import matplotlib
matplotlib.use('Agg')
import functools
import gradio as gr
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
# benchmark order: pytorch, tf eager, tf xla; units = ms
BENCHMARK_DATA = {
"Greedy Decoding": {
"DistilGPT2": {
"T4": [336.22, 3976.23, 115.84],
"3090": [158.38, 1835.82, 46.56],
"A100": [371.49, 4073.84, 60.94],
},
"GPT2": {
"T4": [607.31, 7140.23, 185.12],
"3090": [297.03, 3308.31, 76.68],
"A100": [691.75, 7323.60, 110.72],
},
"OPT-1.3B": {
"T4": [1303.41, 15939.07, 1488.15],
"3090": [428.33, 7259.43, 468.37],
"A100": [1125.00, 16713.63, 384.52],
},
"GPTJ-6B": {
"T4": [0, 0, 0],
"3090": [0, 0, 0],
"A100": [2664.28, 32783.09, 1440.06],
},
"T5 Small": {
"T4": [99.88, 1527.73, 18.78],
"3090": [55.09, 665.70, 9.25],
"A100": [124.91, 1642.07, 13.72],
},
"T5 Base": {
"T4": [416.56, 6095.05, 106.12],
"3090": [223.00, 2503.28, 46.67],
"A100": [550.76, 6504.11, 64.57],
},
"T5 Large": {
"T4": [645.05, 9587.67, 225.17],
"3090": [377.74, 4216.41, 97.92],
"A100": [944.17, 10572.43, 116.52],
},
"T5 3B": {
"T4": [1493.61, 13629.80, 1494.80],
"3090": [694.75, 6316.79, 489.33],
"A100": [1801.68, 16707.71, 411.93],
},
},
"Sampling": {
"DistilGPT2": {
"T4": [617.40, 6078.81, 221.65],
"3090": [310.37, 2843.73, 85.44],
"A100": [729.05, 7140.05, 121.83],
},
"GPT2": {
"T4": [1205.34, 12256.98, 378.69],
"3090": [577.12, 5637.11, 160.02],
"A100": [1377.68, 15605.72, 234.47],
},
"OPT-1.3B": {
"T4": [2166.72, 19126.25, 2341.32],
"3090": [706.50, 9616.97, 731.58],
"A100": [2019.70, 28621.09, 690.36],
},
"GPTJ-6B": {
"T4": [0, 0, 0],
"3090": [0, 0, 0],
"A100": [5150.35, 70554.07, 2744.49],
},
"T5 Small": {
"T4": [235.93, 3599.47, 41.07],
"3090": [100.41, 1093.33, 23.24],
"A100": [267.42, 3366.73, 28.53],
},
"T5 Base": {
"T4": [812.59, 7966.73, 196.85],
"3090": [407.81, 4904.54, 97.56],
"A100": [1033.05, 11521.97, 123.93],
},
"T5 Large": {
"T4": [1114.22, 16433.31, 424.91],
"3090": [647.61, 7184.71, 160.97],
"A100": [1668.73, 19962.78, 200.75],
},
"T5 3B": {
"T4": [2282.56, 20891.22, 2196.02],
"3090": [1011.32, 9735.97, 734.40],
"A100": [2769.64, 26440.65, 612.98],
},
},
"Beam Search": {
"DistilGPT2": {
"T4": [2407.89, 19442.60, 3313.92],
"3090": [998.52, 8286.03, 900.28],
"A100": [2237.41, 21771.40, 760.47],
},
"GPT2": {
"T4": [3767.43, 34813.93, 5559.42],
"3090": [1633.04, 14606.93, 1533.55],
"A100": [3705.43, 34586.23, 1295.87],
},
"OPT-1.3B": {
"T4": [16649.82, 78500.33, 21894.31],
"3090": [508518, 32822.81, 5762.46],
"A100": [5967.32, 78334.56, 4096.38],
},
"GPTJ-6B": {
"T4": [0, 0, 0],
"3090": [0, 0, 0],
"A100": [15119.10, 134000.40, 10214.17],
},
"T5 Small": {
"T4": [283.64, 25089.12, 1391.66],
"3090": [137.38, 10680.28, 486.96],
"A100": [329.28, 24747.38, 513.99],
},
"T5 Base": {
"T4": [1383.21, 44809.14, 3920.40],
"3090": [723.11, 18657.48, 1258.60],
"A100": [2360.85, 45085.07, 1107.58],
},
"T5 Large": {
"T4": [1663.50, 81902.41, 9551.29],
"3090": [922.53, 35524.30, 2838.86],
"A100": [2168.22, 86890.00, 2373.04],
},
"T5 3B": {
"T4": [0, 0, 0],
"3090": [1521.05, 35337.30, 8282.09],
"A100": [3162.54, 88453.65, 5585.20],
},
},
}
FIGURE_PATH = "plt.png"
FIG_DPI = 300
def get_plot(model_name, plot_eager, generate_type):
df = pd.DataFrame(BENCHMARK_DATA[generate_type][model_name])
df["framework"] = ["PyTorch", "TF (Eager Execution)", "TF (XLA)"]
df = pd.melt(df, id_vars=["framework"], value_vars=["T4", "3090", "A100"])
