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| import gradio as gr | |
| import pandas as pd | |
| from hub_utils import check_for_discussion, report_results | |
| from model_utils import calculate_memory, get_model | |
| # We need to store them as globals because gradio doesn't have a way for us to pass them in to the button | |
| MODEL = None | |
| def get_results(model_name: str, library: str, options: list, access_token: str): | |
| global MODEL | |
| MODEL = get_model(model_name, library, access_token) | |
| has_discussion = check_for_discussion(model_name) | |
| title = f"## Memory usage for '{model_name}'" | |
| data = calculate_memory(MODEL, options) | |
| return [title, gr.update(visible=True, value=pd.DataFrame(data)), gr.update(visible=not has_discussion)] | |
| with gr.Blocks() as demo: | |
| with gr.Column(): | |
| gr.Markdown( | |
| """<img src="https://huggingface.co/spaces/hf-accelerate/model-memory-usage/resolve/main/measure_model_size.png" style="float: left;" width="250" height="250"><h1>🤗 Model Memory Calculator</h1> | |
| This tool will help you calculate how much vRAM is needed to train and perform big model inference | |
| on a model hosted on the 🤗 Hugging Face Hub. The minimum recommended vRAM needed for a model | |
| is denoted as the size of the "largest layer", and training of a model is roughly 4x its size (for Adam). | |
| These calculations are accurate within a few percent at most, such as `bert-base-cased` being 413.68 MB and the calculator estimating 413.18 MB. | |
| When performing inference, expect to add up to an additional 20% to this as found by [EleutherAI](https://blog.eleuther.ai/transformer-math/). | |
| More tests will be performed in the future to get a more accurate benchmark for each model. | |
| Currently this tool supports all models hosted that use `transformers` and `timm`. | |
| To use this tool pass in the URL or model name of the model you want to calculate the memory usage for, | |
| select which framework it originates from ("auto" will try and detect it from the model metadata), and | |
| what precisions you want to use.""" | |
| ) | |
| out_text = gr.Markdown() | |
| out = gr.DataFrame( | |
| headers=["dtype", "Largest Layer", "Total Size", "Training using Adam"], | |
| interactive=False, | |
| visible=False, | |
| ) | |
| with gr.Row(): | |
| inp = gr.Textbox(label="Model Name or URL", value="bert-base-cased") | |
| with gr.Row(): | |
| library = gr.Radio(["auto", "transformers", "timm"], label="Library", value="auto") | |
| options = gr.CheckboxGroup( | |
| ["float32", "float16/bfloat16", "int8", "int4"], | |
| value="float32", | |
| label="Model Precision", | |
| ) | |
| access_token = gr.Textbox(label="API Token", placeholder="Optional (for gated models)") | |
| with gr.Row(): | |
| btn = gr.Button("Calculate Memory Usage") | |
| post_to_hub = gr.Button( | |
| value="Report results in this model repo's discussions!\n(Will open in a new tab)", visible=False | |
| ) | |
| btn.click( | |
| get_results, | |
| inputs=[inp, library, options, access_token], | |
| outputs=[out_text, out, post_to_hub], | |
| ) | |
| post_to_hub.click(report_results, inputs=[inp, library, access_token]).then( | |
| lambda: gr.Button.update(visible=False), outputs=post_to_hub | |
| ) | |
| demo.launch() | |