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from copy import deepcopy
from typing import Dict, List
from mmengine.config import Config, ConfigDict
from opencompass.registry import PARTITIONERS
from .base import BasePartitioner
@PARTITIONERS.register_module()
class MultimodalNaivePartitioner(BasePartitioner):
"""Multimodal naive task partitioner.
This partitioner will generate a task for each
model-dataset-evaluator pair.
Args:
config (ConfigDict): The full config dict.
"""
def partition(self, models: List[ConfigDict], datasets: List[ConfigDict],
evaluators: List[ConfigDict], load_froms: List[ConfigDict],
work_dir: str, num_gpus: int, num_procs: int,
launcher: str) -> List[Dict]:
"""Partition model-dataset pairs into tasks. Each task is defined as a
dict and will run independently as a unit. Its structure is as follows:
.. code-block:: python
{
'models': [], # a list of model configs
'datasets': [], # a list of dataset configs
'evaluators': [], # a list of evaluator configs
'load_froms': [], # a list of load_from paths
'work_dir': '', # the work dir
'num_gpus': int, # integer, number of gpus for each task
'num_procs': int, # integer, number of gpus on single machine
'launcher': str, # string, how to launch distributed training
}
Args:
models (List[ConfigDict]): A list of model configs.
datasets (List[ConfigDict]): A list of dataset configs.
evaluators (List[ConfigDict]): A list of evaluator configs.
load_froms (List[ConfigDict]): A list of load_from paths.
work_dir (str): The work dir for the task.
num_gpus (int): Number of gpus for each task.
num_procs (int): Number of gpus on single machine.
launcher (str): How to launch distributed training.
Only `slurm`, `pytorch` and `mpi` are available.
Returns:
List[Dict]: A list of tasks.
"""
tasks = []
for model, dataset, evaluator, load_from in zip(
models, datasets, evaluators, load_froms):
task = Config({
'model': model,
'dataset': dataset,
'evaluator': evaluator,
'load_from': load_from,
'work_dir': work_dir,
'num_gpus': num_gpus,
'num_procs': num_procs,
'launcher': launcher
})
tasks.append(task)
return tasks
def __call__(self, cfg: ConfigDict) -> List[Dict]:
"""Generate tasks from config. Each task is defined as a
dict and will run independently as a unit. Its structure is as
follows:
.. code-block:: python
{
'models': [], # a list of model configs
'datasets': [], # a list of dataset configs
'evaluators': [], # a list of evaluator configs
'load_froms': [], # a list of load_from paths
'work_dir': '', # the work dir
'num_gpus': int, # integer, number of gpus for each task
'num_procs': int, # integer, number of gpus on single machine
}
Args:
cfg (ConfigDict): The config dict, containing "models", "dataset"
and "work_dir" keys.
Returns:
List[Dict]: A list of tasks.
"""
cfg = deepcopy(cfg)
models = cfg['models']
datasets = cfg['datasets']
evaluators = cfg['evaluators']
load_froms = cfg['load_froms']
work_dir = cfg['work_dir']
num_gpus = cfg['num_gpus']
num_procs = cfg['num_procs']
launcher = cfg['launcher']
tasks = self.partition(models, datasets, evaluators, load_froms,
work_dir, num_gpus, num_procs, launcher)
self.logger.info(f'Partitioned into {len(tasks)} tasks.')
for i, task in enumerate(tasks):
model_name = task['model']['type']
dataset_name = task['dataset']['dataset']['type']
evaluator_name = task['evaluator'][0]['type']
self.logger.debug(
f'Task {i}: {model_name}-{dataset_name}-{evaluator_name}')
return tasks
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