
benchmark_cols=BENCHMARK_COLS
)
# update_collections(original_df.copy())
leaderboard_df = original_df.copy()
plot_df = create_plot_df(create_scores_df(raw_data))
(
finished_eval_queue_df,
running_eval_queue_df,
pending_eval_queue_df,
) = get_evaluation_queue_df(GIT_STATUS_PATH, EVAL_COLS)
return leaderboard_df, original_df, plot_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df
leaderboard_df, original_df, plot_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space()
def str_to_bool(value):
if str(value).lower() == "true":
return True
elif str(value).lower() == "false":
return False
else:
return False
# Searching and filtering
def update_table(
hidden_df: pd.DataFrame,
columns: list,
type_query: list,
precision_query: str,
size_query: list,
params_query: list,
hide_models: list,
query: str,
compute_dtype: str,
weight_dtype: str,
double_quant: str,
group_dtype: str
):
global init_select
global current_weightDtype
if selected_dtypes == ['All']:
weight_dtype = current_weightDtype
elif weight_dtype == ['All'] or weight_dtype == 'All' or init_select:
weight_dtype = selected_dtypes
init_select = False
else:
weight_dtype = [weight_dtype]
if compute_dtype == 'All':
compute_dtype = ['bfloat16', 'float16', 'int8', 'float32']
else:
compute_dtype = [compute_dtype]
if group_dtype == 'All':
group_dtype = [-1, 1024, 256, 128, 64, 32]
else:
try:
group_dtype = [int(group_dtype)]
except ValueError:
group_dtype = [-1]
double_quant = [str_to_bool(double_quant)]
filtered_df = filter_models(df=hidden_df, type_query=type_query, size_query=size_query, precision_query=precision_query, hide_models=hide_models, compute_dtype=compute_dtype, weight_dtype=weight_dtype, double_quant=double_quant, group_dtype=group_dtype, params_query=params_query)
filtered_df = filter_queries(query, filtered_df)
df = select_columns(filtered_df, columns)
return df
def load_query(request: gr.Request): # triggered only once at startup => read query parameter if it exists
query = request.query_params.get("query") or ""
return query, query # return one for the "search_bar", one for a hidden component that triggers a reload only if value has changed
def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))]
def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
always_here_cols = [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
dummy_col = [AutoEvalColumn.dummy.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] + dummy_col
]
return filtered_df
def filter_queries(query: str, filtered_df: pd.DataFrame):
"""Added by Abishek"""
final_df = []
if query != "":
queries = [q.strip() for q in query.split(";")]
for _q in queries:
_q = _q.strip()
if _q != "":
temp_filtered_df = search_table(filtered_df, _q)
if len(temp_filtered_df) > 0:
final_df.append(temp_filtered_df)
if len(final_df) > 0:
filtered_df = pd.concat(final_df)
filtered_df = filtered_df.drop_duplicates(
subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
)
return filtered_df
def filter_models(
df: pd.DataFrame, type_query: list, size_query: list, params_query:list, precision_query: list, hide_models: list, compute_dtype: list, weight_dtype: list, double_quant: list, group_dtype: list,
) -> pd.DataFrame:
# Show all models
if "Private or deleted" in hide_models:
filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
else:
filtered_df = df
if "Contains a merge/moerge" in hide_models:
filtered_df = filtered_df[filtered_df[AutoEvalColumn.merged.name] == False]
if "MoE" in hide_models:
filtered_df = filtered_df[filtered_df[AutoEvalColumn.moe.name] == False]
if "Flagged" in hide_models:
filtered_df = filtered_df[filtered_df[AutoEvalColumn.flagged.name] == False]
type_emoji = [t[0] for t in type_query]
filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
filtered_df = filtered_df.loc[df[AutoEvalColumn.weight_dtype.name].isin(weight_dtype)]
filtered_df = filtered_df.loc[df[AutoEvalColumn.compute_dtype.name].isin(compute_dtype)]
filtered_df = filtered_df.loc[df[AutoEvalColumn.double_quant.name].isin(double_quant)]
filtered_df = filtered_df.loc[df[AutoEvalColumn.group_size.name].isin(group_dtype)]
numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
filtered_df = filtered_df.loc[mask]
numeric_interval_params = pd.IntervalIndex(sorted([NUMERIC_MODELSIZE[s] for s in params_query]))
params_column_params = pd.to_numeric(df[AutoEvalColumn.model_size.name], errors="coerce")
