104 lines
3.7 KiB
Python
104 lines
3.7 KiB
Python
# Copyright 2024 Bytedance Ltd. and/or its affiliates
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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A unified tracking interface that supports logging data to different backend
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"""
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import dataclasses
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from enum import Enum
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from functools import partial
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from pathlib import Path
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from typing import List, Union, Dict, Any
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class Tracking(object):
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supported_backend = ['wandb', 'mlflow', 'console']
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def __init__(self, project_name, experiment_name, default_backend: Union[str, List[str]] = 'console', config=None):
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if isinstance(default_backend, str):
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default_backend = [default_backend]
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for backend in default_backend:
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if backend == 'tracking':
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import warnings
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warnings.warn("`tracking` logger is deprecated. use `wandb` instead.", DeprecationWarning)
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else:
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assert backend in self.supported_backend, f'{backend} is not supported'
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self.logger = {}
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if 'tracking' in default_backend or 'wandb' in default_backend:
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import wandb
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import os
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WANDB_API_KEY = os.environ.get("WANDB_API_KEY", None)
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if WANDB_API_KEY:
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wandb.login(key=WANDB_API_KEY)
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wandb.init(project=project_name, name=experiment_name, config=config)
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self.logger['wandb'] = wandb
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if 'mlflow' in default_backend:
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import mlflow
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mlflow.start_run(run_name=experiment_name)
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mlflow.log_params(_compute_mlflow_params_from_objects(config))
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self.logger['mlflow'] = _MlflowLoggingAdapter()
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if 'console' in default_backend:
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from verl.utils.logger.aggregate_logger import LocalLogger
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self.console_logger = LocalLogger(print_to_console=True)
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self.logger['console'] = self.console_logger
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def log(self, data, step, backend=None):
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for default_backend, logger_instance in self.logger.items():
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if backend is None or default_backend in backend:
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logger_instance.log(data=data, step=step)
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class _MlflowLoggingAdapter:
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def log(self, data, step):
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import mlflow
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mlflow.log_metrics(metrics=data, step=step)
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def _compute_mlflow_params_from_objects(params) -> Dict[str, Any]:
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if params is None:
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return {}
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return _flatten_dict(_transform_params_to_json_serializable(params, convert_list_to_dict=True), sep='/')
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def _transform_params_to_json_serializable(x, convert_list_to_dict: bool):
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_transform = partial(_transform_params_to_json_serializable, convert_list_to_dict=convert_list_to_dict)
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if dataclasses.is_dataclass(x):
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return _transform(dataclasses.asdict(x))
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if isinstance(x, dict):
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return {k: _transform(v) for k, v in x.items()}
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if isinstance(x, list):
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if convert_list_to_dict:
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return {'list_len': len(x)} | {f'{i}': _transform(v) for i, v in enumerate(x)}
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else:
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return [_transform(v) for v in x]
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if isinstance(x, Path):
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return str(x)
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if isinstance(x, Enum):
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return x.value
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return x
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def _flatten_dict(raw: Dict[str, Any], *, sep: str) -> Dict[str, Any]:
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import pandas as pd
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ans = pd.json_normalize(raw, sep=sep).to_dict(orient='records')[0]
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assert isinstance(ans, dict)
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return ans
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