utils.py

# 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, AutoTokenizer, PretrainedConfig

from .configuration_vila import VILAConfig
from .constants import MEDIA_TOKENS
from .tokenizer_utils import infer_stop_tokens


def load_tokenizer_then_handle_media_tokens_and_chat_template(
    model_name_or_path, config: VILAConfig, model_max_length=None
):
    tokenizer = AutoTokenizer.from_pretrained(
        osp.join(model_name_or_path, "llm"), padding_side="right", use_fast=True, legacy=False
    )
    if model_max_length is not None:
        tokenizer.model_max_length = model_max_length

    # Load chat template if specified.
    if getattr(config, "chat_template", None) is not None:
        print(f"Using chat template: {config.chat_template}")
        fpath = os.path.join(os.path.dirname(__file__), "chat_templates", f"{config.chat_template}.jinja")
        if not os.path.exists(fpath):
            fpath = os.path.join(model_name_or_path, f"{config.chat_template}.jinja")
        with open(fpath) as fd:
            chat_template = fd.read()
        tokenizer.chat_template = chat_template.replace("    ", "").replace("\n", "")

    # Set stop tokens for the tokenizer
    tokenizer.stop_tokens = infer_stop_tokens(tokenizer)
    tokenizer.stop_token_ids = tokenizer.convert_tokens_to_ids(tokenizer.stop_tokens)

    # Add media tokens to the tokenizer
    tokenizer.media_tokens = MEDIA_TOKENS
    tokenizer.media_token_ids = {}
    for name, token in MEDIA_TOKENS.items():
        tokenizer.add_tokens([token], special_tokens=True)
        tokenizer.media_token_ids[name] = tokenizer.convert_tokens_to_ids(token)

    return tokenizer


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 get_model_config_fp8(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)

    # fp8_llm
    key = "fp8_llm_cfg"
    directory_path = os.path.join(root_path, key[:-4])
    assert os.path.isdir(directory_path) and os.listdir(
        directory_path
    ), "You need to first convert the model weights to FP8 explicitly."
    return_list.append(directory_path)

    return return_list


def get_model_config_fp8(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)

    # fp8_llm
    key = "fp8_llm_cfg"
    directory_path = os.path.join(root_path, key[:-4])
    assert os.path.isdir(directory_path) and os.listdir(
        directory_path
    ), "You need to first convert the model weights to FP8 explicitly."
    return_list.append(directory_path)

    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)