initial clean commit

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# Copyright 2024 Bytedance Ltd. and/or its 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.
"""
A unified tracking interface that supports logging data to different backend
"""

import dataclasses
import json
import logging
import os
from enum import Enum
from functools import partial
from pathlib import Path
from typing import Any

import orjson

logger = logging.getLogger(__name__)

MLFLOW_MAX_ATTEMPTS = 3
MLFLOW_SLEEP_SECONDS = 5


class Tracking:
    """A unified tracking interface for logging experiment data to multiple backends.
    This class provides a centralized way to log experiment metrics, parameters, and artifacts
    to various tracking backends including WandB, MLflow, SwanLab, TensorBoard, and console.
    Attributes:
        supported_backend: List of supported tracking backends.
        logger: Dictionary of initialized logger instances for each backend.
    """

    supported_backend = [
        "wandb",
        "mlflow",
        "swanlab",
        "vemlp_wandb",
        "tensorboard",
        "console",
        "clearml",
        "trackio",
        "file",
    ]

    def __init__(self, project_name, experiment_name, default_backend: str | list[str] = "console", config=None):
        if isinstance(default_backend, str):
            default_backend = [default_backend]
        for backend in default_backend:
            if backend == "tracking":
                import warnings

                warnings.warn("`tracking` logger is deprecated. use `wandb` instead.", DeprecationWarning, stacklevel=2)
            else:
                assert backend in self.supported_backend, f"{backend} is not supported"

        self.logger = {}

        if "tracking" in default_backend or "wandb" in default_backend:
            import os

            import wandb

            settings = None
            if config and config["trainer"].get("wandb_proxy", None):
                settings = wandb.Settings(https_proxy=config["trainer"]["wandb_proxy"])
            entity = os.environ.get("WANDB_ENTITY", None)
            wandb.init(project=project_name, name=experiment_name, entity=entity, config=config, settings=settings)
            self.logger["wandb"] = wandb

        if "trackio" in default_backend:
            import trackio

            trackio.init(project=project_name, name=experiment_name, config=config)
            self.logger["trackio"] = trackio

        if "mlflow" in default_backend:
            import os
            import time

            import mlflow

            for _mlflow_attempt in range(1, MLFLOW_MAX_ATTEMPTS + 1):
                try:
                    MLFLOW_TRACKING_URI = os.environ.get("MLFLOW_TRACKING_URI", "sqlite:////tmp/mlruns.db")
                    logger.info("Using MLFlow tracking URI: %s", MLFLOW_TRACKING_URI)
                    mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)

                    # Some cloud providers like Azure ML or Databricks automatically set MLFLOW_RUN_ID
                    # If set, attach to the existing run instead of creating a new one
                    run_id = os.environ.get("MLFLOW_RUN_ID")
                    if run_id:
                        mlflow.start_run(run_id=run_id)
                    else:
                        # Project_name is actually experiment_name in MLFlow
                        # If experiment does not exist, will create a new experiment
                        experiment = mlflow.set_experiment(project_name)
                        mlflow.start_run(experiment_id=experiment.experiment_id, run_name=experiment_name)

                    mlflow.log_params(_compute_mlflow_params_from_objects(config))
                    self.logger["mlflow"] = _MlflowLoggingAdapter()
                    break  # Success
                except Exception as e:
                    logger.warning(
                        "MLflow initialization attempt %d/%d failed: %s", _mlflow_attempt, MLFLOW_MAX_ATTEMPTS, e
                    )
                    if _mlflow_attempt < MLFLOW_MAX_ATTEMPTS:
                        time.sleep(MLFLOW_SLEEP_SECONDS)
                    else:
                        logger.warning("All MLflow initialization attempts failed. Proceeding without MLflow tracking.")

        if "swanlab" in default_backend:
            import os

            import swanlab

            SWANLAB_API_KEY = os.environ.get("SWANLAB_API_KEY", None)
            SWANLAB_LOG_DIR = os.environ.get("SWANLAB_LOG_DIR", "swanlog")
            SWANLAB_MODE = os.environ.get("SWANLAB_MODE", "cloud")
            if SWANLAB_API_KEY:
                swanlab.login(SWANLAB_API_KEY)  # NOTE: previous login information will be overwritten

