Merge pull request #19 from boostcampaitech7/version_1.1

Feat: add --testing argument_parser and add requirements.txt

.gitignore

@@ -1,2 +1,4 @@
 data/
-__pycache__/
+__pycache__/
+models/
+outputs/

ReadMe.md

@@ -0,0 +1,54 @@
+## version 설명
+- 본 버전은 version_0.0으로 가정한 기본 제공된 BaseLineCode에서 준성님이 재구조화 시킨 version_1.0을 기반으로 작동하며
+- 해당 버전에 있어 https://github.com/boostcampaitech7/level2-mrc-nlp-01/pull/10 에 기록된 바, `Sparse_retrieval.py` 를 다소 수정하여 retriever 성능 평가 기능을 정상 작동시킴.
+- `requirements.txt` 추가
+- 코드 수정: train.py
+    ```
+    from datasets import load_from_disk, load_metric 
+    ```
+    위 코드를
+    ```
+    from datasets import load_from_disk
+    from evaluate import load as load_metric
+    ```
+    위와 같이 변경
+- 코드 수정: config.py
+    ```
+    class Config:
+    def __init__(self, config_dict=None, path='./config.yaml'):
+    ```
+    위 부분을
+    ```
+    class Config:
+    def __init__(self, config_dict=None, path='../config.yaml'):
+    ```
+    와 같이 현재 파일구조와 맞춤.
+
+- 코드 수정: config.yaml
+    경로 관련하여 
+    CLI에서 실행시킬 `train.py`를
+    `/data/ephemeral/home/project/version_1.1/src/train.py` 와 같이 입력시키면 `permission denied` 문제가 있어서
+    그냥 터미널을 src 폴더 들어가서 실행시키는 것으로 하고 config.yaml의
+    `./data/train_dataset` 라고 되어있던 부분을
+    `../data/train_dataset` 이렇게 바꿈.
+    원래대로 `python train.py --output_dir ./models/train_dataset --do_train`
+    명령하면 이제 됨.
+
+- 기능 추가: `--testing`
+    관련하여 전체 데이터셋을 사용하지 않고 testing 할 수 있도록 argument parser 추가
+    - `train.py`와 `inference.py`, `sparse_retrieval.py`에 해당 부분이 적용될 수 있도록 코드 수정하였고
+    - 구체적으로는 datasets, wikipedia_documents 불러오는 부분을 1%만 불러오게끔 수정함.
+
+- 코드 수정: `.gitignore`에 `__pycache__` 추가함.
+
+
+## 구체적인 적용점 (GitHub Issue)
+- Ljb issue 09 #10 
+
+# 실행 방법
+- train의 경우
+    python train.py --output_dir ./models/train_dataset --do_train `--testing`(선택)
+- eval의 경우
+    python train.py --output_dir ./outputs/train_dataset  --do_eval `--testing`(선택)
+- inference의 경우
+    python inference.py --output_dir ./outputs/test_dataset/ --dataset_name ../data/test_dataset  --do_predict `--testing`(선택)

config.yaml

@@ -40,3 +40,6 @@ dataQA:
     # - method: your-augementation-method
     #   params:
     #     p: 0.0
+
+testing:
+