if plot_eager == "No":
df = df[df["framework"] != "TF (Eager Execution)"]
g = sns.catplot(
data=df,
kind="bar",
x="variable",
y="value",
hue="framework",
palette={"PyTorch": "blue", "TF (Eager Execution)": "orange", "TF (XLA)": "red"},
alpha=.9,
)
g.despine(left=True)
g.set_axis_labels("GPU", "Generation time (ms)")
g.legend.set_title("Framework")
# Add the number to the top of each bar
ax = g.facet_axis(0, 0)
for i in ax.containers:
ax.bar_label(i,)
plt.savefig(FIGURE_PATH, dpi=FIG_DPI)
return FIGURE_PATH
demo = gr.Blocks()
with demo:
gr.Markdown(
"""
# TensorFlow XLA Text Generation Benchmark
Instructions:
1. Pick a tab for the type of generation (or for benchmark information);
2. Select a model from the dropdown menu;
3. Optionally omit results from TensorFlow Eager Execution, if you wish to better compare the performance of
PyTorch to TensorFlow with XLA.
"""
)
with gr.Tabs():
with gr.TabItem("Greedy Decoding"):
plot_fn = functools.partial(get_plot, generate_type="Greedy Decoding")
with gr.Row():
with gr.Column():
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,
)
eager_enabler = gr.Radio(
["Yes", "No"],
value="Yes",
label="Plot TF Eager Execution?",
interactive=True
)
gr.Markdown(
"""
### Greedy Decoding benchmark parameters
- `max_new_tokens = 64`;
- `pad_to_multiple_of = 64` for Tensorflow XLA models. Others do not pad (input prompts between 2 and 33 tokens).
"""
)
plot = gr.Image(value=plot_fn("T5 Small", "Yes")) # Show plot when the gradio app is initialized
model_selector.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot)
eager_enabler.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot)
with gr.TabItem("Sampling"):
plot_fn = functools.partial(get_plot, generate_type="Sampling")
with gr.Row():
with gr.Column():
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,
)
eager_enabler = gr.Radio(
["Yes", "No"],
value="Yes",
label="Plot TF Eager Execution?",
interactive=True
)
gr.Markdown(
"""
### Sampling benchmark parameters
- `max_new_tokens = 128`;
- `temperature = 2.0`;
- `top_k = 50`;
- `pad_to_multiple_of = 64` for Tensorflow XLA models. Others do not pad (input prompts between 2 and 33 tokens).
"""
)
plot = gr.Image(value=plot_fn("T5 Small", "Yes")) # Show plot when the gradio app is initialized
model_selector.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot)
eager_enabler.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot)
with gr.TabItem("Beam Search"):
plot_fn = functools.partial(get_plot, generate_type="Beam Search")
with gr.Row():
with gr.Column():
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,
)
eager_enabler = gr.Radio(
["Yes", "No"],
value="Yes",
label="Plot TF Eager Execution?",
interactive=True
)
gr.Markdown(
"""
### Beam Search benchmark parameters
- `max_new_tokens = 256`;
- `num_beams = 16`;
- `pad_to_multiple_of = 64` for Tensorflow XLA models. Others do not pad (input prompts between 2 and 33 tokens).
"""
)
plot = gr.Image(value=plot_fn("T5 Small", "Yes")) # Show plot when the gradio app is initialized
model_selector.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot)
eager_enabler.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot)
with gr.TabItem("Benchmark Information"):
gr.Dataframe(
headers=["Parameter", "Value"],
value=[
["Transformers Version", "4.21"],
["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()