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Running
on
CPU Upgrade
Clémentine
commited on
Commit
•
8b28d2b
1
Parent(s):
bcc83eb
added leaderboard component to simplify main script
Browse files- app.py +34 -175
- requirements.txt +2 -4
- src/display/utils.py +0 -13
- src/populate.py +1 -1
app.py
CHANGED
@@ -1,5 +1,5 @@
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import subprocess
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import gradio as gr
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import pandas as pd
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from apscheduler.schedulers.background import BackgroundScheduler
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from huggingface_hub import snapshot_download
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@@ -18,8 +18,6 @@ from src.display.utils import (
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COLS,
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EVAL_COLS,
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EVAL_TYPES,
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NUMERIC_INTERVALS,
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TYPES,
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AutoEvalColumn,
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ModelType,
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fields,
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@@ -34,6 +32,7 @@ from src.submission.submit import add_new_eval
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def restart_space():
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API.restart_space(repo_id=REPO_ID)
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try:
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print(EVAL_REQUESTS_PATH)
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snapshot_download(
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restart_space()
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-
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leaderboard_df = original_df.copy()
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(
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finished_eval_queue_df,
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@@ -59,77 +57,36 @@ leaderboard_df = original_df.copy()
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pending_eval_queue_df,
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) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
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)
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return filtered_df
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def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
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final_df = []
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if query != "":
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queries = [q.strip() for q in query.split(";")]
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for _q in queries:
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_q = _q.strip()
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if _q != "":
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temp_filtered_df = search_table(filtered_df, _q)
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if len(temp_filtered_df) > 0:
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final_df.append(temp_filtered_df)
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if len(final_df) > 0:
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filtered_df = pd.concat(final_df)
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filtered_df = filtered_df.drop_duplicates(
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subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
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)
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return filtered_df
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def filter_models(
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df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
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) -> pd.DataFrame:
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# Show all models
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if show_deleted:
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filtered_df = df
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else: # Show only still on the hub models
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filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
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type_emoji = [t[0] for t in type_query]
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filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
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filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
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-
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numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
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params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
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mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
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filtered_df = filtered_df.loc[mask]
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return filtered_df
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demo = gr.Blocks(css=custom_css)
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with gr.Tabs(elem_classes="tab-buttons") as tabs:
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with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
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with gr.Column():
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with gr.Row():
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search_bar = gr.Textbox(
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placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
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show_label=False,
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elem_id="search-bar",
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)
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with gr.Row():
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shown_columns = gr.CheckboxGroup(
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choices=[
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c.name
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for c in fields(AutoEvalColumn)
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if not c.hidden and not c.never_hidden
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],
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value=[
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c.name
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for c in fields(AutoEvalColumn)
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if c.displayed_by_default and not c.hidden and not c.never_hidden
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],
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label="Select columns to show",
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elem_id="column-select",
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interactive=True,
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)
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with gr.Row():
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deleted_models_visibility = gr.Checkbox(
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value=False, label="Show gated/private/deleted models", interactive=True
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)
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with gr.Column(min_width=320):
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#with gr.Box(elem_id="box-filter"):
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filter_columns_type = gr.CheckboxGroup(
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label="Model types",
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choices=[t.to_str() for t in ModelType],
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value=[t.to_str() for t in ModelType],
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interactive=True,
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elem_id="filter-columns-type",
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)
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filter_columns_precision = gr.CheckboxGroup(
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label="Precision",
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choices=[i.value.name for i in Precision],
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value=[i.value.name for i in Precision],
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interactive=True,
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elem_id="filter-columns-precision",
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)
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filter_columns_size = gr.CheckboxGroup(
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label="Model sizes (in billions of parameters)",
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choices=list(NUMERIC_INTERVALS.keys()),
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value=list(NUMERIC_INTERVALS.keys()),
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interactive=True,
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elem_id="filter-columns-size",
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)
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leaderboard_table = gr.components.Dataframe(
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value=leaderboard_df[
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[c.name for c in fields(AutoEvalColumn) if c.never_hidden]
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+ shown_columns.value
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],
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headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
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datatype=TYPES,
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elem_id="leaderboard-table",
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interactive=False,
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visible=True,
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)
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# Dummy leaderboard for handling the case when the user uses backspace key
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hidden_leaderboard_table_for_search = gr.components.Dataframe(
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value=original_df[COLS],
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headers=COLS,
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datatype=TYPES,
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visible=False,
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)
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search_bar.submit(
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update_table,
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[
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hidden_leaderboard_table_for_search,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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filter_columns_size,
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deleted_models_visibility,
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search_bar,
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],
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leaderboard_table,
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)