            if config is None:
                config = {}  # make sure config is not None, otherwise **config will raise error
            swanlab.init(
                project=project_name,
                experiment_name=experiment_name,
                config={"FRAMEWORK": "verl", **config},
                logdir=SWANLAB_LOG_DIR,
                mode=SWANLAB_MODE,
            )
            self.logger["swanlab"] = swanlab

        if "vemlp_wandb" in default_backend:
            import os

            import volcengine_ml_platform
            from volcengine_ml_platform import wandb as vemlp_wandb

            volcengine_ml_platform.init(
                ak=os.environ["VOLC_ACCESS_KEY_ID"],
                sk=os.environ["VOLC_SECRET_ACCESS_KEY"],
                region=os.environ["MLP_TRACKING_REGION"],
            )

            vemlp_wandb.init(
                project=project_name,
                name=experiment_name,
                config=config,
                sync_tensorboard=True,
            )
            self.logger["vemlp_wandb"] = vemlp_wandb

        if "tensorboard" in default_backend:
            self.logger["tensorboard"] = _TensorboardAdapter(project_name, experiment_name)

        if "console" in default_backend:
            from verl.utils.logger import LocalLogger

            self.console_logger = LocalLogger(print_to_console=True)
            self.logger["console"] = self.console_logger

        if "clearml" in default_backend:
            self.logger["clearml"] = ClearMLLogger(project_name, experiment_name, config)

        if "file" in default_backend:
            self.logger["file"] = FileLogger(project_name, experiment_name)

    def log(self, data, step, backend=None):
        for default_backend, logger_instance in self.logger.items():
            if backend is None or default_backend in backend:
                logger_instance.log(data=data, step=step)

    def __del__(self):
        if "wandb" in self.logger:
            self.logger["wandb"].finish(exit_code=0)
        if "swanlab" in self.logger:
            self.logger["swanlab"].finish()
        if "vemlp_wandb" in self.logger:
            self.logger["vemlp_wandb"].finish(exit_code=0)
        if "tensorboard" in self.logger:
            self.logger["tensorboard"].finish()
        if "clearml" in self.logger:
            self.logger["clearml"].finish()
        if "trackio" in self.logger:
            self.logger["trackio"].finish()
        if "file" in self.logger:
            self.logger["file"].finish()


class ClearMLLogger:
    def __init__(self, project_name: str, experiment_name: str, config):
        self.project_name = project_name
        self.experiment_name = experiment_name

        import clearml

        self._task: clearml.Task = clearml.Task.init(
            task_name=experiment_name,
            project_name=project_name,
            continue_last_task=True,
            output_uri=False,
        )

        self._task.connect_configuration(config, name="Hyperparameters")

    def _get_logger(self):
        return self._task.get_logger()

    def log(self, data, step):
        import numpy as np
        import pandas as pd

        # logs = self._rewrite_logs(data)
        logger = self._get_logger()
        for k, v in data.items():
            title, series = k.split("/", 1)

            if isinstance(v, int | float | np.floating | np.integer):
                logger.report_scalar(
                    title=title,
                    series=series,
                    value=v,
                    iteration=step,
                )
            elif isinstance(v, pd.DataFrame):
                logger.report_table(
                    title=title,
                    series=series,
                    table_plot=v,
                    iteration=step,
                )
            else:
                logger.warning(
                    f'Trainer is attempting to log a value of "{v}" of type {type(v)} for key "{k}". This '
                    f"invocation of ClearML logger's function is incorrect so this attribute was dropped. "
                )

    def finish(self):
        self._task.close()


class FileLogger:
    def __init__(self, project_name: str, experiment_name: str):
        self.project_name = project_name
        self.experiment_name = experiment_name

        self.filepath = os.getenv("VERL_FILE_LOGGER_PATH", None)
        if self.filepath is None:
            root_path = os.path.expanduser(os.getenv("VERL_FILE_LOGGER_ROOT", "."))
            directory = os.path.join(root_path, self.project_name)
            os.makedirs(directory, exist_ok=True)
            self.filepath = os.path.join(directory, f"{self.experiment_name}.jsonl")
            print(f"Creating file logger at {self.filepath}")
        self.fp = open(self.filepath, "wb", buffering=0)

    def log(self, data, step):
        data = {"step": step, "data": data}
        self.fp.write(orjson.dumps(data, option=orjson.OPT_SERIALIZE_NUMPY) + b"\n")

    def finish(self):
        self.fp.close()


class _TensorboardAdapter:
    def __init__(self, project_name, experiment_name):
        import os

