[Update] Add dataset configurations of no max_out_len

.github/workflows/pr-stage-check.yml

@@ -20,7 +20,7 @@ jobs:
       matrix:
         python-version: ['3.10']
         include:
-          - torch: 2.0.0
+          - torch: 2.5.1
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python ${{ matrix.python-version }}
@@ -30,7 +30,7 @@ jobs:
       - name: Upgrade pip
         run: python -m pip install --upgrade pip
       - name: Install PyTorch
-        run: pip install torch==${{matrix.torch}}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
+        run: pip install torch==${{matrix.torch}} -f https://download.pytorch.org/whl/cpu/torch_stable.html
       - name: Install system dependencies
         run: |
           sudo sed -i '$ a deb http://th.archive.ubuntu.com/ubuntu jammy main' /etc/apt/sources.list
@@ -106,7 +106,7 @@ jobs:
       - name: Upgrade pip
         run: python -m pip install pip --upgrade
       - name: Install PyTorch
-        run: pip install torch==2.0.0+${{matrix.platform}} -f https://download.pytorch.org/whl/${{matrix.platform}}/torch_stable.html
+        run: pip install torch==2.5.1 -f https://download.pytorch.org/whl/cpu/torch_stable.html
       - name: Install opencompass dependencies
         run: |
           pip install -r requirements.txt

opencompass/configs/datasets/ARC_Prize_Public_Evaluation/arc_prize_public_evaluation_gen_fedd04.py

@@ -0,0 +1,56 @@
+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.arc_prize_public_evaluation import ARCPrizeDataset, ARCPrizeEvaluator
+
+
+# The system_prompt defines the initial instructions for the model, 
+# setting the context for solving ARC tasks.
+system_prompt = '''You are a puzzle solving wizard. You are given a puzzle from the abstraction and reasoning corpus developed by Francois Chollet.'''
+
+# User message template is a template for creating user prompts. It includes placeholders for training data and test input data, 
+# guiding the model to learn the rule and apply it to solve the given puzzle.
+user_message_template = '''Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:
+----------------------------------------
+{training_data}
+----------------------------------------
+Now, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:
+----------------------------------------
+[{{'input': {input_test_data}, 'output': [[]]}}]
+----------------------------------------
+What is the output grid? Only provide the output grid in the form as in the example input and output pairs. Do not provide any additional information:'''
+
+
+arc_prize_public_evaluation_reader_cfg = dict(
+    input_columns=['training_data', 'input_test_data'], 
+    output_column='output_test_data'
+)
+
+arc_prize_public_evaluation_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(
+            round=[
+                dict(role='SYSTEM',fallback_role='HUMAN', prompt=system_prompt),
+                dict(role='HUMAN', prompt=user_message_template),
+            ],
+        )
+    ),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer)
+)
+
+arc_prize_public_evaluation_eval_cfg = dict(
+    evaluator=dict(type=ARCPrizeEvaluator)
+)
+
+arc_prize_public_evaluation_datasets = [
+    dict(
+        abbr='ARC_Prize_Public_Evaluation',
+        type=ARCPrizeDataset,
+        path='opencompass/arc_prize_public_evaluation',
+        reader_cfg=arc_prize_public_evaluation_reader_cfg,
+        infer_cfg=arc_prize_public_evaluation_infer_cfg,
+        eval_cfg=arc_prize_public_evaluation_eval_cfg
+    )
+]

