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import matplotlib |
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matplotlib.use('Agg') |
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import functools |
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import gradio as gr |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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import pandas as pd |
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BENCHMARK_DATA = { |
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"Greedy Search": { |
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"DistilGPT2": { |
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"T4": [336.22, 3976.23, 115.84], |
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"3090": [158.38, 1835.82, 46.56], |
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"A100": [371.49, 4073.84, 60.94], |
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}, |
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"GPT2": { |
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"T4": [607.31, 7140.23, 185.12], |
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"3090": [297.03, 3308.31, 76.68], |
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"A100": [691.75, 7323.60, 110.72], |
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}, |
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"OPT-1.3B": { |
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"T4": [1303.41, 15939.07, 1488.15], |
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"3090": [428.33, 7259.43, 468.37], |
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"A100": [1125.00, 16713.63, 384.52], |
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}, |
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"GPTJ-6B": { |
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"T4": [0, 0, 0], |
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"3090": [0, 0, 0], |
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"A100": [2664.28, 32783.09, 1440.06], |
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}, |
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"T5 Small": { |
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"T4": [99.88, 1527.73, 18.78], |
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"3090": [55.09, 665.70, 9.25], |
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"A100": [124.91, 1642.07, 13.72], |
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}, |
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"T5 Base": { |
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"T4": [416.56, 6095.05, 106.12], |
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"3090": [223.00, 2503.28, 46.67], |
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"A100": [550.76, 6504.11, 64.57], |
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}, |
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"T5 Large": { |
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"T4": [645.05, 9587.67, 225.17], |
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"3090": [377.74, 4216.41, 97.92], |
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"A100": [944.17, 10572.43, 116.52], |
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}, |
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"T5 3B": { |
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"T4": [1493.61, 13629.80, 1494.80], |
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"3090": [694.75, 6316.79, 489.33], |
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"A100": [1801.68, 16707.71, 411.93], |
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}, |
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}, |
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"Sample": { |
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"DistilGPT2": { |
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"T4": [617.40, 6078.81, 221.65], |
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"3090": [310.37, 2843.73, 85.44], |
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"A100": [729.05, 7140.05, 121.83], |
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}, |
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"GPT2": { |
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"T4": [1205.34, 12256.98, 378.69], |
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"3090": [577.12, 5637.11, 160.02], |
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"A100": [1377.68, 15605.72, 234.47], |
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}, |
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"OPT-1.3B": { |
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"T4": [2166.72, 19126.25, 2341.32], |
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"3090": [706.50, 9616.97, 731.58], |
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"A100": [2019.70, 28621.09, 690.36], |
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}, |
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"GPTJ-6B": { |
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"T4": [0, 0, 0], |
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"3090": [0, 0, 0], |
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"A100": [5150.35, 70554.07, 2744.49], |
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}, |
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"T5 Small": { |
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"T4": [235.93, 3599.47, 41.07], |
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"3090": [100.41, 1093.33, 23.24], |
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"A100": [267.42, 3366.73, 28.53], |
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}, |
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"T5 Base": { |
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"T4": [812.59, 7966.73, 196.85], |
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"3090": [407.81, 4904.54, 97.56], |
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"A100": [1033.05, 11521.97, 123.93], |
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}, |
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"T5 Large": { |
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"T4": [1114.22, 16433.31, 424.91], |
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"3090": [647.61, 7184.71, 160.97], |
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"A100": [1668.73, 19962.78, 200.75], |
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}, |
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"T5 3B": { |
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"T4": [2282.56, 20891.22, 2196.02], |
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"3090": [1011.32, 9735.97, 734.40], |
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"A100": [2769.64, 26440.65, 612.98], |
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}, |
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}, |
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"Beam Search": { |
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"DistilGPT2": { |
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"T4": [2407.89, 19442.60, 3313.92], |
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"3090": [998.52, 8286.03, 900.28], |
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"A100": [2237.41, 21771.40, 760.47], |
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}, |
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"GPT2": { |
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"T4": [3767.43, 34813.93, 5559.42], |
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"3090": [1633.04, 14606.93, 1533.55], |
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"A100": [3705.43, 34586.23, 1295.87], |
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}, |
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"OPT-1.3B": { |
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"T4": [16649.82, 78500.33, 21894.31], |
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"3090": [508518, 32822.81, 5762.46], |
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"A100": [5967.32, 78334.56, 4096.38], |
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}, |
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"GPTJ-6B": { |
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"T4": [0, 0, 0], |
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"3090": [0, 0, 0], |
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"A100": [15119.10, 134000.40, 10214.17], |
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}, |
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"T5 Small": { |
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"T4": [283.64, 25089.12, 1391.66], |
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"3090": [137.38, 10680.28, 486.96], |
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"A100": [329.28, 24747.38, 513.99], |
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}, |
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"T5 Base": { |
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"T4": [1383.21, 44809.14, 3920.40], |
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"3090": [723.11, 18657.48, 1258.60], |
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"A100": [2360.85, 45085.07, 1107.58], |
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}, |
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"T5 Large": { |
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"T4": [1663.50, 81902.41, 9551.29], |
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"3090": [922.53, 35524.30, 2838.86], |
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"A100": [2168.22, 86890.00, 2373.04], |
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}, |
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"T5 3B": { |
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"T4": [0, 0, 0], |
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"3090": [1521.05, 35337.30, 8282.09], |
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"A100": [3162.54, 88453.65, 5585.20], |
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}, |
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}, |
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} |
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def get_plot(model_name, plot_eager, generate_type): |
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df = pd.DataFrame(BENCHMARK_DATA[generate_type][model_name]) |
