peft/method_comparison/MetaMathQA/utils.py

# Copyright 2025-present the HuggingFace Inc. team.
#
# 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.

"""
All utilities not related to data handling.
"""

import enum
import json
import os
import platform
import subprocess
import tempfile
import warnings
from dataclasses import asdict, dataclass
from decimal import Decimal, DivisionByZero, InvalidOperation
from typing import Any, Callable, Literal, Optional

import bitsandbytes
import datasets
import huggingface_hub
import numpy as np
import torch
import transformers
from torch import nn
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    get_cosine_schedule_with_warmup,
)

import peft
from peft import PeftConfig, get_peft_model, prepare_model_for_kbit_training
from peft.optimizers import create_lorafa_optimizer, create_loraplus_optimizer
from peft.utils import infer_device, SAFETENSORS_WEIGHTS_NAME

device = infer_device()

if device not in ["cuda", "xpu"]:
    raise RuntimeError("CUDA or XPU is not available, currently only CUDA or XPU is supported")

ACCELERATOR_MEMORY_INIT_THRESHOLD = 500 * 2**20  # 500MB
FILE_NAME_DEFAULT_TRAIN_PARAMS = os.path.join(os.path.dirname(__file__), "default_training_params.json")
FILE_NAME_TRAIN_PARAMS = "training_params.json"  # specific params for this experiment
# main results
RESULT_PATH = os.path.join(os.path.dirname(__file__), "results")
# testing results
RESULT_PATH_TEST = os.path.join(os.path.dirname(__file__), "temporary_results")
# cancelled results
RESULT_PATH_CANCELLED = os.path.join(os.path.dirname(__file__), "cancelled_results")
hf_api = huggingface_hub.HfApi()
WARMUP_STEP_RATIO = 0.1


@dataclass
class TrainConfig:
    """All configuration parameters associated with training the model

    Args:
        model_id: The model identifier
        dtype: The data type to use for the model
        max_seq_length: The maximum sequence length
        batch_size: The batch size for training
        batch_size_eval: The batch size for eval/test, can be much higher than for training
        max_steps: The maximum number of steps to train for
        eval_steps: The number of steps between evaluations
        compile: Whether to compile the model
        query_template: The template for the query
        seed: The random seed
        grad_norm_clip: The gradient norm clipping value (set to 0 to skip)
        optimizer_type: The name of a torch optimizer (e.g. AdamW) or a PEFT method ("lora+", "lora-fa")
        optimizer_kwargs: The optimizer keyword arguments (lr etc.)
        lr_scheduler: The learning rate scheduler (currently only None or 'cosine' are supported)
        use_amp: Whether to use automatic mixed precision
        autocast_adapter_dtype: Whether to cast adapter dtype to float32, same argument as in PEFT
        generation_kwargs: Arguments passed to transformers GenerationConfig (used in evaluation)
        attn_implementation: The attention implementation to use (if any), see transformers docs
    """

    model_id: str
    dtype: Literal["float32", "float16", "bfloat16", "int8", "int4"]
    max_seq_length: int
    batch_size: int
    batch_size_eval: int
    max_steps: int
    eval_steps: int
    compile: bool
    query_template: str
    seed: int
    grad_norm_clip: float  # set to 0 to skip
    optimizer_type: str
    optimizer_kwargs: dict[str, Any]
    lr_scheduler: Optional[Literal["cosine"]]
    use_amp: bool
    autocast_adapter_dtype: bool
    generation_kwargs: dict[str, Any]
    attn_implementation: Optional[str]

