├── .gitignore ├── DATA_LICENSE ├── LICENSE ├── README.md ├── convert_to_hf.py ├── data ├── code_alpaca_20k.json ├── code_alpaca_2k.json ├── new_codealpaca.json └── rosetta_alpaca.json ├── ds_config.json ├── generate_instruction.py ├── nolora.py ├── prompt.txt ├── requirements.txt ├── seed_tasks.jsonl ├── train.py └── utils.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | 131 | .DS_Store 132 | .idea 133 | -------------------------------------------------------------------------------- /DATA_LICENSE: -------------------------------------------------------------------------------- 1 | Attribution-NonCommercial 4.0 International 2 | 3 | ======================================================================= 4 | 5 | Creative Commons Corporation ("Creative Commons") is not a law firm and 6 | does not provide legal services or legal advice. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Code Alpaca: An Instruction-following LLaMA Model trained on code generation instructions 2 | [![License](https://img.shields.io/badge/License-Apache_2.0-green.svg)](https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE) 3 | [![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/release/python-390/) 4 | [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) 5 | 6 | This is the repo for the Code Alpaca project, which aims to build and share an instruction-following LLaMA model for code generation. This repo is fully based on [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) ,and only changes the data used for training. Training approach is the same. 7 | 8 | The repo contains: 9 | - The [20K data](#data-release) used for fine-tuning the model 10 | - The code for [generating the data](#data-generation-process) 11 | - The code for [fine-tuning the model](#fine-tuning) 12 | 13 | Demo for the model can be found [https://code-alpaca-demo.vercel.app/](https://code-alpaca-demo.vercel.app/) 14 | 15 | ## Overview 16 | 17 | The Code Alpaca models are fine-tuned from a 7B and 13B LLaMA model on 20K instruction-following data generated by the techniques in the Self-Instruct [1] paper, with some modifications that we discuss in the next section. 18 | Evals are still a todo. 19 | 20 | The model is not finetuned to be safe and harmless, so be cautious. 21 | 22 | Current release contains the data generation procedure, dataset, and training code. Model weights aren't part of the release for now, to respect OpenAI TOS and LLaMA license. 23 | 24 | [1]: Self-Instruct: Aligning Language Model with Self Generated Instructions. Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, Hannaneh Hajishirzi. https://arxiv.org/abs/2212.10560 25 | 26 | 27 | ## Data Release 28 | [`data/code_alpaca_20k.json`](./data/code_alpaca_20k.json) contains 20K instruction-following data used for fine-tuning the Code Alpaca model. 29 | This JSON file is a list of dictionaries, each dictionary contains the following fields: 30 | - `instruction`: `str`, describes the task the model should perform. Each of the 20K instructions is unique. 31 | - `input`: `str`, optional context or input for the task. For example, when the instruction is "Amend the following SQL query to select distinct elements", the input is the SQL query. Around 40% of the examples have an input. 32 | - `output`: `str`, the answer to the instruction as generated by `text-davinci-003`. 33 | 34 | We used the following prompts for fine-tuning the model: 35 | - for examples with a non-empty input field: 36 | ``` 37 | Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. 38 | 39 | ### Instruction: 40 | {instruction} 41 | 42 | ### Input: 43 | {input} 44 | 45 | ### Response: 46 | ``` 47 | - for examples with an empty input field: 48 | ``` 49 | Below is an instruction that describes a task. Write a response that appropriately completes the request. 50 | 51 | ### Instruction: 52 | {instruction} 53 | 54 | ### Response: 55 | ``` 56 | 57 | During inference (eg for the web demo), we use the user instruction with an empty input field (second option). 58 | 59 | ## Data Generation Process 60 | 61 |
