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import requests
# Utilities related to loading in and working with models/specific models
from urllib.parse import urlparse
import torch
from accelerate.commands.estimate import check_has_model, create_empty_model
from accelerate.utils import compute_module_sizes
from huggingface_hub.utils import GatedRepoError, RepositoryNotFoundError
def fetch_dictionary_content(model_id):
MODEL_URL = "https://huggingface.co/{model_id}/raw/main/config.json"
response = requests.get(MODEL_URL.format(model_id=model_id))
# Check if the request was successful
if response.status_code == 200:
return response.json() # Parse the JSON content into a Python dictionary
else:
return None
def load_parameter(model_dict, cand_keys):
for k in cand_keys:
if k in model_dict:
return model_dict[k]
return 0
# Reference: https://huggingface.co/spaces/hf-accelerate/model-memory-usage
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 RuntimeError(
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 RuntimeError(f"Model `{model_name}` was not found on the Hub, please try another model name.")
except ValueError:
raise RuntimeError(
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 RuntimeError(
f"Tried to load `{model_name}` with `{library}` but a possible model to load was not found inside the repo."
)
raise RuntimeError(
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 RuntimeError(
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
if __name__ == '__main__':
model = get_model('NousResearch/Nous-Hermes-Llama2-13b', None, None)
sizes = compute_module_sizes(model, dtype=torch.int8)
size_dict = {
'attn':0,
'mlp':0,
'embed':0,
}
for k, v in sizes.items():
for kk in size_dict:
if kk in k and 'weight' in k:
size_dict[kk] += v/1024**3
print(sizes) |