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import functools
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import gradio as gr
BENCHMARK_DATA = {
"Greedy Search": {
"DistilGPT2": {
"T4": [],
"3090": [],
"A100": [],
},
"GPT2": {
"T4": [],
"3090": [],
"A100": [],
},
"OPT-1.3B": {
"T4": [],
"3090": [],
"A100": [],
},
"GPTJ-6B": {
"T4": [],
"3090": [],
"A100": [],
},
"T5 Small": {
"T4": [1, 2, 3, 4],
"3090": [],
"A100": [],
},
"T5 Base": {
"T4": [],
"3090": [],
"A100": [],
},
"T5 Large": {
"T4": [],
"3090": [],
"A100": [],
},
"T5 3B": {
"T4": [],
"3090": [],
"A100": [],
},
},
"Sample": {
"DistilGPT2": {
"T4": [],
"3090": [],
"A100": [],
},
"GPT2": {
"T4": [],
"3090": [],
"A100": [],
},
"OPT-1.3B": {
"T4": [],
"3090": [],
"A100": [],
},
"GPTJ-6B": {
"T4": [],
"3090": [],
"A100": [],
},
"T5 Small": {
"T4": [],
"3090": [],
"A100": [],
},
"T5 Base": {
"T4": [],
"3090": [],
"A100": [],
},
"T5 Large": {
"T4": [],
"3090": [],
"A100": [],
},
"T5 3B": {
"T4": [],
"3090": [],
"A100": [],
},
},
"Beam Search": {
"DistilGPT2": {
"T4": [],
"3090": [],
"A100": [],
},
"GPT2": {
"T4": [],
"3090": [],
"A100": [],
},
"OPT-1.3B": {
"T4": [],
"3090": [],
"A100": [],
},
"GPTJ-6B": {
"T4": [],
"3090": [],
"A100": [],
},
"T5 Small": {
"T4": [],
"3090": [],
"A100": [],
},
"T5 Base": {
"T4": [],
"3090": [],
"A100": [],
},
"T5 Large": {
"T4": [],
"3090": [],
"A100": [],
},
"T5 3B": {
"T4": [],
"3090": [],
"A100": [],
},
},
}
def get_plot(model_name, generate_type):
data = BENCHMARK_DATA[generate_type][model_name]["T4"]
plt.plot(data)
plt.title(model_name)
return plt.gcf()
demo = gr.Blocks()
with demo:
with gr.Tabs():
with gr.TabItem("Greedy Search"):
model_selector = gr.Dropdown(
choices=["DistilGPT2", "GPT2", "OPT-1.3B", "GPTJ-6B", "T5 Small", "T5 Base", "T5 Large", "T5 3B"],
value="T5 Small",
label="Model",
interactive=True,
)
plot_fn = functools.partial(get_plot, generate_type="Greedy Search")
plot = gr.Plot(value=plot_fn("T5 Small")) # Show plot when the gradio app is initialized
model_selector.change(fn=get_plot, inputs=[model_selector], outputs=plot)
with gr.TabItem("Sample"):
gr.Button("New Tiger")
with gr.TabItem("Beam Search"):
gr.Button("New Tiger")
with gr.TabItem("Benchmark Information"):
gr.Dataframe(
headers=["Parameter", "Value"],
value=[
["Transformers Version", "4.22.dev0"],
["TensorFlow Version", "2.9.1"],
["Pytorch Version", "1.11.0"],
["OS", "22.04 LTS (3090) / Debian 10 (other GPUs)"],
["CUDA", "11.6 (3090) / 11.3 (others GPUs)"],
["Number of runs", "100 (the first run was discarded to ignore compilation time)"],
["Is there code to reproduce?", "Yes -- https://gist.github.com/gante/f0017e3f13ac11b0c02e4e4db351f52f"],
],
)
demo.launch()
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