    def __post_init__(self) -> None:
        if not isinstance(self.model_id, str):
            raise ValueError(f"Invalid model_id: {self.model_id}")
        if self.dtype not in ["float32", "float16", "bfloat16", "int8", "int4"]:
            raise ValueError(f"Invalid dtype: {self.dtype}")
        if self.max_seq_length < 0:
            raise ValueError(f"Invalid max_seq_length: {self.max_seq_length}")
        if self.batch_size <= 0:
            raise ValueError(f"Invalid batch_size: {self.batch_size}")
        if self.batch_size_eval <= 0:
            raise ValueError(f"Invalid eval batch_size: {self.batch_size_eval}")
        if self.max_steps <= 0:
            raise ValueError(f"Invalid max_steps: {self.max_steps}")
        if self.eval_steps <= 0:
            raise ValueError(f"Invalid eval_steps: {self.eval_steps}")
        if self.eval_steps > self.max_steps:
            raise ValueError(f"Invalid eval_steps: {self.eval_steps} > max_steps: {self.max_steps}")
        if self.grad_norm_clip < 0:
            raise ValueError(f"Invalid grad_norm_clip: {self.grad_norm_clip}")
        if self.optimizer_type not in ["lora+", "lora-fa"] and not hasattr(torch.optim, self.optimizer_type):
            raise ValueError(f"Invalid optimizer_type: {self.optimizer_type}")
        if self.lr_scheduler not in [None, "cosine"]:
            raise ValueError(f"Invalid lr_scheduler: {self.lr_scheduler}, must be None or 'cosine'")
        if "{query}" not in self.query_template:
            raise ValueError("Invalid query_template, must contain '{query}'")


def validate_experiment_path(path: str) -> str:
    # the experiment path should take the form of ./experiments/<peft-method>/<experiment-name>
    # e.g. ./experiments/lora/rank32
    # it should contain:
    # - adapter_config.json
    # - optional: training_params.json
    if not os.path.exists(FILE_NAME_DEFAULT_TRAIN_PARAMS):
        raise FileNotFoundError(
            f"Missing default training params file '{FILE_NAME_DEFAULT_TRAIN_PARAMS}' in the ./experiments directory"
        )
    if not os.path.exists(path):
        raise FileNotFoundError(f"Path {path} does not exist")

    # check path structure
    path_parts = path.rstrip(os.path.sep).split(os.path.sep)
    if (len(path_parts) != 3) or (path_parts[-3] != "experiments"):
        raise ValueError(
            f"Path {path} does not have the correct structure, should be ./experiments/<peft-method>/<experiment-name>"
        )

    experiment_name = os.path.join(*path_parts[-2:])
    return experiment_name


def get_train_config(path: str) -> TrainConfig:
    # first, load the default params, then update with experiment-specific params
    with open(FILE_NAME_DEFAULT_TRAIN_PARAMS) as f:
        default_config_kwargs = json.load(f)

    config_kwargs = {}
    if os.path.exists(path):
        with open(path) as f:
            config_kwargs = json.load(f)

    config_kwargs = {**default_config_kwargs, **config_kwargs}
    return TrainConfig(**config_kwargs)


def init_accelerator() -> int:
    torch_accelerator_module = getattr(torch, device, torch.cuda)
    torch.manual_seed(0)
    torch_accelerator_module.reset_peak_memory_stats()
    torch_accelerator_module.manual_seed_all(0)
    # might not be necessary, but just to be sure
    nn.Linear(1, 1).to(device)

    accelerator_memory_init = torch_accelerator_module.max_memory_reserved()
    if accelerator_memory_init > ACCELERATOR_MEMORY_INIT_THRESHOLD:
        raise RuntimeError(
            f"{device} memory usage at start is too high: {accelerator_memory_init // 2**20}MB, please ensure that no other "
            f"processes are running on {device}."
        )

    torch_accelerator_module.reset_peak_memory_stats()
    accelerator_memory_init = torch_accelerator_module.max_memory_reserved()
    return accelerator_memory_init


def get_tokenizer(*, model_id: str, max_seq_length: int):
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    tokenizer.model_max_length = max_seq_length
    if not tokenizer.pad_token:
        tokenizer.pad_token = tokenizer.eos_token
    return tokenizer


def get_base_model(
    *,
    model_id: str,
    dtype: Literal["float32", "float16", "bfloat16", "int8", "int4"],
    compile: bool,
    attn_implementation: Optional[str],
) -> nn.Module:
    kwargs: dict[str, Any] = {
        "pretrained_model_name_or_path": model_id,
        "device_map": device,
        "attn_implementation": attn_implementation,
    }
    if dtype == "int4":
        quant_config = BitsAndBytesConfig(load_in_4bit=True)
        kwargs["quantization_config"] = quant_config
    elif dtype == "int8":
        quant_config = BitsAndBytesConfig(load_in_8bit=True)
        kwargs["quantization_config"] = quant_config
    elif dtype == "bfloat16":
        kwargs["torch_dtype"] = torch.bfloat16
    elif dtype == "float16":
        kwargs["torch_dtype"] = torch.float16
    elif dtype != "float32":
        raise ValueError(f"Invalid dtype: {dtype}")