opencompass/configs/datasets/GaokaoBench/GaokaoBench_no_subjective_gen_d16acb.py

@@ -0,0 +1,45 @@
+import os
+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 GaokaoBenchDataset
+from mmengine.config import read_base
+
+with read_base():
+    from .GaokaoBench_prompts import MCQ_prompts, FBQ_prompts
+
+GaokaoBench_datasets = []
+for folder, prompts in [
+    ('Multiple-choice_Questions', MCQ_prompts),
+    ('Fill-in-the-blank_Questions', FBQ_prompts),
+]:
+    for p in prompts:
+        reader_cfg = {
+            'input_columns': ['question'],
+            'output_column': 'answer',
+        }
+        infer_cfg = {
+            'ice_template': {
+                'type': PromptTemplate,
+                'template': {'round': [{'role': 'HUMAN', 'prompt': p['prefix_prompt'] + '{question}'}]},
+                'ice_token': '</E>',
+            },
+            'retriever': {'type': ZeroRetriever},
+            'inferencer': {'type': GenInferencer},
+        }
+        eval_cfg = {
+            'evaluator': {'type': 'GaokaoBenchEvaluator' + '_' + p['type']},
+            'pred_role': 'BOT',
+        }
+        _base_path = 'opencompass/GAOKAO-BENCH'
+        dataset = {
+            'type': GaokaoBenchDataset,
+            'abbr': 'GaokaoBench_' + p['keyword'],
+            'path': _base_path,
+            'filename': '/' + folder + '/' + p['keyword'] + '.json',
+            'name': p['keyword'],
+            'reader_cfg': reader_cfg,
+            'infer_cfg': infer_cfg,
+            'eval_cfg': eval_cfg,
+        }
+        GaokaoBench_datasets.append(dataset)

opencompass/configs/datasets/MathBench/mathbench_2024_gen_4b8f28.py

@@ -0,0 +1,81 @@
+from mmengine.config import read_base
+from copy import deepcopy
+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import ZeroRetriever
+from opencompass.openicl.icl_inferencer import GenInferencer, PPLInferencer
+from opencompass.openicl.icl_evaluator import CircularEvaluator, AccEvaluator
+from opencompass.datasets import MathBenchDataset, math_postprocess_v2
+from opencompass.utils.text_postprocessors import first_option_postprocess
+
+with read_base():
+    from .mathbench_prompt import zero_shot_prompts, few_shot_prompts, mathbench_sets
+
+# Max for this dataset is 4
+num_shot = 0
+# Generate reasoning path or not, only for single choice
+with_reasoning = True
+# Use circular evaluation or not
+with_circular_eval = True
+# Use PPL mode in single choice test or not
+use_ppl_single_choice = False
+
+assert 0 <= num_shot <= 4
+if num_shot == 0:
+    prompts = zero_shot_prompts
+else:
+    prompts = {name: p[- 2 * num_shot - 2:] for name, p in few_shot_prompts.items()}
+
+mathbench_datasets = []
+for _split in mathbench_sets:
+    for _name in mathbench_sets[_split]:
+        if 'single_choice' in _name:
+            if with_reasoning:
+                template_round = prompts[_name + '_with_reasoning']
+            else:
+                template_round = prompts[_name]
+        else:
+            template_round = prompts[_name]
+
+        if 'single_choice' in _name:
+            pred_postprocessor = dict(type=first_option_postprocess, options='ABCD')
+        else:
+            pred_postprocessor = dict(type=math_postprocess_v2)
+
+        if 'single_choice' in _name and with_circular_eval:
+            evaluator = dict(type=CircularEvaluator)
+        else:
+            evaluator = dict(type=AccEvaluator)
+
+        # assemble the final config
+        mathbench_reader_cfg = dict(input_columns=['question'], output_column='answer')
+        if use_ppl_single_choice and 'single_choice' in _name and not with_reasoning:
+            template = {}
+            for answer in ['A', 'B', 'C', 'D']:
+                one_template_round = deepcopy(template_round)
+                one_template_round['round'][-1]['prompt'] = one_template_round['round'][-1]['prompt'].format(answer=answer)
+                template[answer] = dict(round=one_template_round)
+            mathbench_infer_cfg = dict(
+                prompt_template=dict(type=PromptTemplate, template=template),
+                retriever=dict(type=ZeroRetriever),
+                inferencer=dict(type=PPLInferencer),
+            )
+        else:
+            mathbench_infer_cfg = dict(
+                prompt_template=dict(type=PromptTemplate, template=dict(round=template_round)),
+                retriever=dict(type=ZeroRetriever),
+                inferencer=dict(type=GenInferencer),
+            )
+        mathbench_eval_cfg = dict(evaluator=evaluator, pred_postprocessor=pred_postprocessor)
+
+        mathbench_datasets.append(
+            dict(
+                abbr='mathbench-' + _split + '-' + _name,
+                type=MathBenchDataset,
+                path=f'data/mathbench_v1/{_split}',
+                name=_name,
+                with_circular=with_circular_eval,
+                reader_cfg=mathbench_reader_cfg,
+                infer_cfg=mathbench_infer_cfg,
+                eval_cfg=mathbench_eval_cfg,
+            )
+        )