import gradio as gr import plotly.express as px SCORE_MEMORY_LATENCY_DATA = [ "Model 🤗", "Backend 🏭", "Precision 📥", "Params (B)", "Quantization 🗜️", "Attention 👁️", "Kernel ⚛️", "Open LLM Score (%)", "Prefill (s)", "Decode (tokens/s)", "Memory (MB)", "End-to-End (s)", "Architecture 🏛️", ] def get_lat_score_mem_fig(llm_perf_df): copy_df = llm_perf_df.copy() # plot # filter nan memory fig = px.scatter( copy_df, size="Memory (MB)", x="End-to-End (s)", y="Open LLM Score (%)", color="Architecture 🏛️", custom_data=SCORE_MEMORY_LATENCY_DATA, color_discrete_sequence=px.colors.qualitative.Light24, ) fig.update_traces( hovertemplate="
".join( [ f"{column}: %{{customdata[{i}]}}" for i, column in enumerate(SCORE_MEMORY_LATENCY_DATA) ] ) ) fig.update_layout( title={ "text": "Latency vs. Score vs. Memory", "xanchor": "center", "yanchor": "top", "y": 0.95, "x": 0.5, }, xaxis_title="Time To Generate 64 Tokens (s)", yaxis_title="Open LLM Score (%)", legend_title="LLM Architecture", width=1200, height=600, ) # update x range with 95 percentile of fig.update_xaxes(range=[0, copy_df["End-to-End (s)"].quantile(0.95)]) return fig def create_lat_score_mem_plot(llm_perf_df): # descriptive text gr.HTML("👆 Hover over the points 👆 for additional information. ", elem_id="text") # get figure fig = get_lat_score_mem_fig(llm_perf_df) # create plot plot = gr.components.Plot( value=fig, elem_id="plot", show_label=False, ) return plot