        from torch.utils.tensorboard import SummaryWriter

        tensorboard_dir = os.environ.get("TENSORBOARD_DIR", f"tensorboard_log/{project_name}/{experiment_name}")
        os.makedirs(tensorboard_dir, exist_ok=True)
        print(f"Saving tensorboard log to {tensorboard_dir}.")
        self.writer = SummaryWriter(tensorboard_dir)

    def log(self, data, step):
        for key in data:
            self.writer.add_scalar(key, data[key], step)

    def finish(self):
        self.writer.close()


class _MlflowLoggingAdapter:
    def __init__(self):
        import logging
        import re

        self.logger = logging.getLogger(__name__)
        # Suppress noisy "Found credentials from IAM Role" on every MLflow request
        logging.getLogger("botocore.credentials").setLevel(logging.WARNING)
        # MLflow metric key validation logic:
        # https://github.com/mlflow/mlflow/blob/master/mlflow/utils/validation.py#L157C12-L157C44
        # Only characters allowed: slashes, alphanumerics, underscores, periods, dashes, colons,
        # and spaces.
        self._invalid_chars_pattern = re.compile(
            r"[^/\w.\- :]"
        )  # Allowed: slashes, alphanumerics, underscores, periods, dashes, colons, and spaces.
        self._consecutive_slashes_pattern = re.compile(r"/+")
        self._sanitized_key_cache = {}

    def _sanitize_key(self, key):
        if key in self._sanitized_key_cache:
            return self._sanitized_key_cache[key] or key
        # First replace @ with _at_ for backward compatibility
        sanitized = key.replace("@", "_at_")
        # Replace consecutive slashes with a single slash (MLflow treats them as file paths)
        sanitized = self._consecutive_slashes_pattern.sub("/", sanitized)
        # Then replace any other invalid characters with _
        sanitized = self._invalid_chars_pattern.sub("_", sanitized)
        if sanitized == key:
            self._sanitized_key_cache[key] = None
        else:
            self.logger.warning("[MLflow] Metric key '%s' sanitized to '%s' due to invalid characters.", key, sanitized)
            self._sanitized_key_cache[key] = sanitized
        return sanitized

    def log(self, data, step):
        import mlflow

        results = {self._sanitize_key(k): v for k, v in data.items()}
        mlflow.log_metrics(metrics=results, step=step)


def _compute_mlflow_params_from_objects(params) -> dict[str, Any]:
    if params is None:
        return {}

    return _flatten_dict(_transform_params_to_json_serializable(params, convert_list_to_dict=True), sep="/")


def _transform_params_to_json_serializable(x, convert_list_to_dict: bool):
    _transform = partial(_transform_params_to_json_serializable, convert_list_to_dict=convert_list_to_dict)

    if dataclasses.is_dataclass(x):
        return _transform(dataclasses.asdict(x))
    if isinstance(x, dict):
        return {k: _transform(v) for k, v in x.items()}
    if isinstance(x, list):
        if convert_list_to_dict:
            return {"list_len": len(x)} | {f"{i}": _transform(v) for i, v in enumerate(x)}
        else:
            return [_transform(v) for v in x]
    if isinstance(x, Path):
        return str(x)
    if isinstance(x, Enum):
        return x.value

    return x


def _flatten_dict(raw: dict[str, Any], *, sep: str) -> dict[str, Any]:
    import pandas as pd

    ans = pd.json_normalize(raw, sep=sep).to_dict(orient="records")[0]
    assert isinstance(ans, dict)
    return ans


@dataclasses.dataclass
class ValidationGenerationsLogger:
    project_name: str = None
    experiment_name: str = None

    def log(self, loggers, samples, step):
        if "wandb" in loggers:
            self.log_generations_to_wandb(samples, step)
        if "swanlab" in loggers:
            self.log_generations_to_swanlab(samples, step)
        if "mlflow" in loggers:
            self.log_generations_to_mlflow(samples, step)

        if "clearml" in loggers:
            self.log_generations_to_clearml(samples, step)
        if "tensorboard" in loggers:
            self.log_generations_to_tensorboard(samples, step)

        if "vemlp_wandb" in loggers:
            self.log_generations_to_vemlp_wandb(samples, step)

    def log_generations_to_vemlp_wandb(self, samples, step):
        from volcengine_ml_platform import wandb as vemlp_wandb

        self._log_generations_to_wandb(samples, step, vemlp_wandb)

    def log_generations_to_wandb(self, samples, step):
        import wandb

        self._log_generations_to_wandb(samples, step, wandb)

    def _log_generations_to_wandb(self, samples, step, wandb):
        """Log samples to wandb as a table"""

