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--- |
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license: bsd-3-clause |
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--- |
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# CodeGen (CodeGen-NL 16B) |
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## Sharded version of codegen |
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This model was sharded using torch.float16. Use the code below to load this model, configure the device_map for your GPU/CPU split. |
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First pull the model. |
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```bash |
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git clone https://huggingface.co/abacaj/codegen-16B-nl-sharded |
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cd codegen-16B-nl-sharded |
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git-lfs install |
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git pull |
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``` |
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```python |
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def load_model_sharded(): |
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config = AutoConfig.from_pretrained("abacaj/codegen-16B-nl-sharded") |
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tokenizer = AutoTokenizer.from_pretrained("abacaj/codegen-16B-nl-sharded") |
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with init_empty_weights(): |
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model = AutoModelForCausalLM.from_config(config) |
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device_map = infer_auto_device_map( |
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model, |
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max_memory={ |
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0: "20GiB", |
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"cpu": "110GiB", |
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}, |
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dtype=torch.float16, |
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no_split_module_classes=["CodeGenBlock"]) |
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model = load_checkpoint_and_dispatch( |
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model, |
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dtype=torch.float16, |
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checkpoint="codegen-16B-nl-sharded", |
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device_map=device_map, |
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).eval() |
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return model, tokenizer |
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``` |
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## Model description |
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CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are originally released in [this repository](https://github.com/salesforce/CodeGen), under 3 pre-training data variants (`NL`, `Multi`, `Mono`) and 4 model size variants (`350M`, `2B`, `6B`, `16B`). |
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The checkpoint included in this repository is denoted as **CodeGen-NL 16B** in the paper, where "NL" means it is pre-trained on the Pile and "16B" refers to the number of trainable parameters. |
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## Training data |
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This checkpoint (CodeGen-NL 16B) was pre-trained on [the Pile](https://github.com/EleutherAI/the-pile), a large-scale curated dataset created by [EleutherAI](https://www.eleuther.ai/). Parts of the dataset include code data. |
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## Training procedure |
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CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs. |
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The family of models are trained using multiple TPU-v4-512 by Google, leveraging data and model parallelism. |
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See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details. |
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## Evaluation results |
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We evaluate our models on two code generation benchmark: HumanEval and MTPB. Please refer to the [paper](https://arxiv.org/abs/2203.13474) for more details. |
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## Intended Use and Limitations |
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As an autoregressive language model, CodeGen is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them. |
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However, the model is intended for and best at **program synthesis**, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well. |
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## How to use |
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This model can be easily loaded using the `AutoModelForCausalLM` functionality: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-16B-nl") |
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model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-16B-nl") |
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text = "def hello_world():" |
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input_ids = tokenizer(text, return_tensors="pt").input_ids |
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generated_ids = model.generate(input_ids, max_length=128) |
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print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) |
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``` |
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## BibTeX entry and citation info |
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```bibtex |
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@article{Nijkamp2022ACP, |
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title={A Conversational Paradigm for Program Synthesis}, |
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author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming}, |
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journal={arXiv preprint}, |
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year={2022} |
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} |
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``` |
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