# 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 contextlib import contextmanager
from enum import Enum
from functools import partial
from pathlib import Path
from typing import Any
import orjson
from packaging.version import Version
logger = logging.getLogger(__name__)
MLFLOW_MAX_ATTEMPTS = 3
MLFLOW_SLEEP_SECONDS = 5
[docs]
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",
"rl_insight",
]
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 = {}
self._finished = False
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
from trackio import context_vars
if context_vars.current_run.get() is None:
trackio.init(project=project_name, name=experiment_name, config=config)
self.logger["trackio"] = _TrackioLoggingAdapter(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)
if "rl_insight" in default_backend:
self.logger["rl_insight"] = RLInsightLogger(project_name, experiment_name, config)
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)
[docs]
def finish(self, exit_code: int = 0):
"""Flush and finalize every configured backend exactly once."""
if getattr(self, "_finished", False):
return
self._finished = True
loggers = getattr(self, "logger", {})
if "wandb" in loggers:
loggers["wandb"].finish(exit_code=exit_code)
if "swanlab" in loggers:
loggers["swanlab"].finish()
if "vemlp_wandb" in loggers:
loggers["vemlp_wandb"].finish(exit_code=exit_code)
if "tensorboard" in loggers:
loggers["tensorboard"].finish()
if "clearml" in loggers:
loggers["clearml"].finish()
if "trackio" in loggers:
loggers["trackio"].finish()
if "file" in loggers:
loggers["file"].finish()
if "rl_insight" in loggers:
loggers["rl_insight"].finish()
def __del__(self):
self.finish()
class RLInsightLogger:
"""Logger backend that exports scalar metrics and rl-insight runtime signals."""
ENABLE_ENV = "VERL_RL_INSIGHT_ENABLE"
_init_done = False
_rl_insight_module = None
_registered_metrics: set[tuple[str | None, tuple[str, ...], str | None]] = set()
def __init__(self, project_name, experiment_name, config=None):
self.init(project_name=project_name, experiment_name=experiment_name, config=config)
@classmethod
def _get_rl_insight(cls):
if cls._rl_insight_module is None:
import rl_insight
cls._rl_insight_module = rl_insight
return cls._rl_insight_module
@classmethod
def enabled(cls) -> bool:
"""Return whether rl-insight is globally enabled in this process."""
return os.getenv(cls.ENABLE_ENV) == "1"
@classmethod
def init(cls, project_name=None, experiment_name=None, config=None):
if not cls.enabled() or cls._init_done:
return
rl_insight_config = {}
if config is not None:
try:
rl_insight_config = config.get("trainer", {}).get("rl_insight", {}) or {}
except (AttributeError, KeyError, TypeError):
pass
cls._get_rl_insight().init(project=project_name, experiment_name=experiment_name, config=rl_insight_config)
cls._init_done = True
if config is not None:
cls.register_transfer_queue_metrics(config)
@classmethod
def log(cls, data, step):
if not cls.enabled():
return
if not cls._init_done:
cls._get_rl_insight().init()
cls._init_done = True
metric_gauge = cls._get_rl_insight().metric_gauge
for key, value in data.items():
try:
scalar = float(value)
except (TypeError, ValueError):
continue
metric_gauge(str(key).replace("/", "_"), scalar)
@classmethod
def finish(cls):
if not cls._init_done:
return
cls._get_rl_insight().finish()
cls._init_done = False
cls._registered_metrics.clear()
@classmethod
@contextmanager
def trace_state(
cls,
state_name: str,
*,
state_lane_id: str | int | None = None,
**labels: Any,
):
if not cls.enabled():
yield
return
if not cls._init_done:
cls._get_rl_insight().init()
cls._init_done = True
with cls._get_rl_insight().trace_state(state_name, state_lane_id=state_lane_id, **labels):
yield
@classmethod
def register_rollout_metrics(
cls,
server_addresses: list[str],
rollout_name: str | None,
labels: list[dict[str, Any] | None] | None = None,
) -> None:
cls.register_metrics(server_addresses, rollout_name, labels)
@classmethod
def register_transfer_queue_metrics(cls, config) -> None:
if not (config or {}).get("transfer_queue", {}).get("metrics", {}).get("enabled", False):
return
try:
