Spaces:
Running
Running
File size: 1,734 Bytes
ab5f5f1 dc685a9 ab5f5f1 a1f6c2e ab5f5f1 dc685a9 ab5f5f1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
import gradio as gr
import plotly.express as px
SCORE_MEMORY_LATENCY_DATA = [
"Model 🤗",
"Arch 🏛️",
"Params (B)",
"DType 📥",
"Backend 🏭",
"Optimization 🛠️",
"Quantization 🗜️",
"Open LLM Score (%)",
"Prefill Latency (s)",
"Decode Throughput (tokens/s)",
"Allocated Memory (MB)",
"E2E Latency (s)",
# "E2E Throughput (tokens/s)",
]
def get_lat_score_mem_fig(llm_perf_df):
copy_df = llm_perf_df.copy()
# plot
fig = px.scatter(
copy_df,
x="E2E Latency (s)",
y="Open LLM Score (%)",
size="Allocated Memory (MB)",
color="Arch 🏛️",
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",
"y": 0.95,
"x": 0.5,
"xanchor": "center",
"yanchor": "top",
},
xaxis_title="Time To Generate 256 Tokens (s)",
yaxis_title="Open LLM Score (%)",
legend_title="LLM Architecture",
width=1200,
height=600,
)
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
|