Add an examples folder for code downstream tasks

examples/research_projects/codeparrot/README.md

@@ -12,7 +12,11 @@ This is an open-source effort to train and evaluate code generation models. Code
 - continuously push checkpoints to the hub with `huggingface_hub`
 - stream the dataset with `datasets` during training to avoid disk bottlenecks
 - apply the `code_eval` metric in `datasets` to evaluate on [OpenAI's _HumanEval_ benchmark](https://huggingface.co/datasets/openai_humaneval)
-
+- showcase examples for downstream tasks with code models in [examples](https://github.com/huggingface/transformers/tree/main/examples/research_projects/codeparrot/examples) folder:
+    - Algorithmic complexity prediction
+    - Code generation from english text
+    - Code explanation
+
 ## Installation
 To install the dependencies simply run the following command:
 ```bash

examples/research_projects/codeparrot/examples/README.md

@@ -0,0 +1,58 @@
+# Examples
+In this folder we showcase some examples to use code models for downstream tasks.
+
+## Complexity prediction
+In this task we want to predict the complexity of Java programs in [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex) dataset. Using Hugging Face `trainer`, we finetuned [multilingual CodeParrot](https://huggingface.co/codeparrot/codeparrot-small-multi) and [UniXcoder](https://huggingface.co/microsoft/unixcoder-base-nine) on it, and we used the latter to build this Java complexity prediction [space](https://huggingface.co/spaces/codeparrot/code-complexity-predictor) on Hugging Face hub.
+
+To fine-tune a model on this dataset you can use the following commands:
+
+```python
+python train_complexity_predictor.py \
+    --model_ckpt microsoft/unixcoder-base-nine \
+    --num_epochs 60 \
+    --num_warmup_steps 10 \
+    --batch_size 8 \
+    --learning_rate 5e-4 
+```
+
+## Code generation: text to python
+In this task we want to train a model to generate code from english text. We finetuned Codeparrot-small on [github-jupyter-text-to-code](https://huggingface.co/datasets/codeparrot/github-jupyter-text-to-code), a dataset where the samples are a succession of docstrings and their Python code, originally extracted from Jupyter notebooks parsed in this [dataset](https://huggingface.co/datasets/codeparrot/github-jupyter-parsed).
+
+To fine-tune a model on this dataset we use the same [script](https://github.com/huggingface/transformers/blob/main/examples/research_projects/codeparrot/scripts/codeparrot_training.py) as the pretraining of codeparrot:
+
+```python
+accelerate launch scripts/codeparrot_training.py \
+    --model_ckpt codeparrot/codeparrot-small \
+    --dataset_name_train codeparrot/github-jupyter-text-to-code \
+    --dataset_name_valid codeparrot/github-jupyter-text-to-code \
+    --train_batch_size 12 \
+    --valid_batch_size 12 \
+    --learning_rate 5e-4 \
+    --num_warmup_steps 100 \
+    --gradient_accumulation 1 \
+    --gradient_checkpointing False \
+    --max_train_steps 3000 \
+    --save_checkpoint_steps 200 \
+    --save_dir jupyter-text-to-python
+```
+
+## Code explanation: python to text
+In this task we want to train a model to explain python code. We finetuned Codeparrot-small on [github-jupyter-code-to-text](https://huggingface.co/datasets/codeparrot/github-jupyter-code-to-text), a dataset where the samples are a succession of Python code and its explanation as a docstring, we just inverted the order of text and code pairs in github-jupyter-code-to-text dataset and added the delimiters "Explanation:" and "End of explanation" inside the doctrings.
