--- language: - en license: apache-2.0 --- # Model Card for UniXcoder-base # Model Details ## Model Description UniXcoder is a unified cross-modal pre-trained model that leverages multimodal data (i.e. code comment and AST) to pretrain code representation. - **Developed by:** Microsoft Team - **Shared by [Optional]:** Hugging Face - **Model type:** Feature Engineering - **Language(s) (NLP):** en - **License:** Apache-2.0 - **Related Models:** - **Parent Model:** RoBERTa - **Resources for more information:** - [Associated Paper](https://arxiv.org/abs/2203.03850) # Uses ## 1. Dependency - pip install torch - pip install transformers ## 2. Quick Tour We implement a class to use UniXcoder and you can follow the code to build UniXcoder. You can download the class by ```shell wget https://raw.githubusercontent.com/microsoft/CodeBERT/master/UniXcoder/unixcoder.py ``` ```python import torch from unixcoder import UniXcoder device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = UniXcoder("microsoft/unixcoder-base") model.to(device) ``` In the following, we will give zero-shot examples for several tasks under different mode, including **code search (encoder-only)**, **code completion (decoder-only)**, **function name prediction (encoder-decoder)** , **API recommendation (encoder-decoder)**, **code summarization (encoder-decoder)**. ## 3. Encoder-only Mode For encoder-only mode, we give an example of **code search**. ### 1) Code and NL Embeddings Here, we give an example to obtain code fragment embedding from CodeBERT. ```python # Encode maximum function func = "def f(a,b): if a>b: return a else return b" tokens_ids = model.tokenize([func],max_length=512,mode="") source_ids = torch.tensor(tokens_ids).to(device) tokens_embeddings,max_func_embedding = model(source_ids) # Encode minimum function func = "def f(a,b): if a") source_ids = torch.tensor(tokens_ids).to(device) tokens_embeddings,min_func_embedding = model(source_ids) # Encode NL nl = "return maximum value" tokens_ids = model.tokenize([nl],max_length=512,mode="") source_ids = torch.tensor(tokens_ids).to(device) tokens_embeddings,nl_embedding = model(source_ids) print(max_func_embedding.shape) print(max_func_embedding) ``` ```python torch.Size([1, 768]) tensor([[ 8.6533e-01, -1.9796e+00, -8.6849e-01, 4.2652e-01, -5.3696e-01, -1.5521e-01, 5.3770e-01, 3.4199e-01, 3.6305e-01, -3.9391e-01, -1.1816e+00, 2.6010e+00, -7.7133e-01, 1.8441e+00, 2.3645e+00, ..., -2.9188e+00, 1.2555e+00, -1.9953e+00, -1.9795e+00, 1.7279e+00, 6.4590e-01, -5.2769e-02, 2.4965e-01, 2.3962e-02, 5.9996e-02, 2.5659e+00, 3.6533e+00, 2.0301e+00]], device='cuda:0', grad_fn=) ``` ### 2) Similarity between code and NL Now, we calculate cosine similarity between NL and two functions. Although the difference of two functions is only a operator (```<``` and ```>```), UniXcoder can distinguish them. ```python # Normalize embedding norm_max_func_embedding = torch.nn.functional.normalize(max_func_embedding, p=2, dim=1) norm_min_func_embedding = torch.nn.functional.normalize(min_func_embedding, p=2, dim=1) norm_nl_embedding = torch.nn.functional.normalize(nl_embedding, p=2, dim=1) max_func_nl_similarity = torch.einsum("ac,bc->ab",norm_max_func_embedding,norm_nl_embedding) min_func_nl_similarity = torch.einsum("ac,bc->ab",norm_min_func_embedding,norm_nl_embedding) print(max_func_nl_similarity) print(min_func_nl_similarity) ``` ```python tensor([[0.3002]], device='cuda:0', grad_fn=) tensor([[0.1881]], device='cuda:0', grad_fn=) ``` ## 3. Decoder-only Mode For decoder-only mode, we give an example of **code completion**. ```python context = """ def f(data,file_path): # write json data into file_path in python language """ tokens_ids = model.tokenize([context],max_length=512,mode="") source_ids = torch.tensor(tokens_ids).to(device) prediction_ids = model.generate(source_ids, decoder_only=True, beam_size=3, max_length=128) predictions = model.decode(prediction_ids) print(context+predictions[0][0]) ``` ```python def f(data,file_path): # write json data into file_path in python language data = json.dumps(data) with open(file_path, 'w') as f: f.write(data) ``` ## 4. Encoder-Decoder Mode For encoder-decoder mode, we give two examples including: **function name prediction**, **API recommendation**, **code summarization**. ### 1) **Function Name Prediction** ```python context = """ def (data,file_path): data = json.dumps(data) with open(file_path, 'w') as f: f.write(data) """ tokens_ids = model.tokenize([context],max_length=512,mode="") source_ids = torch.tensor(tokens_ids).to(device) prediction_ids = model.generate(source_ids, decoder_only=False, beam_size=3, max_length=128) predictions = model.decode(prediction_ids) print([x.replace("","").strip() for x in predictions[0]]) ``` ```python ['write_json', 'write_file', 'to_json'] ``` ### 2) API Recommendation ```python context = """ def write_json(data,file_path): data = (data) with open(file_path, 'w') as f: f.write(data) """ tokens_ids = model.tokenize([context],max_length=512,mode="") source_ids = torch.tensor(tokens_ids).to(device) prediction_ids = model.generate(source_ids, decoder_only=False, beam_size=3, max_length=128) predictions = model.decode(prediction_ids) print([x.replace("","").strip() for x in predictions[0]]) ``` ```python ['json.dumps', 'json.loads', 'str'] ``` ### 3) Code Summarization ```python context = """ # def write_json(data,file_path): data = json.dumps(data) with open(file_path, 'w') as f: f.write(data) """ tokens_ids = model.tokenize([context],max_length=512,mode="") source_ids = torch.tensor(tokens_ids).to(device) prediction_ids = model.generate(source_ids, decoder_only=False, beam_size=3, max_length=128) predictions = model.decode(prediction_ids) print([x.replace("","").strip() for x in predictions[0]]) ``` ```python ['Write JSON to file', 'Write json to file', 'Write a json file'] ``` # Reference If you use this code or UniXcoder, please consider citing us.

@article{guo2022unixcoder,
  title={UniXcoder: Unified Cross-Modal Pre-training for Code Representation},
  author={Guo, Daya and Lu, Shuai and Duan, Nan and Wang, Yanlin and Zhou, Ming and Yin, Jian},
  journal={arXiv preprint arXiv:2203.03850},
  year={2022}
}

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