        # Create column names for all samples
        columns = ["step"] + sum(
            [[f"input_{i + 1}", f"output_{i + 1}", f"score_{i + 1}"] for i in range(len(samples))], []
        )

        if not hasattr(self, "validation_table"):
            # Initialize the table on first call
            self.validation_table = wandb.Table(columns=columns)

        # Create a new table with same columns and existing data
        # Workaround for https://github.com/wandb/wandb/issues/2981#issuecomment-1997445737
        new_table = wandb.Table(columns=columns, data=self.validation_table.data)

        # Add new row with all data
        row_data = []
        row_data.append(step)
        for sample in samples:
            row_data.extend(sample)

        new_table.add_data(*row_data)

        # Update reference and log
        if wandb.run is not None:
            wandb.log({"val/generations": new_table}, step=step)
        self.validation_table = new_table

    def log_generations_to_swanlab(self, samples, step):
        """Log samples to swanlab as text"""
        import swanlab

        swanlab_table = swanlab.echarts.Table()

        # Create column names
        headers = ["step", "input", "output", "score"]

        swanlab_row_list = [[step, *sample] for sample in samples]
        swanlab_table.add(headers=headers, rows=swanlab_row_list)

        # Log to swanlab
        swanlab.log({"val/generations": swanlab_table}, step=step)

    def log_generations_to_mlflow(self, samples, step):
        """Log validation generation to mlflow as artifacts"""
        # https://mlflow.org/docs/latest/api_reference/python_api/mlflow.html?highlight=log_artifact#mlflow.log_artifact

        import tempfile

        import mlflow

        try:
            with tempfile.TemporaryDirectory() as tmp_dir:
                validation_gen_step_file = Path(tmp_dir, f"val_step{step}.json")
                row_data = []
                for sample in samples:
                    data = {"input": sample[0], "output": sample[1], "score": sample[2]}
                    row_data.append(data)
                with open(validation_gen_step_file, "w") as file:
                    json.dump(row_data, file)
                mlflow.log_artifact(validation_gen_step_file)
        except Exception as e:
            print(f"WARNING: save validation generation file to mlflow failed with error {e}")

    def log_generations_to_clearml(self, samples, step):
        """Log validation generation to clearml as table"""

        import clearml
        import pandas as pd

        task: clearml.Task | None = clearml.Task.current_task()
        if task is None:
            return

        table = [
            {
                "step": step,
                "input": sample[0],
                "output": sample[1],
                "score": sample[2],
            }
            for sample in samples
        ]

        logger = task.get_logger()
        logger.report_table(
            series="Validation generations",
            title="Validation",
            table_plot=pd.DataFrame.from_records(table),
            iteration=step,
        )

    def log_generations_to_tensorboard(self, samples, step):
        """Log samples to tensorboard as text"""
        # Initialize tensorboard writer if not exists
        if not hasattr(self, "writer"):
            from torch.utils.tensorboard import SummaryWriter

            # Use the same directory structure as _TensorboardAdapter
            if self.project_name and self.experiment_name:
                default_dir = os.path.join("tensorboard_log", self.project_name, self.experiment_name)
            else:
                default_dir = "tensorboard_log"

            tensorboard_dir = os.environ.get("TENSORBOARD_DIR", default_dir)
            os.makedirs(tensorboard_dir, exist_ok=True)
            self.writer = SummaryWriter(log_dir=tensorboard_dir)

        # Format the samples data into readable text
        text_content = f"**Generation Results - Step {step}**\n\n"

        for i, sample in enumerate(samples):
            text_content += f"### Sample {i + 1}\n"

            # Assuming sample contains [input, output, score]
            if len(sample) >= 3:
                input_text, output_text, score = sample[0], sample[1], sample[2]

                text_content += f"**Input:** {input_text}\n\n"
                text_content += f"**Output:** {output_text}\n\n"
                text_content += f"**Score:** {score}\n\n"
            else:
                # Handle cases where sample format might be different
                text_content += f"**Data:** {sample}\n\n"

            text_content += "---\n\n"

        # Log to tensorboard as text
        self.writer.add_text("val/generations", text_content, step)
        # Flush to ensure data is written
        self.writer.flush()

	# Copyright 2024 Bytedance Ltd. and/or its 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.
	"""
	A unified tracking interface that supports logging data to different backend
	"""

	import dataclasses
	import json
	import logging
	import os
	from enum import Enum
	from functools import partial
	from pathlib import Path
	from typing import Any

	import orjson

	logger = logging.getLogger(__name__)

	MLFLOW_MAX_ATTEMPTS = 3
	MLFLOW_SLEEP_SECONDS = 5


	class Tracking:
	"""A unified tracking interface for logging experiment data to multiple backends.

