[Feature] Add heuristic size partitioner (#63)

* [Feature] Add heuristic size partitioner

* update

docs/en/tools.md

@@ -57,3 +57,15 @@ Running method:
 ```bash
 python tools/test_api_model.py [CONFIG_PATH] -n
 ```
+
+## Prediction Merger
+
+This tool can merge patitioned predictions.
+
+Running method:
+
+```bash
+python tools/prediction_merger.py CONFIG_PATH [-w WORK_DIR]
+```
+
+- `-w`: Work path, default is `'./outputs/default'`.

docs/zh_cn/tools.md

@@ -66,3 +66,15 @@ python tools/case_analyzer.py CONFIG_PATH [-w WORK_DIR]
 ```bash
 python tools/test_api_model.py [CONFIG_PATH] -n
 ```
+
+## Prediction Merger
+
+本工具可以合并由于 `partitioner` 而产生的分片推理结果。
+
+运行方式：
+
+```bash
+python tools/prediction_merger.py CONFIG_PATH [-w WORK_DIR]
+```
+
+- `-w`：工作路径，默认为 `'./outputs/default'`。

opencompass/partitioners/size.py

@@ -1,6 +1,7 @@
 import copy
 import math
 import os.path as osp
+from fnmatch import fnmatch
 from typing import List, Tuple, Union
 
 import mmengine
@@ -134,6 +135,30 @@ class SizePartitioner(BasePartitioner):
             split_configs.append(cfg)
         return split_configs
 
+    def get_factor(self, dataset: ConfigDict) -> int:
+        infer_cfg = dataset.infer_cfg
+        template = (infer_cfg.prompt_template.template if 'prompt_template'
+                    in infer_cfg else infer_cfg.ice_template.template)
+        # If it's the Gen template, the dataset size will be multiplied by the
+        # self.gen_task_coef
+        factor = self.gen_task_coef
+        # If it's the PPL template, the dataset size will be multiplied by the
+        # number of labels
+        if isinstance(template, dict):
+            ctr = sum(key in template for key in ('begin', 'round', 'end'))
+            if ctr != len(template.keys()):
+                factor = len(template.keys())
+
+        dataset_abbr = dataset_abbr_from_cfg(dataset)
+        if any(
+                fnmatch(dataset_abbr, pattern)
+                for pattern in ('bbh*', 'gsm8k*', 'math*', 'strategyqa*',
+                                'agieval-jec*', 'agieval-gaokao-mathcloze',
+                                'agieval-math')):
+            factor *= 10
+
+        return factor
+
     def get_cost(self,
                  dataset: ConfigDict,
                  get_raw_factors: bool = False) -> Union[int, Tuple[int, int]]:
@@ -150,19 +175,8 @@ class SizePartitioner(BasePartitioner):
         """
         dataset_abbr = dataset_abbr_from_cfg(dataset)
 
-        # If it's the PPL template, the dataset size will be multiplied by the
-        # number of labels
-        infer_cfg = dataset.infer_cfg
         test_range = dataset.reader_cfg.get('test_range', '')
-        template = (infer_cfg.prompt_template.template if 'prompt_template'
+        factor = self.get_factor(dataset)
-                    in infer_cfg else infer_cfg.ice_template.template)
-        # If it's the Gen template, the dataset size will be multiplied by the
-        # self.gen_task_coef
-        factor = self.gen_task_coef
-        if isinstance(template, dict):
-            ctr = sum(key in template for key in ('begin', 'round', 'end'))
-            if ctr != len(template.keys()):
-                factor = len(template.keys())
 
         if dataset_abbr in self.dataset_size:
             actual_size = eval('len(range(self.dataset_size[dataset_abbr])'

tools/prediction_merger.py

@@ -0,0 +1,100 @@
+import argparse
+import copy
+import json
+import os.path as osp
+
+import mmengine
+from mmengine.config import Config, ConfigDict
+
+from opencompass.utils import build_dataset_from_cfg, get_infer_output_path
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Run an evaluation task')
+    parser.add_argument('config', help='Train config file path')
+    parser.add_argument('-w',
+                        '--work-dir',
+                        help='Work path, all the outputs will be '
+                        'saved in this path, including the slurm logs, '
+                        'the evaluation results, the summary results, etc.'
+                        'If not specified, the work_dir will be set to '
+                        './outputs/default.',
+                        default=None,
+                        type=str)
+    args = parser.parse_args()
+    return args
+
+
+class PredictionMerger:
+    """"""
+
+    def __init__(self, cfg: ConfigDict) -> None:
+
+        self.cfg = cfg
+        self.model_cfg = copy.deepcopy(self.cfg['model'])
+        self.dataset_cfg = copy.deepcopy(self.cfg['dataset'])
+        self.work_dir = self.cfg.get('work_dir')
+
+    def run(self):
+        filename = get_infer_output_path(
+            self.model_cfg, self.dataset_cfg,
+            osp.join(self.work_dir, 'predictions'))
+        root, ext = osp.splitext(filename)
+        partial_filename = root + '_0' + ext
+
+        if osp.exists(osp.realpath(filename)):
+            return
+
+        if not osp.exists(osp.realpath(partial_filename)):
+            print(f'{filename} not found')
+            return
+
+        # Load predictions
+        partial_filenames = []
+        if osp.exists(osp.realpath(filename)):
+            preds = mmengine.load(filename)
+        else:
+            preds, offset = {}, 0
+            i = 1
+            while osp.exists(osp.realpath(partial_filename)):
+                partial_filenames.append(osp.realpath(partial_filename))
+                _preds = mmengine.load(partial_filename)
+                partial_filename = root + f'_{i}' + ext
+                i += 1
+                for _o in range(len(_preds)):
+                    preds[str(offset)] = _preds[str(_o)]
+                    offset += 1
+
+        dataset = build_dataset_from_cfg(self.dataset_cfg)
+        if len(preds) != len(dataset.test):
+            print('length mismatch')
+            return
+
+        print(f'Merge {partial_filenames} to {filename}')
+        with open(filename, 'w', encoding='utf-8') as f:
+            json.dump(preds, f, indent=4, ensure_ascii=False)
+
+
+def dispatch_tasks(cfg):
+    for model in cfg['models']:
+        for dataset in cfg['datasets']:
+            PredictionMerger({
+                'model': model,
+                'dataset': dataset,
+                'work_dir': cfg['work_dir']
+            }).run()
+
+
+def main():
+    args = parse_args()
+    cfg = Config.fromfile(args.config)
+    # set work_dir
+    if args.work_dir is not None:
+        cfg['work_dir'] = args.work_dir
+    else:
+        cfg.setdefault('work_dir', './outputs/default')
+    dispatch_tasks(cfg)
+
+
+if __name__ == '__main__':
+    main()