Support Mbpp_plus dataset (#770)

* support mbpp+

* support mbpp+

* minor fix

* [Feat] minor fix

---------
Co-authored-by: yingfhu <yingfhu@gmail.com>

configs/datasets/mbpp_plus/mbpp_plus_gen.py

+from mmengine.config import read_base
+
+with read_base():
+    from.mbpp_plus_gen_94815c import mbpp_plus_datasets  # noqa: F401, F403
\ No newline at end of file

configs/datasets/mbpp_plus/mbpp_plus_gen_94815c.py

+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import ZeroRetriever
+from opencompass.openicl.icl_inferencer import GenInferencer
+from opencompass.datasets import MBPPEvaluator, MBPPPlusDataset
+
+mbpp_plus_reader_cfg = dict(
+    input_columns=['text', 'test_list'], output_column='task_id')
+
+mbpp_plus_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(
+            round=[
+                dict(
+                    role="HUMAN",
+                    prompt=
+                    "You are an expert Python programmer, and here is your task: Write a function to find the shared elements from the given two lists. Your code should pass these tests:\n\n assert similar_elements((3, 4, 5, 6),(5, 7, 4, 10)) == (4, 5)\n assert similar_elements((1, 2, 3, 4),(5, 4, 3, 7)) == (3, 4) \n assert similar_elements((11, 12, 14, 13),(17, 15, 14, 13)) == (13, 14) \n"
+                ),
+                dict(
+                    role="BOT",
+                    prompt=
+                    "[BEGIN]\n 'def similar_elements(test_tup1, test_tup2):\n  return tuple(set(test_tup1) & set(test_tup2))' \n[DONE] \n\n "
+                ),
+                dict(
+                    role="HUMAN",
+                    prompt=
+                    "You are an expert Python programmer, and here is your task: Write a python function to identify non-prime numbers. Your code should pass these tests:\n\n assert is_not_prime(2) == False \n assert is_not_prime(10) == True \n assert is_not_prime(35) == True \n"
+                ),
+                dict(
+                    role="BOT",
+                    prompt=
+                    "[BEGIN]\n 'import math\ndef is_not_prime(n):\n    if n == 1:\n        return True\n    for i in range(2, int(math.sqrt(n))+1):\n        if n % i == 0:\n            return True\n    return False' \n[DONE] \n\n "
+                ),
+                dict(
+                    role="HUMAN",
+                    prompt=
+                    "You are an expert Python programmer, and here is your task: Write a function to find the n largest integers from a given list of numbers, returned in descending order. Your code should pass these tests:\n\n assert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],3)==[85, 75, 65] \n assert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],2)==[85, 75] \n assert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],5)==[85, 75, 65, 58, 35] \n"
+                ),
+                dict(
+                    role="BOT",
+                    prompt=
+                    "[BEGIN]\n 'import heapq as hq\ndef heap_queue_largest(nums: list,n: int) -> list:\n  largest_nums = hq.nlargest(n, nums)\n  return largest_nums' \n[DONE] \n\n "
+                ),
+                dict(
+                    role="HUMAN",
+                    prompt=
+                    "You are an expert Python programmer, and here is your task: {text} Your code should pass these tests:\n\n {test_list}  \n"
+                ),
+                dict(role="BOT", prompt="[BEGIN]\n"),
+            ], )),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer, max_out_len=512))
+
+mbpp_plus_eval_cfg = dict(evaluator=dict(type=MBPPEvaluator, metric='MBPPPlus'), pred_role="BOT")
+
+mbpp_plus_datasets = [
+    dict(
+        type=MBPPPlusDataset,
+        abbr='mbpp_plus',
+        path='./data/mbpp_plus/mbpp_plus.jsonl',
+        reader_cfg=mbpp_plus_reader_cfg,
+        infer_cfg=mbpp_plus_infer_cfg,
+        eval_cfg=mbpp_plus_eval_cfg)
+]

opencompass/datasets/mbpp.py

 import contextlib
 import io
 import itertools
+import json
 import multiprocessing
+import os.path as osp
 import re
 import signal
+import tempfile
 from collections import defaultdict
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from typing import List, Sequence, Union
 
 import numpy as np
-from datasets import DatasetDict, concatenate_datasets, load_dataset
+from datasets import Dataset, DatasetDict, concatenate_datasets, load_dataset
 
 from opencompass.openicl.icl_evaluator import BaseEvaluator
 from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET
@@ -110,6 +113,35 @@ class SanitizedMBPPDataset(BaseDataset):
         return DatasetDict({'train': train, 'test': test})
 