mask_params = params_column_params.apply(lambda x: any(numeric_interval_params.contains(x)))
filtered_df = filtered_df.loc[mask_params]
return filtered_df
def select(df, data: gr.SelectData):
global selected_indices
selected_index = data.index[0]
if selected_index in selected_indices:
selected_indices.remove(selected_index)
else:
selected_indices.append(selected_index)
fig = go.Figure()
for i in selected_indices:
row = df.iloc[i, :]
fig.add_trace(go.Scatterpolar(
r=[row['Average β¬οΈ'], row['ARC-c'], row['ARC-e'], row['Boolq'], row['HellaSwag'], row['Lambada'], row['MMLU'], row['Openbookqa'], row['Piqa'], row['Truthfulqa'], row['Winogrande']],
theta=['Average β¬οΈ', 'ARC-c', 'ARC-e', 'Boolq', 'HellaSwag', 'Lambada', 'MMLU', 'Openbookqa', 'Piqa', 'Truthfulqa', 'Winogrande',],
fill='toself',
name=str(row['Model'])
))
fig.update_layout(
polar=dict(
radialaxis=dict(
visible=True,
)),
showlegend=True
)
return fig
leaderboard_df = filter_models(
df=leaderboard_df,
type_query=[t.to_str(" : ") for t in QuantType],
size_query=list(NUMERIC_INTERVALS.keys()),
params_query=list(NUMERIC_MODELSIZE.keys()),
precision_query=[i.value.name for i in Precision],
hide_models=["Private or deleted", "Contains a merge/moerge", "Flagged"], # Deleted, merges, flagged, MoEs,
compute_dtype=[i.value.name for i in ComputeDtype],
weight_dtype=[i.value.name for i in WeightDtype],
double_quant=[True, False],
group_dtype=[-1, 1024, 256, 128, 64, 32]
)
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("π
LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
with gr.Row():
with gr.Column():
with gr.Row():
search_bar = gr.Textbox(
placeholder=" π Search for your model (separate multiple queries with `;`) and press ENTER...",
show_label=False,
elem_id="search-bar",
)
with gr.Row():
shown_columns = gr.CheckboxGroup(
choices=[
c.name
for c in fields(AutoEvalColumn)
if not c.hidden and not c.never_hidden and not c.dummy
],
value=[
c.name
for c in fields(AutoEvalColumn)
if c.displayed_by_default and not c.hidden and not c.never_hidden
],
label="Select columns to show",
elem_id="column-select",
interactive=True,
)
with gr.Row():
filter_columns_parameters = gr.CheckboxGroup(
label="Model parameters (in billions of parameters)",
choices=list(NUMERIC_INTERVALS.keys()),
value=list(NUMERIC_INTERVALS.keys()),
interactive=True,
elem_id="filter-columns-size",
)
with gr.Row():
filter_columns_size = gr.CheckboxGroup(
label="Model sizes (GB, int4)",
choices=list(NUMERIC_MODELSIZE.keys()),
value=list(NUMERIC_MODELSIZE.keys()),
interactive=True,
elem_id="filter-columns-size",
)
with gr.Column(min_width=320):
#with gr.Box(elem_id="box-filter"):
filter_columns_type = gr.CheckboxGroup(
label="Quantization types",
choices=[t.to_str() for t in QuantType],
value=[t.to_str() for t in QuantType if t != QuantType.QuantType_None],
interactive=True,
elem_id="filter-columns-type",
)
filter_columns_precision = gr.CheckboxGroup(
label="Weight precision",
choices=[i.value.name for i in Precision],
value=[i.value.name for i in Precision],
interactive=True,
elem_id="filter-columns-precision",
)
with gr.Box() as config:
gr.HTML("""<p style='padding-bottom: 0.5rem; color: #6b7280; '>Quantization config</p>""")
with gr.Row():
filter_columns_computeDtype = gr.Dropdown(choices=[i.value.name for i in ComputeDtype], label="Compute Dtype", multiselect=False, value="All", interactive=True,)
filter_columns_weightDtype = gr.Dropdown(choices=[i.value.name for i in WeightDtype], label="Weight Dtype", multiselect=False, value="All", interactive=True,)
filter_columns_doubleQuant = gr.Dropdown(choices=["True", "False"], label="Double Quant", multiselect=False, value=False, interactive=True)
filter_columns_groupDtype = gr.Dropdown(choices=[i.value.name for i in GroupDtype], label="Group Size", multiselect=False, value="All", interactive=True,)
leaderboard_table = gr.components.Dataframe(
value=leaderboard_df[
[c.name for c in fields(AutoEvalColumn) if c.never_hidden]
+ shown_columns.value
+ [AutoEvalColumn.dummy.name]
],
headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
datatype=TYPES,
elem_id="leaderboard-table",
interactive=False,
visible=True,
#column_widths=["2%", "33%"]
)
with Modal(visible=False) as modal:
map = gr.Plot()
leaderboard_table.select(select, leaderboard_table, map)
leaderboard_table.select(lambda: Modal(visible=True), None, modal)
