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| # Utilities related to loading in and working with models/specific models | |
| from urllib.parse import urlparse | |
| import gradio as gr | |
| import torch | |
| from accelerate.commands.estimate import check_has_model, create_empty_model | |
| from accelerate.utils import calculate_maximum_sizes, convert_bytes | |
| from huggingface_hub.utils import GatedRepoError, RepositoryNotFoundError | |
| DTYPE_MODIFIER = {"float32": 1, "float16/bfloat16": 2, "int8": 4, "int4": 8} | |
| def extract_from_url(name: str): | |
| "Checks if `name` is a URL, and if so converts it to a model name" | |
| is_url = False | |
| try: | |
| result = urlparse(name) | |
| is_url = all([result.scheme, result.netloc]) | |
| except Exception: | |
| is_url = False | |
| # Pass through if not a URL | |
| if not is_url: | |
| return name | |
| else: | |
| path = result.path | |
| return path[1:] | |
| def translate_llama2(text): | |
| "Translates llama-2 to its hf counterpart" | |
| if not text.endswith("-hf"): | |
| return text + "-hf" | |
| return text | |
| def get_model(model_name: str, library: str, access_token: str): | |
| "Finds and grabs model from the Hub, and initializes on `meta`" | |
| if "meta-llama" in model_name: | |
| model_name = translate_llama2(model_name) | |
| if library == "auto": | |
| library = None | |
| model_name = extract_from_url(model_name) | |
| try: | |
| model = create_empty_model(model_name, library_name=library, trust_remote_code=True, access_token=access_token) | |
| except GatedRepoError: | |
| raise gr.Error( | |
| f"Model `{model_name}` is a gated model, please ensure to pass in your access token and try again if you have access. You can find your access token here : https://huggingface.co/settings/tokens. " | |
| ) | |
| except RepositoryNotFoundError: | |
| raise gr.Error(f"Model `{model_name}` was not found on the Hub, please try another model name.") | |
| except ValueError: | |
| raise gr.Error( | |
| f"Model `{model_name}` does not have any library metadata on the Hub, please manually select a library_name to use (such as `transformers`)" | |
| ) | |
| except (RuntimeError, OSError) as e: | |
| library = check_has_model(e) | |
| if library != "unknown": | |
| raise gr.Error( | |
| f"Tried to load `{model_name}` with `{library}` but a possible model to load was not found inside the repo." | |
| ) | |
| raise gr.Error( | |
| f"Model `{model_name}` had an error, please open a discussion on the model's page with the error message and name: `{e}`" | |
| ) | |
| except ImportError: | |
| # hacky way to check if it works with `trust_remote_code=False` | |
| model = create_empty_model( | |
| model_name, library_name=library, trust_remote_code=False, access_token=access_token | |
| ) | |
| except Exception as e: | |
| raise gr.Error( | |
| f"Model `{model_name}` had an error, please open a discussion on the model's page with the error message and name: `{e}`" | |
| ) | |
| return model | |
| def calculate_memory(model: torch.nn.Module, options: list): | |
| "Calculates the memory usage for a model init on `meta` device" | |
| total_size, largest_layer = calculate_maximum_sizes(model) | |
| data = [] | |
| for dtype in options: | |
| dtype_total_size = total_size | |
| dtype_largest_layer = largest_layer[0] | |
| modifier = DTYPE_MODIFIER[dtype] | |
| dtype_total_size /= modifier | |
| dtype_largest_layer /= modifier | |
| dtype_training_size = convert_bytes(dtype_total_size * 4) | |
| dtype_total_size = convert_bytes(dtype_total_size) | |
| dtype_largest_layer = convert_bytes(dtype_largest_layer) | |
| data.append( | |
| { | |
| "dtype": dtype, | |
| "Largest Layer or Residual Group": dtype_largest_layer, | |
| "Total Size": dtype_total_size, | |
| "Training using Adam": dtype_training_size, | |
| } | |
| ) | |
| return data | |