    model = AutoModelForCausalLM.from_pretrained(**kwargs)

    if dtype in ["int8", "int4"]:
        model = prepare_model_for_kbit_training(model)

    if compile:
        model = torch.compile(model)

    return model


def get_model(
    *,
    model_id: str,
    dtype: Literal["float32", "float16", "bfloat16", "int8", "int4"],
    compile: bool,
    attn_implementation: Optional[str],
    peft_config: Optional[PeftConfig],
    autocast_adapter_dtype: bool,
) -> nn.Module:
    base_model = get_base_model(
        model_id=model_id, dtype=dtype, compile=compile, attn_implementation=attn_implementation
    )
    if peft_config is None:
        model = base_model
    else:
        model = get_peft_model(base_model, peft_config, autocast_adapter_dtype=autocast_adapter_dtype)
    return model


class DummyScheduler:
    # if no lr scheduler is being used
    def __init__(self, lr):
        self.lr = lr

    def get_last_lr(self):
        return [self.lr]

    def step(self):
        pass


def get_optimizer_and_scheduler(
    model, *, optimizer_type: str, max_steps: int, lr_scheduler_arg: Optional[Literal["cosine"]], **optimizer_kwargs
) -> tuple[torch.optim.Optimizer, Any]:
    if optimizer_type == "lora+":
        optimizer = create_loraplus_optimizer(model, optimizer_cls=torch.optim.AdamW, **optimizer_kwargs)
    elif optimizer_type == "lora-fa":
        optimizer = create_lorafa_optimizer(model, **optimizer_kwargs)
    else:
        cls = getattr(torch.optim, optimizer_type)
        optimizer = cls(model.parameters(), **optimizer_kwargs)

    if lr_scheduler_arg == "cosine":
        warmup_steps = int(WARMUP_STEP_RATIO * max_steps)
        lr_scheduler = get_cosine_schedule_with_warmup(
            optimizer, num_warmup_steps=warmup_steps, num_training_steps=max_steps
        )
    elif lr_scheduler_arg is None:
        lr_scheduler = DummyScheduler(optimizer_kwargs["lr"])
    else:
        raise ValueError(f"Invalid lr_scheduler argument: {lr_scheduler_arg}")

    return optimizer, lr_scheduler


class BucketIterator:
    """
    Iterator that yields batches of data from a torch Dataset, grouped in buckets by sequence length

    The iterator will yield batches of size `batch_size`, where the samples in each batch are sorted by sequence length.
    This is done to minimize the amount of padding required for each batch. To avoid sorting the entire dataset and thus
    introducing a bias, the dataset is first split into buckets of size `batch_size * bucket_factor`.

    Args:
        ds: The torch Dataset to iterate over
        batch_size: The batch size
        bucket_factor: The factor by which to multiply the batch size to determine the bucket size
        delete_cols: The columns to delete from the dataset before yielding a batch
    """

    def __init__(self, ds, *, batch_size: int, bucket_factor: int, delete_cols: list[str]) -> None:
        self.ds = ds
        self.batch_size = batch_size
        self.bucket_factor = bucket_factor
        self.delete_cols = set(delete_cols)

        assert self.bucket_factor > 0, "bucket_factor must be greater than 0"

    def _batch_iterator(self, bucket):
        tokens_per_sample_bucket = torch.tensor([len(i) for i in bucket["input_ids"]])
        # sort long to short instead to encounter possible OOM errors as early as possible
        sorted = torch.argsort(tokens_per_sample_bucket, descending=True)
        cls = type(bucket)  # conserve the type returned by the ds
        bucket = {k: [v[i] for i in sorted] for k, v in bucket.items() if k not in self.delete_cols}
        num_samples = len(bucket["input_ids"])
        for j in range(0, num_samples, self.batch_size):
            batch = {k: v[j : j + self.batch_size] for k, v in bucket.items()}
            yield cls(batch)

    def __iter__(self):
        bucket_size = self.batch_size * self.bucket_factor
        for i in range(0, len(self.ds), bucket_size):
            bucket = self.ds[i : i + bucket_size]
            yield from self._batch_iterator(bucket)