62 | Running the code 63 | 64 | 1. Set environment variables `OPENAI_API_KEY` to your OpenAI API key. 65 | 2. Install the dependencies with `pip install -r requirements.txt`. 66 | 3. Run `python -m generate_instruction generate_instruction_following_data` to generate the data. 67 | 68 |
69 | Data generation pipeline had minor changes from [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) 70 | - Modified prompt to focus on code generation/editing/optimization tasks instead of general tasks. 71 | - Modified seed tasks to only be related to code generation. 72 | 73 | This produced an instruction-following dataset with 20K examples obtained at a much lower cost (less than $200). Also including a smaller 2k samples dataset which was used to derisk the approach and quality of the model. 74 | 75 | ## Fine-tuning 76 | Finetuned the models using standard Hugging Face training code and deepspeed with the following hyperparameters: 77 | 78 | | Hyperparameter | Value | 79 | |----------------|-------| 80 | | Learning rate | 2e-5 | 81 | | Epochs | 3 | 82 | | Max length | 512 | 83 | | Weight decay | 0 | 84 | 85 | Given Hugging Face hasn't officially supported the LLaMA models, we fine-tuned LLaMA with Hugging Face's transformers library by installing it from a particular fork (i.e. this [PR](https://github.com/huggingface/transformers/pull/21955) to be merged). 86 | The hash of the specific commit we installed was `68d640f7c368bcaaaecfc678f11908ebbd3d6176`. 87 | 88 | The code runs on a 8xA100 80GB, but can also run on 8xA10040GB or 4xA100 with lower batch size and gradient accumulation steps. To get the GPUs, I suggest using [Lambda Labs](https://cloud.lambdalabs.com/login?redirect_to=/instances?), best pricing for the best hardware. 89 | 90 | To reproduce the fine-tuning runs for LLaMA, first install the requirements 91 | ```bash 92 | pip install -r requirements.txt 93 | ``` 94 | Then, install the particular fork of Hugging Face's transformers library. 95 | 96 | Below is a command that fine-tunes LLaMA-7B with our dataset on a machine with 4 A100 80G GPUs using deepspeed. 97 | 98 | Replace `` with a port of your own, `` with the 99 | path to your converted checkpoint and tokenizer (following instructions in the PR), and `` with where you want to store your outputs. 100 | 101 | ```bash 102 | torchrun --nproc_per_node=8 --master_port= train.py \ 103 | --model_name_or_path 104 | --data_path ./data/code_alpaca_20k.json \ 105 | --fp16 True \ 106 | --output_dir \ 107 | --num_train_epochs 3 \ 108 | --per_device_train_batch_size 8 \ 109 | --per_device_eval_batch_size 8 \ 110 | --gradient_accumulation_steps 4 \ 111 | --evaluation_strategy "no" \ 112 | --save_strategy "steps" \ 113 | --save_steps 500 \ 114 | --save_total_limit 1 \ 115 | --learning_rate 2e-5 \ 116 | --weight_decay 0. \ 117 | --warmup_ratio 0.03 \ 118 | --lr_scheduler_type "cosine" \ 119 | --logging_steps 1 \ 120 | --deepspeed ds_config.json 121 | --tf32 False 122 | ``` 123 | 124 | Note the given training script is meant to be simple and easy to use, and is not particularly optimized. 