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for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility]:
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selector.change(
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update_table,
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[
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hidden_leaderboard_table_for_search,
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shown_columns,
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filter_columns_type,
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filter_columns_precision,
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filter_columns_size,
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deleted_models_visibility,
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search_bar,
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],
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leaderboard_table,
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queue=True,
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)
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with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
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gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
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import gradio as gr
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from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
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import pandas as pd
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from apscheduler.schedulers.background import BackgroundScheduler
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from huggingface_hub import snapshot_download
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COLS,
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EVAL_COLS,
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EVAL_TYPES,
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AutoEvalColumn,
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ModelType,
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fields,
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def restart_space():
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API.restart_space(repo_id=REPO_ID)
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### Space initialisation
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try:
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print(EVAL_REQUESTS_PATH)
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snapshot_download(
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restart_space()
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LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
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(
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finished_eval_queue_df,
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pending_eval_queue_df,
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) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
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def init_leaderboard(dataframe):
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if dataframe is None or dataframe.empty:
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raise ValueError("Leaderboard DataFrame is empty or None.")
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return Leaderboard(
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value=dataframe,
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datatype=[c.type for c in fields(AutoEvalColumn)],
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select_columns=SelectColumns(
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default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
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cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
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label="Select Columns to Display:",
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),
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search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
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hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
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filter_columns=[
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ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
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ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
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ColumnFilter(
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AutoEvalColumn.params.name,
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type="slider",
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min=0.01,
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max=150,
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label="Select the number of parameters (B)",
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),
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ColumnFilter(
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AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
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),
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],
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bool_checkboxgroup_label="Hide models",
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interactive=False,
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)
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demo = gr.Blocks(css=custom_css)
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with gr.Tabs(elem_classes="tab-buttons") as tabs:
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with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
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leaderboard = init_leaderboard(LEADERBOARD_DF)
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with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
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gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
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requirements.txt
CHANGED
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APScheduler
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black
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click
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datasets
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gradio
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gradio_client
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huggingface-hub>=0.18.0
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matplotlib
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numpy
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pandas
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python-dateutil
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requests
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tqdm
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transformers
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tokenizers>=0.15.0
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git+https://github.com/EleutherAI/lm-evaluation-harness.git@b281b0921b636bc36ad05c0b0b0763bd6dd43463#egg=lm-eval
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accelerate
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sentencepiece
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APScheduler
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black
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datasets
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gradio
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gradio[oauth]
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gradio_leaderboard==0.0.9
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gradio_client
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huggingface-hub>=0.18.0
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matplotlib
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numpy
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pandas
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python-dateutil
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tqdm
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transformers
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tokenizers>=0.15.0
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sentencepiece
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src/display/utils.py
CHANGED
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# Column selection
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COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
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TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
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COLS_LITE = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
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TYPES_LITE = [c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
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EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
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EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
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BENCHMARK_COLS = [t.value.col_name for t in Tasks]
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NUMERIC_INTERVALS = {
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"?": pd.Interval(-1, 0, closed="right"),
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"~1.5": pd.Interval(0, 2, closed="right"),
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"~3": pd.Interval(2, 4, closed="right"),
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"~7": pd.Interval(4, 9, closed="right"),
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"~13": pd.Interval(9, 20, closed="right"),
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"~35": pd.Interval(20, 45, closed="right"),
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"~60": pd.Interval(45, 70, closed="right"),
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"70+": pd.Interval(70, 10000, closed="right"),
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}
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# Column selection
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COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
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EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
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EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
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BENCHMARK_COLS = [t.value.col_name for t in Tasks]
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src/populate.py
CHANGED
@@ -19,7 +19,7 @@ def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchm
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# filter out if any of the benchmarks have not been produced
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df = df[has_no_nan_values(df, benchmark_cols)]
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return
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def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
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# filter out if any of the benchmarks have not been produced
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df = df[has_no_nan_values(df, benchmark_cols)]
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return df
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def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
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