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import gradio as gr
import plotly.express as px
from pathlib import Path
import pandas as pd
import numpy as np
from langchain_openai import ChatOpenAI
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
from langchain.agents.agent_types import AgentType
from langchain_google_genai import ChatGoogleGenerativeAI
import plotly.graph_objects as go
def explain_df(query, df):
agent = create_pandas_dataframe_agent(
# ChatOpenAI(
# base_url="https://fmapi.swissai.cscs.ch",
# temperature=0.01,
# model="meta-llama/Llama-3.3-70B-Instruct"
# ),
ChatGoogleGenerativeAI(
model="gemini-1.5-pro",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
),
df,
verbose=True,
allow_dangerous_code=True,
)
response = agent.invoke(query)
return response['output']
abs_path = Path(__file__).parent
def parse_model_args(model_args):
if "deltazip" in model_args:
model_args = model_args.split("deltazip")[1]
model_args = model_args.split(",")[0]
model_args = model_args.strip(".")
model_args = model_args.replace(".", "/")
if "espressor/" in model_args:
model_args = model_args.split("espressor/")[1]
model_args = model_args.split(",")[0]
model_args = model_args.strip(".")
model_args = model_args.replace(".", "/",1)
model_args = model_args.split("_")[0]
else:
model_args = model_args.split(",")[0]
model_args = model_args.replace("pretrained=", "")
return model_args
def parse_model_precision(model_args):
if "espressor" in model_args:
if 'W8A8_int8' in model_args:
precision = 'W8A8_int8'
else:
precision = model_args.split("_")[-1]
else:
precision = "Default"
return precision
# Any pandas-compatible data
df = pd.read_csv(str(abs_path / "eval_results.csv"))
perf_df = pd.read_csv(str(abs_path / "perfbench_results.csv"))
# take acc only
df = df[df['metric'] == 'acc']
# dedup
df = df.drop_duplicates(subset=['model', 'task'])
# pivot df, such that the column names are model,task,efficiency
# but keep precision in its original place
df['model_physical_size'] = df['model_physical_size'].apply(lambda x: x/1024/1024/1024)
df = df.pivot(index=['model','hf_name','model_physical_size'], columns='task', values='value').reset_index()
df['precision'] = df['model'].apply(lambda x: x.split(":")[-1])
df['model'] = df['model'].apply(lambda x: x.split(":")[0])
df['avg_acc'] = df.filter(like='task_').mean(axis=1)
df = df.rename(columns=lambda x: x.replace('task_', ''))
numeric_columns = df.select_dtypes(include=[np.number]).columns
# remove physical size from numeric columns
numeric_columns = numeric_columns.drop('model_physical_size')
df[numeric_columns] = (df[numeric_columns]*100).round(2)
df['model_physical_size'] = df['model_physical_size'].round(2)
full_df = df.merge(perf_df, left_on='hf_name', right_on='hf_name', how='left')
with gr.Blocks() as demo:
gr.Markdown("""
# 🥇 Efficient LLM Leaderboard
""")
with gr.Tabs():
with gr.TabItem("Leaderboard"):
# ...existing code...
task_options = [col for col in df.columns if col not in ['model','hf_name','model_physical_size', 'precision']]
task_options.append("plot_pareto")
with gr.Row():
# print pareto or not
selected_tasks = gr.CheckboxGroup(choices=task_options, label="Select Tasks")
with gr.Row():
accuracy_plot = gr.Plot(label="Accuracy Plot")
line_plot = gr.Plot(label="Average Accuracy vs Model Size")
with gr.Row():
throughput_line_plot = gr.Plot(label="Throughput vs Average Accuracy")
latency_line_plot = gr.Plot(label="Latency vs Average Accuracy")
with gr.Row():
data_table = gr.Dataframe(value=df, label="Result Table")
def update_outputs(selected_tasks):
if not selected_tasks:
return df[['model', 'precision']], None, None
plot_pareto=False
if "plot_pareto" in selected_tasks:
plot_pareto = True
selected_tasks.remove("plot_pareto")
filtered_df = df[['model', 'precision', 'model_physical_size','hf_name'] + selected_tasks]
# average accuracy of selected tasks
filtered_df['avg_accuracy'] = filtered_df[selected_tasks].mean(axis=1)
bar_fig = px.bar(filtered_df, x='model', y='avg_accuracy', color='precision', barmode='group')
line_fig = px.line(filtered_df, x='model_physical_size', y='avg_accuracy', color='model', symbol='precision')
pareto_df = filtered_df.sort_values('model_physical_size')
pareto_df = pareto_df.loc[pareto_df['avg_accuracy'].cummax().drop_duplicates().index]
# Add Pareto frontier to line_plot
if plot_pareto:
line_fig.add_trace(go.Scatter(
x=pareto_df['model_physical_size'],
y=pareto_df['avg_accuracy'],
mode='lines+markers',
name='Pareto Frontier'
))
# set title of bar_fig
bar_fig.update_layout(title=f'tasks: {", ".join(selected_tasks)}')
line_fig.update_layout(title=f'tasks: {", ".join(selected_tasks)}')
with_perf_df = filtered_df.merge(perf_df, left_on='hf_name', right_on='hf_name', how='left')
throughput_line_fig = px.line(with_perf_df, x='output_throughput', y='avg_accuracy', color='model', symbol='precision')
latency_line_fig = px.line(with_perf_df, x="avg_e2e_latency", y='avg_accuracy', color='model', symbol='precision')
pareto_df = with_perf_df.sort_values('avg_e2e_latency')
pareto_df = pareto_df.loc[pareto_df['avg_accuracy'].cummax().drop_duplicates().index]
if plot_pareto:
latency_line_fig.add_trace(go.Scatter(
x=pareto_df['avg_e2e_latency'],
y=pareto_df['avg_accuracy'],
mode='lines+markers',
name='Pareto Frontier'
))
return with_perf_df, bar_fig, line_fig, throughput_line_fig, latency_line_fig
selected_tasks.change(
fn=update_outputs,
inputs=selected_tasks,
outputs=[data_table, accuracy_plot, line_plot, throughput_line_plot, latency_line_plot]
)
with gr.TabItem("Find Model"):
query_input = gr.Textbox(label="Enter your query", placeholder="Enter your query here")
response_output = gr.Textbox(label="Response", interactive=False)
query_input.submit(
fn=lambda query: explain_df(query, df),
inputs=query_input,
outputs=response_output
)
if __name__ == "__main__":
demo.launch(share=True) |