	This class provides a centralized way to log experiment metrics, parameters, and artifacts
	to various tracking backends including WandB, MLflow, SwanLab, TensorBoard, and console.

	Attributes:
	supported_backend: List of supported tracking backends.
	logger: Dictionary of initialized logger instances for each backend.
	"""

	supported_backend = [
	"wandb",
	"mlflow",
	"swanlab",
	"vemlp_wandb",
	"tensorboard",
	"console",
	"clearml",
	"trackio",
	"file",
	]

	def __init__(self, project_name, experiment_name, default_backend: str \| list[str] = "console", config=None):
	if isinstance(default_backend, str):
	default_backend = [default_backend]
	for backend in default_backend:
	if backend == "tracking":
	import warnings

	warnings.warn("`tracking` logger is deprecated. use `wandb` instead.", DeprecationWarning, stacklevel=2)
	else:
	assert backend in self.supported_backend, f"{backend} is not supported"

	self.logger = {}

	if "tracking" in default_backend or "wandb" in default_backend:
	import os

	import wandb

	settings = None
	if config and config["trainer"].get("wandb_proxy", None):
	settings = wandb.Settings(https_proxy=config["trainer"]["wandb_proxy"])
	entity = os.environ.get("WANDB_ENTITY", None)
	wandb.init(project=project_name, name=experiment_name, entity=entity, config=config, settings=settings)
	self.logger["wandb"] = wandb

	if "trackio" in default_backend:
	import trackio

	trackio.init(project=project_name, name=experiment_name, config=config)
	self.logger["trackio"] = trackio

	if "mlflow" in default_backend:
	import os
	import time

	import mlflow

	for _mlflow_attempt in range(1, MLFLOW_MAX_ATTEMPTS + 1):
	try:
	MLFLOW_TRACKING_URI = os.environ.get("MLFLOW_TRACKING_URI", "sqlite:////tmp/mlruns.db")
	logger.info("Using MLFlow tracking URI: %s", MLFLOW_TRACKING_URI)
	mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)

	# Some cloud providers like Azure ML or Databricks automatically set MLFLOW_RUN_ID
	# If set, attach to the existing run instead of creating a new one
	run_id = os.environ.get("MLFLOW_RUN_ID")
	if run_id:
	mlflow.start_run(run_id=run_id)
	else:
	# Project_name is actually experiment_name in MLFlow
	# If experiment does not exist, will create a new experiment
	experiment = mlflow.set_experiment(project_name)
	mlflow.start_run(experiment_id=experiment.experiment_id, run_name=experiment_name)

	mlflow.log_params(_compute_mlflow_params_from_objects(config))
	self.logger["mlflow"] = _MlflowLoggingAdapter()
	break # Success
	except Exception as e:
	logger.warning(
	"MLflow initialization attempt %d/%d failed: %s", _mlflow_attempt, MLFLOW_MAX_ATTEMPTS, e
	)
	if _mlflow_attempt < MLFLOW_MAX_ATTEMPTS:
	time.sleep(MLFLOW_SLEEP_SECONDS)
	else:
	logger.warning("All MLflow initialization attempts failed. Proceeding without MLflow tracking.")

	if "swanlab" in default_backend:
	import os

	import swanlab

	SWANLAB_API_KEY = os.environ.get("SWANLAB_API_KEY", None)
	SWANLAB_LOG_DIR = os.environ.get("SWANLAB_LOG_DIR", "swanlog")
	SWANLAB_MODE = os.environ.get("SWANLAB_MODE", "cloud")
	if SWANLAB_API_KEY:
	swanlab.login(SWANLAB_API_KEY) # NOTE: previous login information will be overwritten

