precommits

model/supervised_finetuning/configs/config.yaml

@@ -64,4 +64,4 @@ debug:
   gradient_accumulation_steps: 1
   per_device_train_batch_size: 1
   per_device_eval_batch_size: 1
-  quantization: false
+  quantization: false

model/supervised_finetuning/custom_datasets/__init__.py

@@ -30,6 +30,7 @@ summarization_config_mapping = {
 QA_DATASETS = ["squad_v2", "adversarial_qa", "trivia_qa_context", "trivia_qa_noconext"]
 SUMMARIZATION_DATASETS = ["xsum", "cnn_dailymail", "samsum", "multi_news"]
 
+
 def index_squad_v2(example):
     return example["title"] + ". " + example["context"] + " " + example["question"], example["answers"]["text"][0]
 
@@ -159,4 +160,4 @@ def get_one_dataset(conf, dataset_name):
     else:
         raise ValueError(f"Unknown dataset {dataset_name}")
 
-    return train, eval
+    return train, eval

model/supervised_finetuning/requirements.txt

@@ -2,11 +2,11 @@ accelerate==0.15.0
 bitsandbytes==0.36.0.post2
 datasets==2.8.0
 deepspeed==0.7.7
+evaluate==0.4.0
 mpi4py==3.1.4
+nltk==3.8.1
 numpy==1.23.0
 PyYAML==6.0
 scikit_learn==1.2.0
 torch==1.13.1
 transformers==4.25.1
-evaluate==0.4.0
-nltk==3.8.1

model/supervised_finetuning/trainer.py

@@ -1,6 +1,6 @@
 import argparse
-import os
 from distutils.util import strtobool
+from functools import partial
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import bitsandbytes
@@ -9,7 +9,6 @@ from torch import nn
 from transformers import PreTrainedModel, Trainer, TrainingArguments
 from transformers.training_args import OptimizerNames
 from utils import get_dataset, get_loss, get_metrics, get_model, get_tokenizer, read_yamls
-from functools import partial
 
 
 def compute_metrics(eval_pred, preprocess_fns, metrics):

model/supervised_finetuning/utils.py

@@ -6,7 +6,7 @@ import nltk
 import numpy as np
 import transformers
 import yaml
-from custom_datasets import QA_SPECIAL_TOKENS, QA_DATASETS, SUMMARIZATION_DATASETS, get_one_dataset
+from custom_datasets import QA_DATASETS, QA_SPECIAL_TOKENS, SUMMARIZATION_DATASETS, get_one_dataset
 from custom_datasets.dialogue_collator import DialogueDataCollator
 from losses import CrossEntropyLoss, PolyLoss
 from models import freeze_top_n_layers, get_specific_model