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df["framework"] = ["PyTorch", "TF (Eager Execition)", "TF (XLA)"] |
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df = pd.melt(df, id_vars=["framework"], value_vars=["T4", "3090", "A100"]) |
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if plot_eager == "No": |
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df = df[df["framework"] != "TF (Eager Execition)"] |
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g = sns.catplot( |
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data=df, |
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kind="bar", |
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x="variable", |
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y="value", |
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hue="framework", |
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ci="sd", |
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palette={"PyTorch": "blue", "TF (Eager Execition)": "orange", "TF (XLA)": "red"}, |
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alpha=.6, |
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height=6 |
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) |
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g.despine(left=True) |
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g.set_axis_labels("GPU", "Generation time (ms)") |
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g.legend.set_title("Framework") |
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return plt.gcf() |
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demo = gr.Blocks() |
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with demo: |
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gr.Markdown( |
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""" |
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# TensorFlow XLA Text Generation Benchmark |
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Pick a tab for the type of generation (or other information), and then select a model from the dropdown menu. |
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You can also omit results from TensorFlow Eager Execution, if you wish to better compare the performance of |
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PyTorch to TensorFlow with XLA. |
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""" |
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) |
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with gr.Tabs(): |
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with gr.TabItem("Greedy Search"): |
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gr.Markdown( |
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""" |
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### Greedy Search benchmark parameters |
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- `max_new_tokens = 64`; |
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- `pad_to_multiple_of = 64` for Tensorflow XLA models. Others do not pad (input prompts between 2 and 33 tokens). |
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""" |
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) |
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with gr.Row(): |
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model_selector = gr.Dropdown( |
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choices=["DistilGPT2", "GPT2", "OPT-1.3B", "GPTJ-6B", "T5 Small", "T5 Base", "T5 Large", "T5 3B"], |
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value="T5 Small", |
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label="Model", |
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interactive=True, |
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) |
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eager_enabler = gr.Radio( |
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["Yes", "No"], |
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value="Yes", |
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label="Plot TF Eager Execution?", |
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interactive=True |
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) |
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plot_fn = functools.partial(get_plot, generate_type="Greedy Search") |
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plot = gr.Plot(value=plot_fn("T5 Small", "Yes")) |
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model_selector.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot) |
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eager_enabler.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot) |
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with gr.TabItem("Sample"): |
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gr.Markdown( |
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""" |
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### Sample benchmark parameters |
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- `max_new_tokens = 128`; |
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- `temperature = 2.0`; |
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- `top_k = 50`; |
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- `pad_to_multiple_of = 64` for Tensorflow XLA models. Others do not pad (input prompts between 2 and 33 tokens). |
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""" |
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) |
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with gr.Row(): |
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model_selector = gr.Dropdown( |
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choices=["DistilGPT2", "GPT2", "OPT-1.3B", "GPTJ-6B", "T5 Small", "T5 Base", "T5 Large", "T5 3B"], |
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value="T5 Small", |
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label="Model", |
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interactive=True, |
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) |
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eager_enabler = gr.Radio( |
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["Yes", "No"], |
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value="Yes", |
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label="Plot TF Eager Execution?", |
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interactive=True |
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) |
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plot_fn = functools.partial(get_plot, generate_type="Sample") |
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plot = gr.Plot(value=plot_fn("T5 Small", "Yes")) |
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model_selector.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot) |
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eager_enabler.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot) |
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with gr.TabItem("Beam Search"): |
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gr.Markdown( |
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""" |
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### Beam Search benchmark parameters |
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- `max_new_tokens = 256`; |
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- `num_beams = 16`; |
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- `pad_to_multiple_of = 64` for Tensorflow XLA models. Others do not pad (input prompts between 2 and 33 tokens). |
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""" |
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) |
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with gr.Row(): |
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model_selector = gr.Dropdown( |
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choices=["DistilGPT2", "GPT2", "OPT-1.3B", "GPTJ-6B", "T5 Small", "T5 Base", "T5 Large", "T5 3B"], |
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value="T5 Small", |
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label="Model", |
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interactive=True, |
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) |
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eager_enabler = gr.Radio( |
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["Yes", "No"], |
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value="Yes", |
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label="Plot TF Eager Execution?", |
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interactive=True |
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) |
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plot_fn = functools.partial(get_plot, generate_type="Beam Search") |
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plot = gr.Plot(value=plot_fn("T5 Small", "Yes")) |
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model_selector.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot) |
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eager_enabler.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot) |
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with gr.TabItem("Benchmark Information"): |
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gr.Dataframe( |
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headers=["Parameter", "Value"], |
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value=[ |
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["Transformers Version", "4.22.dev0"], |
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["TensorFlow Version", "2.9.1"], |
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["Pytorch Version", "1.11.0"], |
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["OS", "22.04 LTS (3090) / Debian 10 (other GPUs)"], |
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["CUDA", "11.6 (3090) / 11.3 (others GPUs)"], |
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["Number of Runs", "100 (the first run was discarded to ignore compilation time)"], |
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["Is there code to reproduce?", "Yes -- https://gist.github.com/gante/f0017e3f13ac11b0c02e4e4db351f52f"], |
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], |
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) |
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demo.launch() |
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