	if config is None:
	config = {} # make sure config is not None, otherwise **config will raise error
	swanlab.init(
	project=project_name,
	experiment_name=experiment_name,
	config={"FRAMEWORK": "verl", **config},
	logdir=SWANLAB_LOG_DIR,
	mode=SWANLAB_MODE,
	)
	self.logger["swanlab"] = swanlab

	if "vemlp_wandb" in default_backend:
	import os

	import volcengine_ml_platform
	from volcengine_ml_platform import wandb as vemlp_wandb

	volcengine_ml_platform.init(
	ak=os.environ["VOLC_ACCESS_KEY_ID"],
	sk=os.environ["VOLC_SECRET_ACCESS_KEY"],
	region=os.environ["MLP_TRACKING_REGION"],
	)

	vemlp_wandb.init(
	project=project_name,
	name=experiment_name,
	config=config,
	sync_tensorboard=True,
	)
	self.logger["vemlp_wandb"] = vemlp_wandb

	if "tensorboard" in default_backend:
	self.logger["tensorboard"] = _TensorboardAdapter(project_name, experiment_name)

	if "console" in default_backend:
	from verl.utils.logger import LocalLogger

	self.console_logger = LocalLogger(print_to_console=True)
	self.logger["console"] = self.console_logger

	if "clearml" in default_backend:
	self.logger["clearml"] = ClearMLLogger(project_name, experiment_name, config)

	if "file" in default_backend:
	self.logger["file"] = FileLogger(project_name, experiment_name)

	def log(self, data, step, backend=None):
	for default_backend, logger_instance in self.logger.items():
	if backend is None or default_backend in backend:
	logger_instance.log(data=data, step=step)

	def __del__(self):
	if "wandb" in self.logger:
	self.logger["wandb"].finish(exit_code=0)
	if "swanlab" in self.logger:
	self.logger["swanlab"].finish()
	if "vemlp_wandb" in self.logger:
	self.logger["vemlp_wandb"].finish(exit_code=0)
	if "tensorboard" in self.logger:
	self.logger["tensorboard"].finish()
	if "clearml" in self.logger:
	self.logger["clearml"].finish()
	if "trackio" in self.logger:
	self.logger["trackio"].finish()
	if "file" in self.logger:
	self.logger["file"].finish()


	class ClearMLLogger:
	def __init__(self, project_name: str, experiment_name: str, config):
	self.project_name = project_name
	self.experiment_name = experiment_name

	import clearml

	self._task: clearml.Task = clearml.Task.init(
	task_name=experiment_name,
	project_name=project_name,
	continue_last_task=True,
	output_uri=False,
	)

	self._task.connect_configuration(config, name="Hyperparameters")

	def _get_logger(self):
	return self._task.get_logger()

	def log(self, data, step):
	import numpy as np
	import pandas as pd

	# logs = self._rewrite_logs(data)
	logger = self._get_logger()
	for k, v in data.items():
	title, series = k.split("/", 1)

	if isinstance(v, int \| float \| np.floating \| np.integer):
	logger.report_scalar(
	title=title,
	series=series,
	value=v,
	iteration=step,
	)
	elif isinstance(v, pd.DataFrame):
	logger.report_table(
	title=title,
	series=series,
	table_plot=v,
	iteration=step,
	)
	else:
	logger.warning(
	f'Trainer is attempting to log a value of "{v}" of type {type(v)} for key "{k}". This '
	f"invocation of ClearML logger's function is incorrect so this attribute was dropped. "
	)

	def finish(self):
	self._task.close()


	class FileLogger:
	def __init__(self, project_name: str, experiment_name: str):
	self.project_name = project_name
	self.experiment_name = experiment_name

	self.filepath = os.getenv("VERL_FILE_LOGGER_PATH", None)
	if self.filepath is None:
	root_path = os.path.expanduser(os.getenv("VERL_FILE_LOGGER_ROOT", "."))
	directory = os.path.join(root_path, self.project_name)
	os.makedirs(directory, exist_ok=True)
	self.filepath = os.path.join(directory, f"{self.experiment_name}.jsonl")
	print(f"Creating file logger at {self.filepath}")
	self.fp = open(self.filepath, "wb", buffering=0)

	def log(self, data, step):
	data = {"step": step, "data": data}
	self.fp.write(orjson.dumps(data, option=orjson.OPT_SERIALIZE_NUMPY) + b"\n")

	def finish(self):
	self.fp.close()


	class _TensorboardAdapter:
	def __init__(self, project_name, experiment_name):
	import os