requirements.txt

@@ -0,0 +1,108 @@
+accelerate==1.0.1
+aiohappyeyeballs==2.4.3
+aiohttp==3.10.10
+aiosignal==1.3.1
+asttokens==2.0.5  # Ensure this version or the version you need
+astunparse==1.6.3
+async-timeout==4.0.3
+attrs==23.1.0
+backcall==0.2.0
+beautifulsoup4==4.11.1
+boltons==23.0.0
+brotlipy==0.7.0
+certifi==2022.6.15
+cffi==1.15.1
+cryptography
+chardet==4.0.0
+charset-normalizer==2.1.1
+click==8.1.3
+cryptography==38.0.0
+datasets==3.0.1
+debugpy==1.6.3
+decorator==5.1.1
+dill==0.3.8
+dnspython==2.4.2
+entrypoints==0.3
+evaluate==0.4.3
+exceptiongroup==1.0.0
+executing==0.8.3
+expecttest==0.1.6
+filelock==3.8.0
+frozenlist==1.4.1
+fsspec==2023.9.2
+hypothesis==6.87.1
+idna==3.3
+ipykernel==6.15.2
+ipython==8.6.0
+jedi==0.18.1
+Jinja2==3.0.3
+jsonpatch==1.32
+jsonpointer==2.3
+jupyter-client==7.3.5
+jupyter_core==4.10.0
+libarchive-c==4.0
+MarkupSafe==2.1.1
+matplotlib-inline==0.1.6
+more-itertools==8.14.0
+mpmath==1.2.1
+multidict==6.1.0
+multiprocess==0.70.16
+nest_asyncio==1.5.5
+networkx==2.8.4
+numpy==1.26.0
+packaging==21.3
+pandas==2.2.3
+parso==0.8.3
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==9.2.0
+pkginfo==1.8.3
+platformdirs==2.5.2
+pluggy==1.0.0
+prompt-toolkit==3.0.31
+propcache==0.2.0
+psutil==5.9.1
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pyarrow==15.0.0
+pycparser==2.21
+Pygments==2.12.0
+pyOpenSSL==22.1.0
+PySocks==1.7.1
+python-dateutil==2.8.2
+python-etcd==0.4.5
+pytz==2022.1
+PyYAML==6.0
+pyzmq==23.1.0
+regex==2024.9.11
+requests==2.32.3
+ruamel.yaml==0.17.21
+ruamel.yaml.clib==0.2.6
+safetensors==0.4.3
+six==1.16.0
+sortedcontainers==2.4.0
+soupsieve==2.3.2.post1
+stack-data==0.2.0
+sympy==1.10.1
+tokenizers
+tomli==2.0.1
+toolz==0.12.0
+torch==2.1.0
+torchaudio==2.1.0
+torchelastic==0.2.2
+torchvision==0.16.0
+tornado==6.2
+tqdm==4.66.3
+traitlets==5.5.0
+transformers==4.45.2
+triton==2.1.0
+truststore==0.8.0
+types-dataclasses==0.6.6
+typing_extensions==4.1.1
+tzdata==2024.2
+urllib3==1.26.11
+wcwidth==0.2.5
+xxhash==3.5.0
+yarl==1.12.0
+faiss-gpu
+scikit-learn

src/Retrieval/sparse_retrieval.py

@@ -31,6 +31,7 @@ def __init__(
         self,
         tokenize_fn,
         context_path: Optional[str] = "wikipedia_documents.json",
+        testing: bool = False
     ) -> NoReturn:
 
         """
@@ -53,12 +54,19 @@ def __init__(
         Summary:
             Passage 파일을 불러오고 TfidfVectorizer를 선언하는 기능을 합니다.
         """
-
+        self.testing = testing
         data_path = os.path.dirname(context_path)
         context_path = os.path.basename(context_path)
         self.data_path = data_path
         with open(os.path.join(data_path, context_path), "r", encoding="utf-8") as f:
             wiki = json.load(f)
+
+        if self.testing:
+            # 1%만 선택한다.
+            total_documents = len(wiki)
+            num_documents = int(0.01 * total_documents)
+            wiki = {k: wiki[k] for k in list(wiki.keys())[:num_documents]}
+
 
         self.contexts = list(
             dict.fromkeys([v["text"] for v in wiki.values()])

src/config.py

@@ -64,4 +64,4 @@ def __call__(self, default_value=None):
         if default_value is not None:
             return default_value
 