+
+To fine-tune a model on this dataset we use the same [script](https://github.com/huggingface/transformers/blob/main/examples/research_projects/codeparrot/scripts/codeparrot_training.py) as the pretraining of codeparrot:
+
+```python
+accelerate launch scripts/codeparrot_training.py \
+    --model_ckpt codeparrot/codeparrot-small \
+    --dataset_name_train codeparrot/github-jupyter-code-to-text \
+    --dataset_name_valid codeparrot/github-jupyter-code-to-text \
+    --train_batch_size 12 \
+    --valid_batch_size 12 \
+    --learning_rate 5e-4 \
+    --num_warmup_steps 100 \
+    --gradient_accumulation 1 \
+    --gradient_checkpointing False \
+    --max_train_steps 3000 \
+    --save_checkpoint_steps 200 \
+    --save_dir jupyter-python-to-text
+```

examples/research_projects/codeparrot/examples/requirements.txt

@@ -0,0 +1,5 @@
+datasets==2.3.2
+transformers==4.21.1
+wandb==0.13.1
+evaluate==0.2.2
+scikit-learn==1.1.2

examples/research_projects/codeparrot/examples/train_complexity_predictor.py

@@ -0,0 +1,132 @@
+import argparse
+from copy import deepcopy
+
+import numpy as np
+from datasets import ClassLabel, DatasetDict, load_dataset
+
+from evaluate import load
+from transformers import (
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    DataCollatorWithPadding,
+    Trainer,
+    TrainerCallback,
+    TrainingArguments,
+    set_seed,
+)
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_ckpt", type=str, default="microsoft/unixcoder-base-nine")
+    parser.add_argument("--num_epochs", type=int, default=5)
+    parser.add_argument("--batch_size", type=int, default=6)
+    parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
+    parser.add_argument("--freeze", type=bool, default=True)
+    parser.add_argument("--learning_rate", type=float, default=5e-4)
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--lr_scheduler_type", type=str, default="cosine")
+    parser.add_argument("--num_warmup_steps", type=int, default=10)
+    parser.add_argument("--weight_decay", type=float, default=0.01)
+    parser.add_argument("--output_dir", type=str, default="./results")
+    return parser.parse_args()
+
+
+metric = load("accuracy")
+
+
+def compute_metrics(eval_pred):
+    predictions, labels = eval_pred
+    predictions = np.argmax(predictions, axis=1)
+    return metric.compute(predictions=predictions, references=labels)
+
+
+class CustomCallback(TrainerCallback):
+    def __init__(self, trainer) -> None:
+        super().__init__()
+        self._trainer = trainer
+
+    def on_epoch_end(self, args, state, control, **kwargs):
+        if control.should_evaluate:
+            control_copy = deepcopy(control)
+            self._trainer.evaluate(eval_dataset=self._trainer.train_dataset, metric_key_prefix="train")
+            return control_copy
+
+
+def main():
+    args = get_args()
+    set_seed(args.seed)
+
+    dataset = load_dataset("codeparrot/codecomplex", split="train")
+    train_test = dataset.train_test_split(test_size=0.2)
+    test_validation = train_test["test"].train_test_split(test_size=0.5)
+    train_test_validation = DatasetDict(
+        {
+            "train": train_test["train"],
+            "test": test_validation["train"],
+            "valid": test_validation["test"],
+        }
+    )
+
+    print("Loading tokenizer and model")
+    tokenizer = AutoTokenizer.from_pretrained(args.model_ckpt)
+    tokenizer.pad_token = tokenizer.eos_token
+    model = AutoModelForSequenceClassification.from_pretrained(args.model_ckpt, num_labels=7)
+    model.config.pad_token_id = model.config.eos_token_id
+
+    if args.freeze:
+        for param in model.roberta.parameters():
+            param.requires_grad = False
+
+    labels = ClassLabel(num_classes=7, names=list(set(train_test_validation["train"]["complexity"])))
+
+    def tokenize(example):
+        inputs = tokenizer(example["src"], truncation=True, max_length=1024)
+        label = labels.str2int(example["complexity"])
+        return {
+            "input_ids": inputs["input_ids"],
+            "attention_mask": inputs["attention_mask"],
+            "label": label,
+        }
+
+    tokenized_datasets = train_test_validation.map(
+        tokenize,
+        batched=True,
+        remove_columns=train_test_validation["train"].column_names,
+    )
+    data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+
+    training_args = TrainingArguments(
+        output_dir=args.output_dir,
+        learning_rate=args.learning_rate,
+        lr_scheduler_type=args.lr_scheduler_type,
+        evaluation_strategy="epoch",
+        save_strategy="epoch",
+        logging_strategy="epoch",
+        per_device_train_batch_size=args.batch_size,
+        per_device_eval_batch_size=args.batch_size,
+        num_train_epochs=args.num_epochs,
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        weight_decay=0.01,
+        metric_for_best_model="accuracy",
+        run_name="complexity-java",
+        report_to="wandb",
+    )
+
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["valid"],
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+        compute_metrics=compute_metrics,
+    )
+
+    print("Training...")
+    trainer.add_callback(CustomCallback(trainer))
+    trainer.train()
+
+
+if __name__ == "__main__":
+    main()

examples/research_projects/codeparrot/scripts/minhash_deduplication.py

@@ -63,7 +63,6 @@ def add(self, code_key: Tuple, min_hash: MinHash) -> None:
 
         self._index.insert(code_key, min_hash)
         if len(close_duplicates) > 0:
-
             for base_duplicate in close_duplicates:
                 if base_duplicate in self._duplicate_clusters:
                     self._duplicate_clusters[base_duplicate].add(code_key)