import transfer_queue as tq
endpoint = tq.get_metrics_endpoint()
except Exception:
logger.exception("[rl-insight] Failed to get transfer_queue metrics endpoint")
return
if endpoint:
cls.register_metrics([endpoint], "transfer_queue", None)
@classmethod
def register_metrics(
cls,
server_addresses: list[str],
job_name: str | None = None,
labels: list[dict[str, Any] | None] | None = None,
) -> None:
if not cls.enabled():
return
metric_key = (job_name, tuple(server_addresses), repr(labels))
if metric_key in cls._registered_metrics:
return
try:
cls._get_rl_insight().update_prometheus_config(server_addresses, job_name, labels)
cls._registered_metrics.add(metric_key)
except Exception:
logger.exception("[rl-insight] Failed to register metrics endpoint")
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 _TrackioLoggingAdapter:
def __init__(self, trackio):
self.trackio = trackio
def log(self, data, step):
self.trackio.log(data, step=step)
def finish(self):
from trackio import context_vars
if context_vars.current_run.get() is not None:
self.trackio.finish()
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 {os.path.abspath(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()}
for _attempt in range(MLFLOW_MAX_ATTEMPTS):
try:
mlflow.log_metrics(metrics=results, step=step)
return
except Exception as error:
# No sleep between retries — this runs per training step, so we avoid blocking.
msg = "mlflow.log_metrics failed (attempt %d/%d): %s"
args = (_attempt + 1, MLFLOW_MAX_ATTEMPTS, error)
if _attempt < MLFLOW_MAX_ATTEMPTS - 1:
self.logger.info(msg, *args)
else:
self.logger.warning(msg, *args)
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 "trackio" in loggers:
self.log_generations_to_trackio(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_trackio(self, samples, step):
"""Log validation generations to trackio as traces."""
import trackio
traces = []
for sample_index, sample in enumerate(samples):
if len(sample) >= 3:
input_text, output_text, score = sample[0], sample[1], sample[2]
else:
input_text, output_text, score = sample, "", None
traces.append(
trackio.Trace(
messages=[
{"role": "user", "content": str(input_text)},
{"role": "assistant", "content": str(output_text)},
],
metadata={
"source": "validation_generations",
"sample_index": sample_index,
"score": score,
},
)
)
if traces:
trackio.log({"val/generations": traces}, step=step)
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()
@dataclasses.dataclass
class DapoFilteredRewardTableLogger:
"""Wandb table of DAPO-filtered (no-signal) group counts per reward value.
Each training step adds one row containing compact ``reward:count`` pairs. Wandb 0.20+
uploads rows incrementally; older versions rebuild the full table for compatibility.
Intentionally wandb-only: this "value distribution over time" view is a table, which other
tracking backends do not render usefully. Non-wandb backends are silently skipped.
"""
project_name: str = None
experiment_name: str = None
def log(self, loggers, reward_counts: dict, step: int):
"""reward_counts maps metric value -> count for this step (already merged across mini-batches)."""
if "wandb" in loggers:
self._log_to_wandb(reward_counts, step)
def _log_to_wandb(self, reward_counts: dict, step: int):
import wandb
if wandb.run is None:
return
row = {float(value): int(count) for value, count in reward_counts.items()}
counts_text = ", ".join(f"{value:g}:{row[value]}" for value in sorted(row))
columns = ["step", "reward_counts"]
if not hasattr(self, "_use_incremental_table"):
self._use_incremental_table = Version(wandb.__version__) >= Version("0.20.0")
if self._use_incremental_table:
self._table = wandb.Table(columns=columns, log_mode="INCREMENTAL")
else:
self._rows = []
logger.warning(
"wandb<0.20.0 does not support incremental tables; "
"the DAPO filtered-reward table will re-upload its full history each step."
)
if self._use_incremental_table:
self._table.add_data(step, counts_text)
table = self._table
else:
self._rows.append([step, counts_text])
table = wandb.Table(columns=columns, data=list(self._rows))
wandb.log({"training/filter_groups/filtered_reward_counts": table}, step=step)