125 | 126 | For convenience I have included the [`convert_to_hf.py`](./convert_to_hf.py) to covnert llama checkpoints to huggingface compatible checkpoints. (This file is taken from the hugginface transformers repo) 127 | 128 | ### Citation 129 | 130 | Cite this repo if you want to, or don't, both are fine. 131 | ``` 132 | @misc{codealpaca, 133 | author = {Sahil Chaudhary}, 134 | title = {Code Alpaca: An Instruction-following LLaMA model for code generation}, 135 | year = {2023}, 136 | publisher = {GitHub}, 137 | journal = {GitHub repository}, 138 | howpublished = {\url{https://github.com/sahil280114/codealpaca}}, 139 | } 140 | ``` 141 | 142 | Naturally, you should also cite the original LLaMA paper [1] and the Self-Instruct paper [2] and the [Stanford Alpaca repo](https://github.com/tatsu-lab/stanford_alpaca). 143 | -------------------------------------------------------------------------------- /convert_to_hf.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import json 3 | import math 4 | import os 5 | import shutil 6 | 7 | import torch 8 | 9 | 10 | """ 11 | Sample usage: 12 | ``` 13 | python co.py --input_dir . --model_size 13B --output_dir output/ d 14 | ``` 15 | Thereafter, models can be loaded via: 16 | ``` 17 | tokenizer = transformers.LLaMATokenizer.from_pretrained("/output/path/tokenizer/") 18 | model = transformers.LLaMAForCausalLM.from_pretrained("/output/path/llama-7b/") 19 | ``` 20 | """ 21 | 22 | INTERMEDIATE_SIZE_MAP = { 23 | "7B": 11008, 24 | "13B": 13824, 25 | "30B": 17920, 26 | "65B": 22016, 27 | } 28 | NUM_SHARDS = { 29 | "7B": 1, 30 | "13B": 2, 31 | "30B": 4, 32 | "65B": 8, 33 | } 34 | 35 | 36 | def compute_intermediate_size(n): 37 | return int(math.ceil(n * 8 / 3) + 255) // 256 * 256 38 | 39 | 40 | def read_json(path): 41 | with open(path, "r") as f: 42 | return json.load(f) 43 | 44 | 45 | def write_json(text, path): 46 | with open(path, "w") as f: 47 | json.dump(text, f) 48 | 49 | 50 | def write_model(model_path, input_base_path, model_size): 51 | assert model_size in NUM_SHARDS 52 | os.makedirs(model_path, exist_ok=True) 53 | 54 | params = read_json(os.path.join(input_base_path, "params.json")) 55 | num_shards = NUM_SHARDS[model_size] 56 | n_layers = params["n_layers"] 57 | n_heads = params["n_heads"] 58 | n_heads_per_shard = n_heads // num_shards 59 | dim = params["dim"] 60 | dims_per_head = dim // n_heads 61 | base = 10000.0 62 | inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head)) 63 | 64 | # permute for sliced rotary 65 | def permute(w): 66 | return w.view(n_heads, dim // n_heads // 2, 2, dim).transpose(1, 2).reshape(dim, dim) 67 | 68 | # Load weights 69 | if model_size == "7B": 70 | # Not shared 71 | # (The sharded implementation would also work, but this is simpler.) 72 | loaded = torch.load(os.path.join(input_base_path, "consolidated.00.pth"), map_location="cpu") 73 | else: 74 | # Sharded 75 | loaded = [ 76 | torch.load(os.path.join(input_base_path, f"consolidated.{i:02d}.pth"), map_location="cpu") 77 | for i in range(num_shards) 78 | ] 79 | param_count = 0 80 | index_dict = {"weight_map": {}} 81 | for layer_i in range(n_layers): 82 | filename = "pytorch_model-{:05d}-of-{:05d}.bin".format( 83 | layer_i + 1, 84 | n_layers + 1, 85 | ) 86 | if model_size == "7B": 87 | # Unsharded 88 | state_dict = { 89 | f"model.layers.{layer_i}.self_attn.q_proj.weight": permute( 90 | loaded[f"layers.{layer_i}.attention.wq.weight"] 91 | ), 92 | f"model.layers.{layer_i}.self_attn.k_proj.weight": permute( 93 | loaded[f"layers.{layer_i}.attention.wk.weight"] 94 | ), 95 | f"model.layers.{layer_i}.self_attn.v_proj.weight": loaded[f"layers.{layer_i}.attention.wv.weight"], 96 | f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded[f"layers.{layer_i}.attention.wo.weight"], 97 | f"model.layers.{layer_i}.mlp.gate_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w1.weight"], 98 | f"model.layers.