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| import random | |
| import gradio as gr | |
| from optimum_benchmark.task_utils import ( | |
| TASKS_TO_AUTOMODELS, | |
| infer_task_from_model_name_or_path, | |
| ) | |
| from run import run_benchmark | |
| from configs import ( | |
| get_training_config, | |
| get_inference_config, | |
| get_neural_compressor_config, | |
| get_onnxruntime_config, | |
| get_openvino_config, | |
| get_pytorch_config, | |
| ) | |
| BACKENDS = ["pytorch", "onnxruntime", "openvino", "neural-compressor"] | |
| BENCHMARKS = ["inference", "training"] | |
| DEVICES = ["cpu", "cuda"] | |
| with gr.Blocks() as demo: | |
| # title text | |
| gr.HTML("<h1 style='text-align: center'>🤗 Optimum-Benchmark UI 🏋️</h1>") | |
| # explanation text | |
| gr.Markdown( | |
| "This is a demo space of [Optimum-Benchmark](https://github.com/huggingface/optimum-benchmark.git):" | |
| "<br>A unified multi-backend utility for benchmarking `transformers`, `diffusers`, `peft` and `timm` models with " | |
| "Optimum's optimizations & quantization, for inference & training, on different backends & hardwares." | |
| ) | |
| model = gr.Textbox( | |
| label="model", | |
| value="bert-base-uncased", | |
| ) | |
| task = gr.Dropdown( | |
| label="task", | |
| value="text-classification", | |
| choices=list(TASKS_TO_AUTOMODELS.keys()), | |
| ) | |
| device = gr.Dropdown( | |
| value="cpu", | |
| label="device", | |
| choices=DEVICES, | |
| ) | |
| experiment = gr.Textbox( | |
| label="experiment_name", | |
| value=f"experiment_{random.getrandbits(16)}", | |
| ) | |
| model.submit(fn=infer_task_from_model_name_or_path, inputs=model, outputs=task) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| backend = gr.Dropdown( | |
| label="backend", | |
| choices=BACKENDS, | |
| value=BACKENDS[0], | |
| ) | |
| with gr.Row() as backend_configs: | |
| with gr.Accordion(label="Pytorch Config", open=False, visible=True): | |
| pytorch_config = get_pytorch_config() | |
| with gr.Accordion(label="OnnxRunTime Config", open=False, visible=False): | |
| onnxruntime_config = get_onnxruntime_config() | |
| with gr.Accordion(label="OpenVINO Config", open=False, visible=False): | |
| openvino_config = get_openvino_config() | |
| with gr.Accordion(label="Neural Compressor Config", open=False, visible=False): | |
| neural_compressor_config = get_neural_compressor_config() | |
| # hide backend configs based on backend | |
| backend.change( | |
| inputs=backend, | |
| outputs=backend_configs.children, | |
| fn=lambda value: [gr.update(visible=value == key) for key in BACKENDS], | |
| ) | |
| with gr.Column(): | |
| with gr.Row(): | |
| benchmark = gr.Dropdown( | |
| label="benchmark", | |
| choices=BENCHMARKS, | |
| value=BENCHMARKS[0], | |
| ) | |
| with gr.Row() as benchmark_configs: | |
| with gr.Accordion(label="Inference Config", open=False, visible=True): | |
| inference_config = get_inference_config() | |
| with gr.Accordion(label="Training Config", open=False, visible=False): | |
| training_config = get_training_config() | |
| # hide benchmark configs based on benchmark | |
| benchmark.change( | |
| inputs=benchmark, | |
| outputs=benchmark_configs.children, | |
| fn=lambda value: [gr.update(visible=value == key) for key in BENCHMARKS], | |
| ) | |
| button = gr.Button(value="Run Benchmark", variant="primary") | |
| with gr.Accordion(label="", open=True): | |
| html_output = gr.HTML() | |
| table_output = gr.Dataframe(visible=False) | |
| button.click( | |
| fn=run_benchmark, | |
| inputs={ | |
| experiment, | |
| model, | |
| task, | |
| device, | |
| backend, | |
| benchmark, | |
| *pytorch_config, | |
| *openvino_config, | |
| *onnxruntime_config, | |
| *neural_compressor_config, | |
| *inference_config, | |
| *training_config, | |
| }, | |
| outputs=[html_output, button, table_output], | |
| queue=True, | |
| ) | |
| demo.queue().launch() | |