app.py

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()