-        return None
+        return None

src/inference.py

@@ -2,10 +2,13 @@
 import sys
 import random
 import logging
-
+import argparse
 import numpy as np
 import torch
-from datasets import load_from_disk, load_metric
+
+from datasets import load_from_disk
+from evaluate import load as load_metric
+
 from transformers import (
     AutoConfig,
     AutoModelForQuestionAnswering,
@@ -21,6 +24,8 @@
 from QuestionAnswering.trainer import QuestionAnsweringTrainer
 from QuestionAnswering.tokenizer_wrapper import QuestionAnsweringTokenizerWrapper
 from Retrieval.sparse_retrieval import SparseRetrieval
+from dataclasses import dataclass, field
+
 
 def set_all_seed(seed, deterministic=False):
     random.seed(seed)
@@ -32,6 +37,11 @@ def set_all_seed(seed, deterministic=False):
         torch.backends.cudnn.deterministic = True
         torch.backends.cudnn.benchmark = False
 
+@dataclass
+class DataArguments:
+    dataset_name: str = field(default=None, metadata={"help": "The name of the dataset to use."})
+    testing: bool = field(default=False, metadata={"help": "Use only 1% of the dataset for testing"})
+
 def main():
     config = Config()
 
@@ -44,13 +54,23 @@ def main():
         handlers=[logging.StreamHandler(sys.stdout)],
     )
 
-    parser = HfArgumentParser((TrainingArguments))
-    training_args = parser.parse_args_into_dataclasses()[0]
+    parser = HfArgumentParser((TrainingArguments, DataArguments))
+    training_args, data_args = parser.parse_args_into_dataclasses()
     training_args.do_train = True
 
     logger.info("Training/evaluation parameters %s", training_args)
 
-    datasets = load_from_disk(config.dataQA.path())
+    if data_args.dataset_name:
+        datasets = load_from_disk(data_args.dataset_name)
+    else:
+        datasets = load_from_disk(config.dataQA.path())
+
+    if data_args.testing:
+        # 1%만 선택
+        num_valid_samples = len(datasets["validation"])
+        valid_samples = int(0.01 * num_valid_samples)
+        datasets["validation"] = datasets["validation"].select(range(valid_samples))
+
     print(datasets)
 
     config_hf = AutoConfig.from_pretrained(config.model.name())
@@ -61,6 +81,7 @@ def main():
         retriever = SparseRetrieval(
             tokenize_fn=tokenizer.tokenize,
             context_path=config.dataRetrieval.context_path(),
+            testing = data_args.testing
         )
         datasets = retriever.run(datasets, training_args, config)
 

src/train.py

@@ -2,10 +2,13 @@
 import sys
 import random
 import logging
-
+import argparse
 import numpy as np
 import torch
-from datasets import load_from_disk, load_metric
+# from datasets import load_from_disk, load_metric 
+from datasets import load_from_disk
+from evaluate import load as load_metric
+
 from transformers import (
     AutoConfig,
     AutoModelForQuestionAnswering,
@@ -54,14 +57,29 @@ def main():
         handlers=[logging.StreamHandler(sys.stdout)],
     )
 
-    parser = HfArgumentParser((TrainingArguments))
-    training_args = parser.parse_args_into_dataclasses()[0]
-
+    arg_parser = argparse.ArgumentParser()
+    arg_parser.add_argument("--testing", action='store_true', help="Use only 1% of the dataset for testing")
+    args, unknown = arg_parser.parse_known_args()    
+
+    parser = HfArgumentParser((TrainingArguments,))
+    training_args = parser.parse_args_into_dataclasses(unknown)[0]
     training_args = set_hyperparameters(config, training_args)
 
     logger.info("Training/evaluation parameters %s", training_args)
 
     datasets = load_from_disk(config.dataQA.path())
+
+    if args.testing:
+        # 1%만 선택
+        num_train_samples = len(datasets['train'])
+        num_valid_samples = len(datasets["validation"])
+        test_samples = int(0.01 * num_train_samples)
+        valid_samples = int(0.01 * num_valid_samples)
+
+        datasets["train"] = datasets["train"].select(range(test_samples))
+        datasets["validation"] = datasets["validation"].select(range(valid_samples))
+
+
     print(datasets)
 
     config_hf = AutoConfig.from_pretrained(config.model.name())