# Dummy leaderboard for handling the case when the user uses backspace key
hidden_leaderboard_table_for_search = gr.components.Dataframe(
value=original_df[COLS],
headers=COLS,
datatype=TYPES,
visible=False,
)
hide_models = gr.Textbox(
placeholder="",
show_label=False,
elem_id="search-bar",
value="",
visible=False,
)
search_bar.submit(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_parameters,
filter_columns_size,
hide_models,
search_bar,
filter_columns_computeDtype,
filter_columns_weightDtype,
filter_columns_doubleQuant,
filter_columns_groupDtype
],
leaderboard_table,
)
"""
# Define a hidden component that will trigger a reload only if a query parameter has been set
hidden_search_bar = gr.Textbox(value="", visible=False)
hidden_search_bar.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
hide_models,
search_bar,
],
leaderboard_table,
)
# Check query parameter once at startup and update search bar + hidden component
demo.load(load_query, inputs=[], outputs=[search_bar, hidden_search_bar])
"""
filter_columns_precision.change(
update_Weight_Dtype,
[filter_columns_precision],
[filter_columns_weightDtype]
)
filter_columns_type.change(
quant_update_Weight_Dtype,
[filter_columns_type],
[filter_columns_weightDtype]
)
for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, filter_columns_parameters, hide_models, filter_columns_computeDtype, filter_columns_weightDtype, filter_columns_doubleQuant, filter_columns_groupDtype]:
selector.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_parameters,
filter_columns_size,
hide_models,
search_bar,
filter_columns_computeDtype,
filter_columns_weightDtype,
filter_columns_doubleQuant,
filter_columns_groupDtype
],
leaderboard_table,
queue=True,
)
with gr.TabItem("π Metrics through time", elem_id="llm-benchmark-tab-table", id=2):
with gr.Row():
with gr.Column():
chart = create_metric_plot_obj(
plot_df,
[AutoEvalColumn.average.name],
title="Average of Top Scores and Human Baseline Over Time (from last update)",
)
gr.Plot(value=chart, min_width=500)
with gr.Column():
chart = create_metric_plot_obj(
plot_df,
BENCHMARK_COLS,
title="Top Scores and Human Baseline Over Time (from last update)",
)
gr.Plot(value=chart, min_width=500)
with gr.TabItem("π About", elem_id="llm-benchmark-tab-table", id=3):
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
with gr.TabItem("βFAQ", elem_id="llm-benchmark-tab-table", id=4):
gr.Markdown(FAQ_TEXT, elem_classes="markdown-text")
with gr.TabItem("π Submit ", elem_id="llm-benchmark-tab-table", id=5):
with gr.Column():
with gr.Row():
gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
with gr.Row():
gr.Markdown("# βοΈβ¨ 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 commit", placeholder="main")
private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC)
with gr.Column():
"""
precision = gr.Dropdown(
choices=[i.value.name for i in Precision if i != Precision.Unknown],
label="Precision",
multiselect=False,
value="4bit",
interactive=True,
)
weight_type = gr.Dropdown(
choices=[i.value.name for i in WeightDtype],
label="Weights dtype",
multiselect=False,
value="int4",
interactive=True,
)
"""
base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)",
visible=not IS_PUBLIC)
compute_type = gr.Dropdown(
choices=[i.value.name for i in ComputeDtype],
label="Compute dtype",
multiselect=False,
value="float16",
interactive=True,
)
submit_button = gr.Button("Submit Eval")
submission_result = gr.Markdown()
submit_button.click(
add_new_eval,
[
model_name_textbox,
revision_name_textbox,
private,
compute_type,
],
submission_result,
)
with gr.Column():
with gr.Accordion(
f"β
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,
row_count=5,
)
with gr.Accordion(
f"π 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,
row_count=5,
)
with gr.Accordion(
f"β³ 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,
row_count=5,
)
with gr.Row():
with gr.Accordion("π Citation", open=False):
citation_button = gr.Textbox(
value=CITATION_BUTTON_TEXT,
label=CITATION_BUTTON_LABEL,
lines=20,
elem_id="citation-button",
show_copy_button=True,
)
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", hours=3) # restarted every 3h
scheduler.add_job(update_dynamic_files, "interval", hours=12) # launched every 2 hour
scheduler.start()
# demo.queue(default_concurrency_limit=40).launch()
demo.queue(concurrency_count=40).launch()
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