 
+class MBPPPlusDataset(BaseDataset):
+
+    @staticmethod
+    def load(path: str, num_repeats: int = 1):
+        """Load mbpp dataset for pass k mode. Note that you can use
+        num_repeats.
+
+        > 1 when your model does not support `num_return_sequence` in
+        generation, otherwise use the raw mbpp dataset and set
+        `num_return_sequence` in model config to generate multiple responses
+        for testing pass@k>1.
+
+        It better to change your dataset abbr correspondingly if you want to
+        change num_repeats>1, otherwise the number in
+        `.cache/dataset_size.json` might be inconsistent.
+
+        Args:
+            num_repeats(int): Number of repetition for this dataset to get
+        multiple responses in special cases.
+        """
+
+        dataset = []
+        with open(path, 'r', encoding='utf-8') as f:
+            for line in f:
+                dataset.extend(
+                    [json.loads(line.strip()) for _ in range(num_repeats)])
+        return Dataset.from_list(dataset)
+
+
 class TimeOutException(Exception):
     pass
 
@@ -160,15 +192,21 @@ class redirect_stdin(contextlib._RedirectStream):  # type: ignore
 
 @ICL_EVALUATORS.register_module()
 class MBPPEvaluator(BaseEvaluator):
+    """Evaluator for MBPP or MBPPPlus."""
+
+    def __init__(self, metric: str = 'MBPP') -> None:
+        self.metric = metric
+        assert self.metric in ['MBPP', 'MBPPPlus']
 
     def score(self, predictions, references):
         assert len(predictions) == len(references)
         predictions = [self._process_answer(pred) for pred in predictions]
 
+        if self.metric == 'MBPP':
             result = {'pass': 0, 'timeout': 0, 'failed': 0, 'wrong_answer': 0}
             details = {}
-        for index, (test_case, pred) in enumerate(zip(references,
-                                                      predictions)):
+            for index, (test_case,
+                        pred) in enumerate(zip(references, predictions)):
                 programs = self._process_test(test_case, pred)
                 try:
                     # Add exec globals to prevent the exec to raise
@@ -190,6 +228,39 @@ class MBPPEvaluator(BaseEvaluator):
             result['score'] = result['pass'] / len(predictions) * 100
             result['details'] = details
             return result
+        else:
+            try:
+                from evalplus.data import write_jsonl
+                from evalplus.evaluate import evaluate
+                self.write_jsonl = write_jsonl
+                self.eval = evaluate
+            except ImportError:
+                raise ImportError(
+                    'Please install evalplus use following steps:\n'
+                    'git clone --recurse-submodules git@github.com:open-compass/human-eval.git\n'  # noqa
+                    'cd human-eval\n'
+                    'pip install -e .\n'
+                    'pip install -e evalplus\n')
+            mbpp_preds = []
+            for preds, refer in zip(predictions, references):
+                if not isinstance(preds, list):
+                    preds = [preds]
+                for pred in preds:
+                    mbpp_preds.append({'task_id': refer, 'solution': pred})
+            with tempfile.TemporaryDirectory() as tmp_dir:
+                out_dir = osp.join(tmp_dir, 'mbpp_eval.jsonl')
+                self.write_jsonl(out_dir, mbpp_preds)
+                flags = dict(dataset='mbpp',
+                             samples=out_dir,
+                             base_only=None,
+                             parallel=None,
+                             i_just_wanna_run=None,
+                             test_details=0.2,
+                             min_time_limit=0.2,
+                             gt_time_limit_factor=4.0,
+                             mini=None)
+                score = self.eval(flags)
+                return {f'mbpp_plus_{k}': score[k] * 100 for k in score}
 
     def _process_answer(self, text):
         text = text.strip()