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