minigpt4/common/config.py

"""
 Copyright (c) 2022, salesforce.com, inc.
 All rights reserved.
 SPDX-License-Identifier: BSD-3-Clause
 For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""

import logging
import json
from typing import Dict

from omegaconf import OmegaConf
from minigpt4.common.registry import registry


class Config:
    def __init__(self, args):
        self.config = {}

        self.args = args

        # Register the config and configuration for setup
        registry.register("configuration", self)

        user_config = self._build_opt_list(self.args.options)

        config = OmegaConf.load(self.args.cfg_path)

        runner_config = self.build_runner_config(config)
        model_config = self.build_model_config(config, **user_config)
        dataset_config = self.build_dataset_config(config)

        # Validate the user-provided runner configuration
        # model and dataset configuration are supposed to be validated by the respective classes
        # [TODO] validate the model/dataset configuration
        # self._validate_runner_config(runner_config)

        # Override the default configuration with user options.
        self.config = OmegaConf.merge(
            runner_config, model_config, dataset_config, user_config
        )

    def _validate_runner_config(self, runner_config):
        """
        This method validates the configuration, such that
            1) all the user specified options are valid;
            2) no type mismatches between the user specified options and the config.
        """
        runner_config_validator = create_runner_config_validator()
        runner_config_validator.validate(runner_config)

    def _build_opt_list(self, opts):
        opts_dot_list = self._convert_to_dot_list(opts)
        return OmegaConf.from_dotlist(opts_dot_list)

    @staticmethod
    def build_model_config(config, **kwargs):
        model = config.get("model", None)
        assert model is not None, "Missing model configuration file."

        model_cls = registry.get_model_class(model.arch)
        assert model_cls is not None, f"Model '{model.arch}' has not been registered."

        model_type = kwargs.get("model.model_type", None)
        if not model_type:
            model_type = model.get("model_type", None)
        # else use the model type selected by user.

        assert model_type is not None, "Missing model_type."

        print("--------------")
        print("model arch",model.arch)
        print("model cls",model_cls)

        model_config_path = model_cls.default_config_path(model_type=model_type)

        model_config = OmegaConf.create()
        # hierarchy override, customized config > default config
        model_config = OmegaConf.merge(
            model_config,
            OmegaConf.load(model_config_path),
            {"model": config["model"]},
        )

        return model_config

    @staticmethod
    def build_runner_config(config):
        return {"run": config.run}

    @staticmethod
    def build_dataset_config(config):
        datasets = config.get("datasets", None)
        if datasets is None:
            raise KeyError(
                "Expecting 'datasets' as the root key for dataset configuration."
            )

        dataset_config = OmegaConf.create()

        for dataset_name in datasets:

            print("dataset name", dataset_name)
            builder_cls = registry.get_builder_class(dataset_name)

            dataset_config_type = datasets[dataset_name].get("type", "default")
            dataset_config_path = builder_cls.default_config_path(
                type=dataset_config_type
            )

            # hierarchy override, customized config > default config
            dataset_config = OmegaConf.merge(
                dataset_config,
                OmegaConf.load(dataset_config_path),
                {"datasets": {dataset_name: config["datasets"][dataset_name]}},
            )

        return dataset_config

    def _convert_to_dot_list(self, opts):
        if opts is None:
            opts = []

        if len(opts) == 0:
            return opts

        has_equal = opts[0].find("=") != -1

        if has_equal:
            return opts

        return [(opt + "=" + value) for opt, value in zip(opts[0::2], opts[1::2])]

    def get_config(self):
        return self.config

    @property
    def run_cfg(self):
        return self.config.run

    @property
    def datasets_cfg(self):
        return self.config.datasets

    @property
    def model_cfg(self):
        return self.config.model

    def pretty_print(self):
        logging.info("\n=====  Running Parameters    =====")
        logging.info(self._convert_node_to_json(self.config.run))

        logging.info("\n======  Dataset Attributes  ======")
        datasets = self.config.datasets

        for dataset in datasets:
            if dataset in self.config.datasets:
                logging.info(f"\n======== {dataset} =======")
                dataset_config = self.config.datasets[dataset]
                logging.info(self._convert_node_to_json(dataset_config))
            else:
                logging.warning(f"No dataset named '{dataset}' in config. Skipping")

        logging.info(f"\n======  Model Attributes  ======")
        logging.info(self._convert_node_to_json(self.config.model))

    def _convert_node_to_json(self, node):
        container = OmegaConf.to_container(node, resolve=True)
        return json.dumps(container, indent=4, sort_keys=True)

    def to_dict(self):
        return OmegaConf.to_container(self.config)


def node_to_dict(node):
    return OmegaConf.to_container(node)


class ConfigValidator:
    """
    This is a preliminary implementation to centralize and validate the configuration.
    May be altered in the future.

