Salesforce / codet5p-110m-embedding

CodeT5+ 110M Embedding Models

Model description

CodeT5+ is a new family of open code large language models with an encoder-decoder architecture that can flexibly operate in different modes (i.e. encoder-only , decoder-only , and encoder-decoder ) to support a wide range of code understanding and generation tasks. It is introduced in the paper:

CodeT5+: Open Code Large Language Models for Code Understanding and Generation by Yue Wang *, Hung Le *, Akhilesh Deepak Gotmare , Nghi D.Q. Bui , Junnan Li , Steven C.H. Hoi (* indicates equal contribution).

Compared to the original CodeT5 family (base: 220M , large: 770M ), CodeT5+ is pretrained with a diverse set of pretraining tasks including span denoising , causal language modeling , contrastive learning , and text-code matching to learn rich representations from both unimodal code data and bimodal code-text data. Additionally, it employs a simple yet effective compute-efficient pretraining method to initialize the model components with frozen off-the-shelf LLMs such as CodeGen to efficiently scale up the model (i.e. 2B , 6B , 16B ), and adopts a "shallow encoder and deep decoder" architecture. Furthermore, it is instruction-tuned to align with natural language instructions (see our InstructCodeT5+ 16B) following Code Alpaca .

How to use

This checkpoint consists of an encoder of CodeT5+ 220M model (pretrained from 2 stages on both unimodal and bimodal) and a projection layer, which can be used to extract code embeddings of 256 dimension. It can be easily loaded using the AutoModel functionality and employs the same CodeT5 tokenizer.

from transformers import AutoModel, AutoTokenizer

checkpoint = "Salesforce/codet5p-110m-embedding"
device = "cuda"  # for GPU usage or "cpu" for CPU usage

tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)
model = AutoModel.from_pretrained(checkpoint, trust_remote_code=True).to(device)

inputs = tokenizer.encode("def print_hello_world():\tprint('Hello World!')", return_tensors="pt").to(device)
embedding = model(inputs)[0]
print(f'Dimension of the embedding: {embedding.size()[0]}, with norm={embedding.norm().item()}')
# Dimension of the embedding: 256, with norm=1.0
print(embedding)
# tensor([ 0.0185,  0.0229, -0.0315, -0.0307, -0.1421, -0.0575, -0.0275,  0.0501,
#          0.0203,  0.0337, -0.0067, -0.0075, -0.0222, -0.0107, -0.0250, -0.0657,
#          0.1571, -0.0994, -0.0370,  0.0164, -0.0948,  0.0490, -0.0352,  0.0907,
#         -0.0198,  0.0130, -0.0921,  0.0209,  0.0651,  0.0319,  0.0299, -0.0173,
#         -0.0693, -0.0798, -0.0066, -0.0417,  0.1076,  0.0597, -0.0316,  0.0940,
#         -0.0313,  0.0993,  0.0931, -0.0427,  0.0256,  0.0297, -0.0561, -0.0155,
#         -0.0496, -0.0697, -0.1011,  0.1178,  0.0283, -0.0571, -0.0635, -0.0222,
#          0.0710, -0.0617,  0.0423, -0.0057,  0.0620, -0.0262,  0.0441,  0.0425,
#         -0.0413, -0.0245,  0.0043,  0.0185,  0.0060, -0.1727, -0.1152,  0.0655,
#         -0.0235, -0.1465, -0.1359,  0.0022,  0.0177, -0.0176, -0.0361, -0.0750,
#         -0.0464, -0.0846, -0.0088,  0.0136, -0.0221,  0.0591,  0.0876, -0.0903,
#          0.0271, -0.1165, -0.0169, -0.0566,  0.1173, -0.0801,  0.0430,  0.0236,
#          0.0060, -0.0778, -0.0570,  0.0102, -0.0172, -0.0051, -0.0891, -0.0620,
#         -0.0536,  0.0190, -0.0039, -0.0189, -0.0267, -0.0389, -0.0208,  0.0076,
#         -0.0676,  0.0630, -0.0962,  0.0418, -0.0172, -0.0229, -0.0452,  0.0401,
#          0.0270,  0.0677, -0.0111, -0.0089,  0.0175,  0.0703,  0.0714, -0.0068,
#          0.1214, -0.0004,  0.0020,  0.0255,  0.0424, -0.0030,  0.0318,  0.1227,
#          0.0676, -0.0723,  0.0970,  0.0637, -0.0140, -0.0283, -0.0120,  0.0343,
#         -0.0890,  0.0680,  0.0514,  0.0513,  0.0627, -0.0284, -0.0479,  0.0068,
#         -0.0794,  0.0202,  0.0208, -0.0113, -0.0747,  0.0045, -0.0854, -0.0609,
#         -0.0078,  0.1168,  0.0618, -0.0223, -0.0755,  0.0182, -0.0128,  0.1116,
#          0.0240,  0.0342,  0.0119, -0.0235, -0.0150, -0.0228, -0.0568, -0.1528,
#          0.0164, -0.0268,  0.0727, -0.0569,  0.1306,  0.0643, -0.0158, -0.1070,
#         -0.0107, -0.0139, -0.0363,  0.0366, -0.0986, -0.0628, -0.0277,  0.0316,
#          0.0363,  0.0038, -0.1092, -0.0679, -0.1398, -0.0648,  0.1711, -0.0666,
#          0.0563,  0.0581,  0.0226,  0.0347, -0.0672, -0.0229, -0.0565,  0.0623,
#          0.1089, -0.0687, -0.0901, -0.0073,  0.0426,  0.0870, -0.0390, -0.0144,
#         -0.0166,  0.0262, -0.0310,  0.0467, -0.0164, -0.0700, -0.0602, -0.0720,
#         -0.0386,  0.0067, -0.0337, -0.0053,  0.0829,  0.1004,  0.0427,  0.0026,
#         -0.0537,  0.0951,  0.0584, -0.0583, -0.0208,  0.0124,  0.0067,  0.0403,
#          0.0091, -0.0044, -0.0036,  0.0524,  0.1103, -0.1511, -0.0479,  0.1709,
#          0.0772,  0.0721, -0.0332,  0.0866,  0.0799, -0.0581,  0.0713,  0.0218],
#        device='cuda:0', grad_fn=<SelectBackward0>)

Pretraining data

This checkpoint is trained on the stricter permissive subset of the deduplicated version of the github-code dataset . The data is preprocessed by reserving only permissively licensed code ("mit" “apache-2”, “bsd-3-clause”, “bsd-2-clause”, “cc0-1.0”, “unlicense”, “isc”). Supported languages (9 in total) are as follows: c , c++ , c-sharp , go , java , javascript , php , python , ruby.

Training procedure

This checkpoint is first trained on the unimodal code data at the first-stage pretraining and then on bimodal text-code pair data using the proposed mixture of pretraining tasks. Please refer to the paper for more details.

Evaluation results

We show the zero-shot results of this checkpoint on 6 downstream code retrieval tasks from CodeXGLUE in the following table.

BibTeX entry and citation info

@article{wang2023codet5plus,
  title={CodeT5+: Open Code Large Language Models for Code Understanding and Generation},
  author={Wang, Yue and Le, Hung and Gotmare, Akhilesh Deepak and Bui, Nghi D.Q. and Li, Junnan and Hoi, Steven C. H.},
  journal={arXiv preprint},
  year={2023}
}