        # if there is a remainder, we yield the last batch
        if len(self.ds) % bucket_size != 0:
            bucket = self.ds[-(len(self.ds) % bucket_size) :]
            yield from self._batch_iterator(bucket)


def get_file_size(
    model: nn.Module, *, peft_config: Optional[PeftConfig], clean: bool, print_fn: Callable[..., None]
) -> int:
    file_size = 99999999  # set a default dummy value
    if peft_config is not None:
        try:
            with tempfile.TemporaryDirectory(ignore_cleanup_errors=True, delete=clean) as tmp_dir:
                model.save_pretrained(tmp_dir)
                stat = os.stat(os.path.join(tmp_dir, SAFETENSORS_WEIGHTS_NAME))
                file_size = stat.st_size
                if not clean:
                    print_fn(f"Saved PEFT checkpoint to {tmp_dir}")
        except Exception as exc:
            print(f"Failed to save PEFT checkpoint due to the following error: {exc}")
    else:
        print_fn("Not saving the fully fine-tuned model because it's too big, estimating the size instead")
        try:
            num_params = model.num_parameters()
            dtype_size = next(model.parameters()).element_size()
            file_size = num_params * dtype_size
        except Exception as exc:
            print(f"Failed to determine file size for fully finetuned model because of: {exc}")
    return file_size


##################
# ANSWER PARSING #
##################


def parse_answer(text: str) -> Optional[str]:
    """
    A label/prediction can look like this:

    Question: If the magnitude of vector v is equal to 4, what is the dot product of vector v with itself?. Think step
    by step
    Answer: The dot product of a vector with itself is equal to the square of its magnitude. So, the dot product of
    vector v with itself is equal to $4^2 = \boxed{16}$.The answer is: 16

    We want to extract '16' from this string.

    """
    # This implementation is based on sampling meta-llama/Llama-3.1-8B-Instruct. It may not work for other models.
    candidate_delimiters = [
        # MetaMath:
        "The answer is: ",
        "The answer is ",
        "The final answer is: ",
        "The final answer is ",
        # GSM8K:
        "#### ",
    ]
    text = text.strip()
    text = text.rstrip(".!?")
    for delimiter in candidate_delimiters:
        if delimiter in text:
            break
    else:  # no match
        return None

    text = text.rpartition(delimiter)[-1].strip()
    # if a new paragraph follows after the final answer, we want to remove it
    text = text.split("\n", 1)[0]
    # note: we can just remove % here since the GSM8K dataset just omits it, i.e. 50% -> 50, no need to divide by 100
    text = text.strip(" .!?$%")
    return text


def convert_to_decimal(s: Optional[str]) -> Optional[Decimal]:
    """
    Converts a string representing a number to a Decimal.

    The string may be:
      - A simple number (e.g., "13", "65.33")
      - A fraction (e.g., "20/14")
    """
    if s is None:
        return None

    try:
        s = s.strip()
        # Check if the string represents a fraction.
        if "/" in s:
            parts = s.split("/")
            if len(parts) != 2:
                return None
            numerator = Decimal(parts[0].strip())
            denominator = Decimal(parts[1].strip())
            if denominator == 0:
                return None
            value = numerator / denominator
        else:
            # Parse as a regular decimal or integer string.
            value = Decimal(s)
        return value
    except (DivisionByZero, InvalidOperation, ValueError):
        return None


def get_accuracy(*, predictions: list[str], responses: list[str]) -> float:
    if len(predictions) != len(responses):
        raise ValueError(f"Prediction length mismatch: {len(predictions)} != {len(responses)}")

    y_true: list[str | float | None] = []
    y_pred: list[str | float | None] = []

    for prediction, response in zip(predictions, responses):
        parsed_prediction = parse_answer(prediction)
        parsed_response = parse_answer(response)
        if parsed_response is None:
            raise ValueError(f"Error encountered while trying to parse response: {response}")

        decimal_prediction = convert_to_decimal(parsed_prediction)
        decimal_answer = convert_to_decimal(parsed_response)
        if decimal_prediction is not None:
            y_pred.append(float(decimal_prediction))
        elif parsed_prediction is not None:
            y_pred.append(parsed_prediction)
        else:
            y_pred.append(None)

