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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="<br>".join(
[
f"<b>{column}:</b> %{{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=[-0.5, copy_df["End-to-End (s)"].quantile(0.90)])
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")
gr.HTML("📊 We only show the top 90% LLMs based on latency ⌛", 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
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