	from torch.utils.tensorboard import SummaryWriter

	tensorboard_dir = os.environ.get("TENSORBOARD_DIR", f"tensorboard_log/{project_name}/{experiment_name}")
	os.makedirs(tensorboard_dir, exist_ok=True)
	print(f"Saving tensorboard log to {tensorboard_dir}.")
	self.writer = SummaryWriter(tensorboard_dir)

	def log(self, data, step):
	for key in data:
	self.writer.add_scalar(key, data[key], step)

	def finish(self):
	self.writer.close()


	class _MlflowLoggingAdapter:
	def __init__(self):
	import logging
	import re

	self.logger = logging.getLogger(__name__)
	# Suppress noisy "Found credentials from IAM Role" on every MLflow request
	logging.getLogger("botocore.credentials").setLevel(logging.WARNING)
	# MLflow metric key validation logic:
	# https://github.com/mlflow/mlflow/blob/master/mlflow/utils/validation.py#L157C12-L157C44
	# Only characters allowed: slashes, alphanumerics, underscores, periods, dashes, colons,
	# and spaces.
	self._invalid_chars_pattern = re.compile(
	r"[^/\w.\- :]"
	) # Allowed: slashes, alphanumerics, underscores, periods, dashes, colons, and spaces.
	self._consecutive_slashes_pattern = re.compile(r"/+")
	self._sanitized_key_cache = {}

	def _sanitize_key(self, key):
	if key in self._sanitized_key_cache:
	return self._sanitized_key_cache[key] or key
	# First replace @ with _at_ for backward compatibility
	sanitized = key.replace("@", "_at_")
	# Replace consecutive slashes with a single slash (MLflow treats them as file paths)
	sanitized = self._consecutive_slashes_pattern.sub("/", sanitized)
	# Then replace any other invalid characters with _
	sanitized = self._invalid_chars_pattern.sub("_", sanitized)
	if sanitized == key:
	self._sanitized_key_cache[key] = None
	else:
	self.logger.warning("[MLflow] Metric key '%s' sanitized to '%s' due to invalid characters.", key, sanitized)
	self._sanitized_key_cache[key] = sanitized
	return sanitized

	def log(self, data, step):
	import mlflow

	results = {self._sanitize_key(k): v for k, v in data.items()}
	mlflow.log_metrics(metrics=results, step=step)


	def _compute_mlflow_params_from_objects(params) -> dict[str, Any]:
	if params is None:
	return {}

	return _flatten_dict(_transform_params_to_json_serializable(params, convert_list_to_dict=True), sep="/")


	def _transform_params_to_json_serializable(x, convert_list_to_dict: bool):
	_transform = partial(_transform_params_to_json_serializable, convert_list_to_dict=convert_list_to_dict)

	if dataclasses.is_dataclass(x):
	return _transform(dataclasses.asdict(x))
	if isinstance(x, dict):
	return {k: _transform(v) for k, v in x.items()}
	if isinstance(x, list):
	if convert_list_to_dict:
	return {"list_len": len(x)} \| {f"{i}": _transform(v) for i, v in enumerate(x)}
	else:
	return [_transform(v) for v in x]
	if isinstance(x, Path):
	return str(x)
	if isinstance(x, Enum):
	return x.value

	return x


	def _flatten_dict(raw: dict[str, Any], *, sep: str) -> dict[str, Any]:
	import pandas as pd

	ans = pd.json_normalize(raw, sep=sep).to_dict(orient="records")[0]
	assert isinstance(ans, dict)
	return ans


	@dataclasses.dataclass
	class ValidationGenerationsLogger:
	project_name: str = None
	experiment_name: str = None

	def log(self, loggers, samples, step):
	if "wandb" in loggers:
	self.log_generations_to_wandb(samples, step)
	if "swanlab" in loggers:
	self.log_generations_to_swanlab(samples, step)
	if "mlflow" in loggers:
	self.log_generations_to_mlflow(samples, step)

	if "clearml" in loggers:
	self.log_generations_to_clearml(samples, step)
	if "tensorboard" in loggers:
	self.log_generations_to_tensorboard(samples, step)

	if "vemlp_wandb" in loggers:
	self.log_generations_to_vemlp_wandb(samples, step)

	def log_generations_to_vemlp_wandb(self, samples, step):
	from volcengine_ml_platform import wandb as vemlp_wandb

	self._log_generations_to_wandb(samples, step, vemlp_wandb)

	def log_generations_to_wandb(self, samples, step):
	import wandb

	self._log_generations_to_wandb(samples, step, wandb)

	def _log_generations_to_wandb(self, samples, step, wandb):
	"""Log samples to wandb as a table"""