        # we convert decimals to float so that stuff like this works:
        # float(convert_to_decimal('20/35')) == float(convert_to_decimal('0.5714285714285714'))
        if decimal_answer is not None:
            y_true.append(float(decimal_answer))
        elif parsed_prediction is not None:
            y_true.append(parsed_response)
        else:
            y_true.append(None)

    correct: list[bool] = []
    for true, pred in zip(y_true, y_pred):
        if (true is not None) and (pred is not None):
            correct.append(true == pred)
        else:
            correct.append(False)

    accuracy = sum(correct) / len(correct)
    return accuracy


###########
# LOGGING #
###########


def get_base_model_info(model_id: str) -> Optional[huggingface_hub.ModelInfo]:
    try:
        return hf_api.model_info(model_id)
    except Exception as exc:
        warnings.warn(f"Could not retrieve model info, failed with error {exc}")
        return None


def get_dataset_info(dataset_id: str) -> Optional[huggingface_hub.DatasetInfo]:
    try:
        return hf_api.dataset_info(dataset_id)
    except Exception as exc:
        warnings.warn(f"Could not retrieve dataset info, failed with error {exc}")
        return None


def get_git_hash(module) -> Optional[str]:
    if "site-packages" in module.__path__[0]:
        return None

    return subprocess.check_output("git rev-parse HEAD".split(), cwd=os.path.dirname(module.__file__)).decode().strip()


def get_package_info() -> dict[str, Optional[str]]:
    """Get the package versions and commit hashes of transformers, peft, datasets, bnb, and torch"""
    package_info = {
        "transformers-version": transformers.__version__,
        "transformers-commit-hash": get_git_hash(transformers),
        "peft-version": peft.__version__,
        "peft-commit-hash": get_git_hash(peft),
        "datasets-version": datasets.__version__,
        "datasets-commit-hash": get_git_hash(datasets),
        "bitsandbytes-version": bitsandbytes.__version__,
        "bitsandbytes-commit-hash": get_git_hash(bitsandbytes),
        "torch-version": torch.__version__,
        "torch-commit-hash": get_git_hash(torch),
    }
    return package_info


def get_system_info() -> dict[str, str]:
    device = infer_device()
    torch_accelerator_module = getattr(torch, device, torch.cuda)
    system_info = {
        "system": platform.system(),
        "release": platform.release(),
        "version": platform.version(),
        "machine": platform.machine(),
        "processor": platform.processor(),
        "accelerator": torch_accelerator_module.get_device_name(0),
    }
    return system_info


@dataclass
class MetaInfo:
    package_info: dict[str, Optional[str]]
    system_info: dict[str, str]
    pytorch_info: str


def get_meta_info() -> MetaInfo:
    meta_info = MetaInfo(
        package_info=get_package_info(),
        system_info=get_system_info(),
        pytorch_info=torch.__config__.show(),
    )
    return meta_info


def get_peft_branch() -> str:
    return (
        subprocess.check_output("git rev-parse --abbrev-ref HEAD".split(), cwd=os.path.dirname(peft.__file__))
        .decode()
        .strip()
    )


class TrainStatus(enum.Enum):
    FAILED = "failed"
    SUCCESS = "success"
    CANCELED = "canceled"


@dataclass
class TrainResult:
    status: TrainStatus
    train_time: float
    accelerator_memory_reserved_log: list[int]
    losses: list[float]
    metrics: list[Any]  # TODO
    error_msg: str
    num_trainable_params: int
    num_total_params: int


def log_to_console(log_data: dict[str, Any], print_fn: Callable[..., None]) -> None:
    accelerator_memory_max = log_data["train_info"]["accelerator_memory_max"]
    accelerator_memory_avg = log_data["train_info"]["accelerator_memory_reserved_avg"]
    accelerator_memory_reserved_99th = log_data["train_info"]["accelerator_memory_reserved_99th"]
    time_train = log_data["train_info"]["train_time"]
    time_total = log_data["run_info"]["total_time"]
    file_size = log_data["train_info"]["file_size"]

    print_fn(f"accelerator memory max: {accelerator_memory_max // 2**20}MB")
    print_fn(f"accelerator memory reserved avg: {accelerator_memory_avg // 2**20}MB")
    print_fn(f"accelerator memory reserved 99th percentile: {accelerator_memory_reserved_99th // 2**20}MB")
    print_fn(f"train time: {time_train}s")
    print_fn(f"total time: {time_total:.2f}s")
    print_fn(f"file size of checkpoint: {file_size / 2**20:.1f}MB")