    A helper class to validate configurations from yaml file.

    This serves the following purposes:
        1. Ensure all the options in the yaml are defined, raise error if not.
        2. when type mismatches are found, the validator will raise an error.
        3. a central place to store and display helpful messages for supported configurations.

    """

    class _Argument:
        def __init__(self, name, choices=None, type=None, help=None):
            self.name = name
            self.val = None
            self.choices = choices
            self.type = type
            self.help = help

        def __str__(self):
            s = f"{self.name}={self.val}"
            if self.type is not None:
                s += f", ({self.type})"
            if self.choices is not None:
                s += f", choices: {self.choices}"
            if self.help is not None:
                s += f", ({self.help})"
            return s

    def __init__(self, description):
        self.description = description

        self.arguments = dict()

        self.parsed_args = None

    def __getitem__(self, key):
        assert self.parsed_args is not None, "No arguments parsed yet."

        return self.parsed_args[key]

    def __str__(self) -> str:
        return self.format_help()

    def add_argument(self, *args, **kwargs):
        """
        Assume the first argument is the name of the argument.
        """
        self.arguments[args[0]] = self._Argument(*args, **kwargs)

    def validate(self, config=None):
        """
        Convert yaml config (dict-like) to list, required by argparse.
        """
        for k, v in config.items():
            assert (
                k in self.arguments
            ), f"""{k} is not a valid argument. Support arguments are {self.format_arguments()}."""

            if self.arguments[k].type is not None:
                try:
                    self.arguments[k].val = self.arguments[k].type(v)
                except ValueError:
                    raise ValueError(f"{k} is not a valid {self.arguments[k].type}.")

            if self.arguments[k].choices is not None:
                assert (
                    v in self.arguments[k].choices
                ), f"""{k} must be one of {self.arguments[k].choices}."""

        return config

    def format_arguments(self):
        return str([f"{k}" for k in sorted(self.arguments.keys())])

    def format_help(self):
        # description + key-value pair string for each argument
        help_msg = str(self.description)
        return help_msg + ", available arguments: " + self.format_arguments()

    def print_help(self):
        # display help message
        print(self.format_help())


def create_runner_config_validator():
    validator = ConfigValidator(description="Runner configurations")

