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# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
# This file is modified from https://github.com/haotian-liu/LLaVA/
import os
import os.path as osp
from huggingface_hub import repo_exists, snapshot_download
from huggingface_hub.utils import HFValidationError, validate_repo_id
from transformers import AutoConfig, PretrainedConfig
def get_model_config(config):
default_keys = ["llm_cfg", "vision_tower_cfg", "mm_projector_cfg"]
if hasattr(config, "_name_or_path") and len(config._name_or_path) >= 2:
root_path = config._name_or_path
else:
root_path = config.resume_path
# download from huggingface
if root_path is not None and not osp.exists(root_path):
try:
valid_hf_repo = repo_exists(root_path)
except HFValidationError as e:
valid_hf_repo = False
if valid_hf_repo:
root_path = snapshot_download(root_path)
return_list = []
for key in default_keys:
cfg = getattr(config, key, None)
if isinstance(cfg, dict):
try:
return_list.append(os.path.join(root_path, key[:-4]))
except:
raise ValueError(f"Cannot find resume path in config for {key}!")
elif isinstance(cfg, PretrainedConfig):
return_list.append(os.path.join(root_path, key[:-4]))
elif isinstance(cfg, str):
return_list.append(cfg)
return return_list
def is_mm_model(model_path):
"""
Check if the model at the given path is a visual language model.
Args:
model_path (str): The path to the model.
Returns:
bool: True if the model is an MM model, False otherwise.
"""
config = AutoConfig.from_pretrained(model_path)
architectures = config.architectures
for architecture in architectures:
if "llava" in architecture.lower():
return True
return False
def auto_upgrade(config):
cfg = AutoConfig.from_pretrained(config)
if "llava" in config and "llava" not in cfg.model_type:
assert cfg.model_type == "llama"
print(
"You are using newer LLaVA code base, while the checkpoint of v0 is from older code base."
)
print(
"You must upgrade the checkpoint to the new code base (this can be done automatically)."
)
confirm = input("Please confirm that you want to upgrade the checkpoint. [Y/N]")
if confirm.lower() in ["y", "yes"]:
print("Upgrading checkpoint...")
assert len(cfg.architectures) == 1
setattr(cfg.__class__, "model_type", "llava")
cfg.architectures[0] = "LlavaLlamaForCausalLM"
cfg.save_pretrained(config)
print("Checkpoint upgraded.")
else:
print("Checkpoint upgrade aborted.")
exit(1)
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