def log_to_file(
    *, log_data: dict, save_dir: str, experiment_name: str, timestamp: str, print_fn: Callable[..., None]
) -> None:
    if save_dir.endswith(RESULT_PATH):
        file_name = f"{experiment_name.replace(os.path.sep, '--')}.json"
    else:
        # For cancelled and temporary runs, we want to include the timestamp, as these runs are not tracked in git, thus
        # we need unique names to avoid losing history.
        file_name = f"{experiment_name.replace(os.path.sep, '--')}--{timestamp.replace(':', '-')}.json"
    file_name = os.path.join(save_dir, file_name)
    with open(file_name, "w") as f:
        json.dump(log_data, f, indent=2)
    print_fn(f"Saved log to: {file_name}")


def log_results(
    *,
    experiment_name: str,
    train_result: TrainResult,
    accelerator_memory_init: int,
    time_total: float,
    file_size: int,
    model_info: Optional[huggingface_hub.ModelInfo],
    datasets_info: dict[str, Optional[huggingface_hub.DatasetInfo]],
    start_date: str,
    train_config: TrainConfig,
    peft_config: Optional[PeftConfig],
    print_fn: Callable[..., None],
) -> None:
    # collect results
    device = infer_device()
    torch_accelerator_module = getattr(torch, device, torch.cuda)
    accelerator_memory_final = torch_accelerator_module.max_memory_reserved()
    accelerator_memory_avg = int(
        sum(train_result.accelerator_memory_reserved_log) / len(train_result.accelerator_memory_reserved_log)
    )
    accelerator_memory_reserved_99th = int(np.percentile(train_result.accelerator_memory_reserved_log, 99))

    meta_info = get_meta_info()
    if model_info is not None:
        model_sha = model_info.sha
        model_created_at = model_info.created_at.isoformat()
    else:
        model_sha = None
        model_created_at = None

    dataset_info_log = {}
    for key, dataset_info in datasets_info.items():
        if dataset_info is not None:
            dataset_sha = dataset_info.sha
            dataset_created_at = dataset_info.created_at.isoformat()
        else:
            dataset_sha = None
            dataset_created_at = None
        dataset_info_log[key] = {"sha": dataset_sha, "created_at": dataset_created_at}

    peft_branch = get_peft_branch()

    if train_result.status == TrainStatus.CANCELED:
        save_dir = RESULT_PATH_CANCELLED
        print_fn("Experiment run was categorized as canceled")
    elif peft_branch != "main":
        save_dir = RESULT_PATH_TEST
        print_fn(f"Experiment run was categorized as a test run on branch {peft_branch}")
    elif train_result.status == TrainStatus.SUCCESS:
        save_dir = RESULT_PATH
        print_fn("Experiment run was categorized as successful run")
    else:
        save_dir = tempfile.mkdtemp()
        print_fn(f"Experiment could not be categorized, writing results to {save_dir}. Please open an issue on PEFT.")

    if peft_config is None:
        peft_config_dict: Optional[dict[str, Any]] = None
    else:
        peft_config_dict = peft_config.to_dict()
        for key, value in peft_config_dict.items():
            if isinstance(value, set):
                peft_config_dict[key] = list(value)

    log_data = {
        "run_info": {
            "created_at": start_date,
            "total_time": time_total,
            "experiment_name": experiment_name,
            "peft_branch": peft_branch,
            "train_config": asdict(train_config),
            "peft_config": peft_config_dict,
            "error_msg": train_result.error_msg,
        },
        "train_info": {
            "accelerator_memory_reserved_avg": accelerator_memory_avg,
            "accelerator_memory_max": (accelerator_memory_final - accelerator_memory_init),
            "accelerator_memory_reserved_99th": accelerator_memory_reserved_99th,
            "train_time": train_result.train_time,
            "file_size": file_size,
            "num_trainable_params": train_result.num_trainable_params,
            "num_total_params": train_result.num_total_params,
            "status": train_result.status.value,
            "metrics": train_result.metrics,
        },
        "meta_info": {
            "model_info": {"sha": model_sha, "created_at": model_created_at},
            "dataset_info": dataset_info_log,
            **asdict(meta_info),
        },
    }

    log_to_console(log_data, print_fn=print)  # use normal print to be able to redirect if so desired
    log_to_file(
        log_data=log_data, save_dir=save_dir, experiment_name=experiment_name, timestamp=start_date, print_fn=print_fn
    )