	# Create column names for all samples
	columns = ["step"] + sum(
	[[f"input_{i + 1}", f"output_{i + 1}", f"score_{i + 1}"] for i in range(len(samples))], []
	)

	if not hasattr(self, "validation_table"):
	# Initialize the table on first call
	self.validation_table = wandb.Table(columns=columns)

	# Create a new table with same columns and existing data
	# Workaround for https://github.com/wandb/wandb/issues/2981#issuecomment-1997445737
	new_table = wandb.Table(columns=columns, data=self.validation_table.data)

	# Add new row with all data
	row_data = []
	row_data.append(step)
	for sample in samples:
	row_data.extend(sample)

	new_table.add_data(*row_data)

	# Update reference and log
	if wandb.run is not None:
	wandb.log({"val/generations": new_table}, step=step)
	self.validation_table = new_table

	def log_generations_to_swanlab(self, samples, step):
	"""Log samples to swanlab as text"""
	import swanlab

	swanlab_table = swanlab.echarts.Table()

	# Create column names
	headers = ["step", "input", "output", "score"]

	swanlab_row_list = [[step, *sample] for sample in samples]
	swanlab_table.add(headers=headers, rows=swanlab_row_list)

	# Log to swanlab
	swanlab.log({"val/generations": swanlab_table}, step=step)

	def log_generations_to_mlflow(self, samples, step):
	"""Log validation generation to mlflow as artifacts"""
	# https://mlflow.org/docs/latest/api_reference/python_api/mlflow.html?highlight=log_artifact#mlflow.log_artifact

	import tempfile

	import mlflow

	try:
	with tempfile.TemporaryDirectory() as tmp_dir:
	validation_gen_step_file = Path(tmp_dir, f"val_step{step}.json")
	row_data = []
	for sample in samples:
	data = {"input": sample[0], "output": sample[1], "score": sample[2]}
	row_data.append(data)
	with open(validation_gen_step_file, "w") as file:
	json.dump(row_data, file)
	mlflow.log_artifact(validation_gen_step_file)
	except Exception as e:
	print(f"WARNING: save validation generation file to mlflow failed with error {e}")

	def log_generations_to_clearml(self, samples, step):
	"""Log validation generation to clearml as table"""

	import clearml
	import pandas as pd

	task: clearml.Task \| None = clearml.Task.current_task()
	if task is None:
	return

	table = [
	{
	"step": step,
	"input": sample[0],
	"output": sample[1],
	"score": sample[2],
	}
	for sample in samples
	]

	logger = task.get_logger()
	logger.report_table(
	series="Validation generations",
	title="Validation",
	table_plot=pd.DataFrame.from_records(table),
	iteration=step,
	)

	def log_generations_to_tensorboard(self, samples, step):
	"""Log samples to tensorboard as text"""
	# Initialize tensorboard writer if not exists
	if not hasattr(self, "writer"):
	from torch.utils.tensorboard import SummaryWriter

	# Use the same directory structure as _TensorboardAdapter
	if self.project_name and self.experiment_name:
	default_dir = os.path.join("tensorboard_log", self.project_name, self.experiment_name)
	else:
	default_dir = "tensorboard_log"

	tensorboard_dir = os.environ.get("TENSORBOARD_DIR", default_dir)
	os.makedirs(tensorboard_dir, exist_ok=True)
	self.writer = SummaryWriter(log_dir=tensorboard_dir)

	# Format the samples data into readable text
	text_content = f"Generation Results - Step {step}\n\n"

	for i, sample in enumerate(samples):
	text_content += f"### Sample {i + 1}\n"

	# Assuming sample contains [input, output, score]
	if len(sample) >= 3:
	input_text, output_text, score = sample[0], sample[1], sample[2]

	text_content += f"Input: {input_text}\n\n"
	text_content += f"Output: {output_text}\n\n"
	text_content += f"Score: {score}\n\n"
	else:
	# Handle cases where sample format might be different
	text_content += f"Data: {sample}\n\n"

	text_content += "---\n\n"

	# Log to tensorboard as text
	self.writer.add_text("val/generations", text_content, step)
	# Flush to ensure data is written
	self.writer.flush()