    validator.add_argument(
        "runner",
        type=str,
        choices=["runner_base", "runner_iter"],
        help="""Runner to use. The "runner_base" uses epoch-based training while iter-based
            runner runs based on iters. Default: runner_base""",
    )
    # add argumetns for training dataset ratios
    validator.add_argument(
        "train_dataset_ratios",
        type=Dict[str, float],
        help="""Ratios of training dataset. This is used in iteration-based runner.
        Do not support for epoch-based runner because how to define an epoch becomes tricky.
        Default: None""",
    )
    validator.add_argument(
        "max_iters",
        type=float,
        help="Maximum number of iterations to run.",
    )
    validator.add_argument(
        "max_epoch",
        type=int,
        help="Maximum number of epochs to run.",
    )
    # add arguments for iters_per_inner_epoch
    validator.add_argument(
        "iters_per_inner_epoch",
        type=float,
        help="Number of iterations per inner epoch. This is required when runner is runner_iter.",
    )
    lr_scheds_choices = registry.list_lr_schedulers()
    validator.add_argument(
        "lr_sched",
        type=str,
        choices=lr_scheds_choices,
        help="Learning rate scheduler to use, from {}".format(lr_scheds_choices),
    )
    task_choices = registry.list_tasks()
    validator.add_argument(
        "task",
        type=str,
        choices=task_choices,
        help="Task to use, from {}".format(task_choices),
    )
    # add arguments for init_lr
    validator.add_argument(
        "init_lr",
        type=float,
        help="Initial learning rate. This will be the learning rate after warmup and before decay.",
    )
    # add arguments for min_lr
    validator.add_argument(
        "min_lr",
        type=float,
        help="Minimum learning rate (after decay).",
    )
    # add arguments for warmup_lr
    validator.add_argument(
        "warmup_lr",
        type=float,
        help="Starting learning rate for warmup.",
    )
    # add arguments for learning rate decay rate
    validator.add_argument(
        "lr_decay_rate",
        type=float,
        help="Learning rate decay rate. Required if using a decaying learning rate scheduler.",
    )
    # add arguments for weight decay
    validator.add_argument(
        "weight_decay",
        type=float,
        help="Weight decay rate.",
    )
    # add arguments for training batch size
    validator.add_argument(
        "batch_size_train",
        type=int,
        help="Training batch size.",
    )
    # add arguments for evaluation batch size
    validator.add_argument(
        "batch_size_eval",
        type=int,
        help="Evaluation batch size, including validation and testing.",
    )
    # add arguments for number of workers for data loading
    validator.add_argument(
        "num_workers",
        help="Number of workers for data loading.",
    )
    # add arguments for warm up steps
    validator.add_argument(
        "warmup_steps",
        type=int,
        help="Number of warmup steps. Required if a warmup schedule is used.",
    )
    # add arguments for random seed
    validator.add_argument(
        "seed",
        type=int,
        help="Random seed.",
    )
    # add arguments for output directory
    validator.add_argument(
        "output_dir",
        type=str,
        help="Output directory to save checkpoints and logs.",
    )
    # add arguments for whether only use evaluation
    validator.add_argument(
        "evaluate",
        help="Whether to only evaluate the model. If true, training will not be performed.",
    )
    # add arguments for splits used for training, e.g. ["train", "val"]
    validator.add_argument(
        "train_splits",
        type=list,
        help="Splits to use for training.",
    )
    # add arguments for splits used for validation, e.g. ["val"]
    validator.add_argument(
        "valid_splits",
        type=list,
        help="Splits to use for validation. If not provided, will skip the validation.",
    )
    # add arguments for splits used for testing, e.g. ["test"]
    validator.add_argument(
        "test_splits",
        type=list,
        help="Splits to use for testing. If not provided, will skip the testing.",
    )
    # add arguments for accumulating gradient for iterations
    validator.add_argument(
        "accum_grad_iters",
        type=int,
        help="Number of iterations to accumulate gradient for.",
    )

    # ====== distributed training ======
    validator.add_argument(
        "device",
        type=str,
        choices=["cpu", "cuda"],
        help="Device to use. Support 'cuda' or 'cpu' as for now.",
    )
    validator.add_argument(
        "world_size",
        type=int,
        help="Number of processes participating in the job.",
    )
    validator.add_argument("dist_url", type=str)
    validator.add_argument("distributed", type=bool)
    # add arguments to opt using distributed sampler during evaluation or not
    validator.add_argument(
        "use_dist_eval_sampler",
        type=bool,
        help="Whether to use distributed sampler during evaluation or not.",
    )

    # ====== task specific ======
    # generation task specific arguments
    # add arguments for maximal length of text output
    validator.add_argument(
        "max_len",
        type=int,
        help="Maximal length of text output.",
    )
    # add arguments for minimal length of text output
    validator.add_argument(
        "min_len",
        type=int,
        help="Minimal length of text output.",
    )
    # add arguments number of beams
    validator.add_argument(
        "num_beams",
        type=int,
        help="Number of beams used for beam search.",
    )

    # vqa task specific arguments
    # add arguments for number of answer candidates
    validator.add_argument(
        "num_ans_candidates",
        type=int,
        help="""For ALBEF and BLIP, these models first rank answers according to likelihood to select answer candidates.""",
    )
    # add arguments for inference method
    validator.add_argument(
        "inference_method",
        type=str,
        choices=["genearte", "rank"],
        help="""Inference method to use for question answering. If rank, requires a answer list.""",
    )

    # ====== model specific ======
    validator.add_argument(
        "k_test",
        type=int,
        help="Number of top k most similar samples from ITC/VTC selection to be tested.",
    )

    return validator