{layer_i}.mlp.down_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w2.weight"], 99 | f"model.layers.{layer_i}.mlp.up_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w3.weight"], 100 | f"model.layers.{layer_i}.input_layernorm.weight": loaded[f"layers.{layer_i}.attention_norm.weight"], 101 | f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[f"layers.{layer_i}.ffn_norm.weight"], 102 | } 103 | else: 104 | # Sharded 105 | state_dict = { 106 | f"model.layers.{layer_i}.input_layernorm.weight": loaded[0][f"layers.{layer_i}.attention_norm.weight"], 107 | f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[0][ 108 | f"layers.{layer_i}.ffn_norm.weight" 109 | ], 110 | } 111 | state_dict[f"model.layers.{layer_i}.self_attn.q_proj.weight"] = permute( 112 | torch.cat( 113 | [ 114 | loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(n_heads_per_shard, dims_per_head, dim) 115 | for i in range(num_shards) 116 | ], 117 | dim=0, 118 | ).reshape(dim, dim) 119 | ) 120 | state_dict[f"model.layers.{layer_i}.self_attn.k_proj.weight"] = permute( 121 | torch.cat( 122 | [ 123 | loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(n_heads_per_shard, dims_per_head, dim) 124 | for i in range(num_shards) 125 | ], 126 | dim=0, 127 | ).reshape(dim, dim) 128 | ) 129 | state_dict[f"model.layers.{layer_i}.self_attn.v_proj.weight"] = torch.cat( 130 | [ 131 | loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(n_heads_per_shard, dims_per_head, dim) 132 | for i in range(num_shards) 133 | ], 134 | dim=0, 135 | ).reshape(dim, dim) 136 | 137 | state_dict[f"model.layers.{layer_i}.self_attn.o_proj.weight"] = torch.cat( 138 | [loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(num_shards)], dim=1 139 | ) 140 | state_dict[f"model.layers.{layer_i}.mlp.gate_proj.weight"] = torch.cat( 141 | [loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(num_shards)], dim=0 142 | ) 143 | state_dict[f"model.layers.{layer_i}.mlp.down_proj.weight"] = torch.cat( 144 | [loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(num_shards)], dim=1 145 | ) 146 | state_dict[f"model.layers.{layer_i}.mlp.up_proj.weight"] = torch.cat( 147 | [loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(num_shards)], dim=0 148 | ) 149 | 150 | state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq 151 | for k, v in state_dict.items(): 152 | index_dict["weight_map"][k] = filename 153 | param_count += v.numel() 154 | torch.save(state_dict, os.path.join(model_path, filename)) 155 | 156 | filename = "pytorch_model-{:05d}-of-{:05d}.bin".format( 157 | n_layers + 1, 158 | n_layers + 1, 159 | ) 160 | if model_size == "7B": 161 | # Unsharded 162 | state_dict = { 163 | "model.embed_tokens.weight": loaded["tok_embeddings.weight"], 164 | "model.norm.weight": loaded["norm.weight"], 165 | "lm_head.weight": loaded["output.weight"], 166 | } 167 | else: 168 | state_dict = { 169 | "model.norm.weight": loaded[0]["norm.weight"], 170 | "model.embed_tokens.weight": torch.cat( 171 | [loaded[i]["tok_embeddings.weight"] for i in range(num_shards)], dim=1 172 | ), 173 | "lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(num_shards)], dim=0), 174 | } 175 | 176 | for k, v in state_dict.items(): 177 | index_dict["weight_map"][k] = filename 178 | param_count += v.numel() 179 | torch.save(state_dict, os.path.join(model_path, filename)) 180 | 181 | # Write configs 182 | index_dict["metadata"] = {"total_size": param_count * 2} 183 | write_json(index_dict, os.path.join(model_path, "pytorch_model.bin.index.json")) 184 | config_out = { 185 | "architectures": ["LlamaForCausalLM"], 186 | "bos_token_id": 1, 187 | "eos_token_id": 2, 188 | "hidden_act": "silu", 189 | "hidden_size": dim, 190 | "intermediate_size": compute_intermediate_size(dim), 191 | "initializer_range": 0.02, 192 | "max_sequence_length": 2048, 193 | "model_type": "llama", 194 | "num_attention_heads": params["n_heads"], 195 | "num_hidden_layers": params["n_layers"], 196 | "pad_token_id": 0, 197 | "rms_norm_eps": params["norm_eps"], 198 | "torch_dtype": "float16", 199 | "transformers_version": "4.27.0.dev0", 200 | "use_cache": True, 201 | "vocab_size": 32000, 202 | } 203 | write_json( 204 | config_out, 205 | os.path.join(model_path, "config.json"), 206 | ) 207 | generation_config = { 208 | "_from_model_config": True, 209 | "bos_token_id": 1, 210 | "eos_token_id": 2, 211 | "pad_token_id": 0, 212 | "transformers_version": "4.27.0.dev0", 213 | } 214 | write_json( 215 | generation_config, 216 | os.path.join(model_path, "generation_config.json"), 217 | ) 218 | 219 | 220 | def write_tokenizer(tokenizer_path, input_tokenizer_path): 221 | os.makedirs(tokenizer_path, exist_ok=True) 222 | write_json({}, os.path.join(tokenizer_path, "special_tokens_map.json")) 223 | write_json( 224 | { 225 | "bos_token": "", 226 | "eos_token": "", 227 | "model_max_length": int(1e30), 228 | "tokenizer_class": "LlamaTokenizer", 229 | "unk_token": "", 230 | }, 231 | os.path.join(tokenizer_path, "tokenizer_config.json"), 232 | ) 233 | shutil.copyfile(input_tokenizer_path, os.path.join(tokenizer_path, "tokenizer.model")) 234 | 235 | 236 | def main(): 237 | parser = argparse.ArgumentParser() 238 | parser.add_argument( 239 | "--input_dir", 240 | help="Location of LLaMA weights, which contains tokenizer.model and model folders", 241 | ) 242 | parser.add_argument( 243 | "--model_size", 244 | choices=["7B", "13B", "30B", "65B", "tokenizer_only"], 245 | ) 246 | parser.add_argument( 247 | "--output_dir", 248 | help="Location to write HF model and tokenizer", 249 | ) 250 | args = parser.parse_args() 251 | if args.model_size != "tokenizer_only": 252 | write_model( 253 | model_path=os.path.join(args.output_dir, "llama-{}".format(args.model_size).lower()), 254 | input_base_path=os.path.join(args.input_dir, args.model_size), 255 | model_size=args.model_size, 256 | ) 257 | write_tokenizer( 258 | tokenizer_path=os.path.join(args.output_dir, "tokenizer"), 259 | input_tokenizer_path=os.path.join(args.input_dir, "tokenizer.model"), 260 | ) 261 | 262 | 263 | if __name__ == "__main__": 264 | main() -------------------------------------------------------------------------------- /ds_config.json: -------------------------------------------------------------------------------- 1 | { 2 | "zero_optimization": { 3 | "stage": 3, 4 | "contiguous_gradients": true, 5 | "stage3_max_live_parameters": 0, 6 | "stage3_max_reuse_distance": 0, 7 | "stage3_prefetch_bucket_size": 0, 8 | "stage3_param_persistence_threshold": 100, 9 | "reduce_bucket_size": 100, 10 | "sub_group_size": 100000000, 11 | "offload_optimizer": { 12 | "device": "cpu", 13 | "pin_memory": true 14 | }, 15 | "offload_param": { 16 | "device": "cpu", 17 | "pin_memory": true 18 | }, 19 | "stage3_gather_16bit_weights_on_model_save": true 20 | }, 21 | "optimizer": { 22 | "type": "Adam", 23 | "params": { 24 | "lr": 0.00002, 25 | "betas": [ 26 | 0.9, 27 | 0.999 28 | ], 29 | "eps": 1e-8, 30 | "weight_decay": 0 31 | } 32 | }, 33 | "fp16": { 34 | "enabled": "auto", 35 | "auto_cast": "auto", 36 | "loss_scale": 0, 37 | "initial_scale_power": 32, 38 | "loss_scale_window": 1000, 39 | "hysteresis": 2, 40 | "min_loss_scale": 1 41 | }, 42 | "train_batch_size": "auto", 43 | "train_micro_batch_size_per_gpu": "auto", 44 | "wall_clock_breakdown": false 45 | } 46 | -------------------------------------------------------------------------------- /generate_instruction.py: -------------------------------------------------------------------------------- 1 | """ 2 | batch_selfinstruct_generate.py 3 | 4 | run: 5 | python -m generate_instruction generate_instruction_following_data \ 6 | --output_dir ./ \ 7 | --num_instructions_to_generate 10 \ 8 | --model_name="text-davinci-003" \ 9 | """ 10 | import time 11 | import json 12 | import os 13 | import random 14 | import re 15 | import string 16 | from functools import partial 17 | from multiprocessing import Pool 18 | 19 | import numpy as np 20 | import tqdm 21 | from rouge_score import rouge_scorer 22 | import utils 23 | 24 | import fire 25 | 26 | 27 | def encode_prompt(prompt_instructions): 28 | """Encode multiple prompt instructions into a single string.""" 29 | prompt = open("./prompt.txt").read() + "\n" 30 | 31 | for idx, task_dict in enumerate(prompt_instructions): 32 | (instruction, input, output) = task_dict["instruction"], task_dict["input"], task_dict["output"] 33 | instruction = re.sub(r"\s+", " ", instruction).strip().rstrip(":") 34 | input = "" if input.lower() == "" else input 35 | prompt += f"###\n" 36 | prompt += f"{idx + 1}. Instruction: {instruction}\n" 37 | prompt += f"{idx + 1}. Input:\n{input}\n" 38 | prompt += f"{idx + 1}. Output:\n{output}\n" 39 | prompt += f"###\n" 40 | prompt += f"{idx + 2}. Instruction:" 41 | return prompt 42 | 43 | 44 | def post_process_gpt3_response(num_prompt_instructions, response): 45 | if response is None: 46 | return [] 47 | raw_instructions = f"{num_prompt_instructions+1}. Instruction:" + response["text"] 48 | raw_instructions = re.split("###", raw_instructions) 49 | instructions = [] 50 | for idx, inst in enumerate(raw_instructions): 51 | # if the decoding stops due to length, the last example is likely truncated so we discard it 52 | if idx == len(raw_instructions) - 1 and response["finish_reason"] == "length": 53 | continue 54 | idx += num_prompt_instructions + 1 55 | splitted_data = re.split(f"{idx}\.\s+(Instruction|Input|Output):", inst) 56 | if len(splitted_data) != 7: 57 | continue 58 | else: 59 | inst = splitted_data[2].strip() 60 | input = splitted_data[4].strip() 61 | input = "" if input.lower() == "" else input 62 | output = splitted_data[6].strip() 63 | # filter out too short or too long instructions 64 | if len(inst.split()) <= 3 or len(inst.split()) > 150: 65 | continue 66 | # filter based on keywords that are not suitable for language models. 67 | blacklist = [ 68 | "image", 69 | "images", 70 | "graph", 71 | "graphs", 72 | "picture", 73 | "pictures", 74 | "file", 75 | "files", 76 | "map", 77 | "maps", 78 | "draw", 79 | "plot", 80 | "go to", 81 | "video", 82 | "audio", 83 | "music", 84 | "flowchart", 85 | "diagram", 86 | ] 87 | blacklist += [] 88 | if any(find_word_in_string(word, inst) for word in blacklist): 89 | continue 90 | # We found that the model tends to add "write a program" to some existing instructions, which lead to a lot of such instructions. 91 | # And it's a bit comfusing whether the model need to write a program or directly output the result. 92 | # Here we filter them out. 93 | # Note this is not a comprehensive filtering for all programming instructions. 94 | if inst.startswith("Write a program"): 95 | continue 96 | # filter those starting with punctuation 97 | if inst[0] in string.punctuation: 98 | continue 99 | # filter those starting with non-english character 100 | if not inst[0].isascii(): 101 | continue 102 | instructions.append({"instruction": inst, "input": input, "output": output}) 103 | return instructions 104 | 105 | 106 | def find_word_in_string(w, s): 107 | return re.compile(r"\b({0})\b".format(w), flags=re.IGNORECASE).search(s) 108 | 109 | 110 | def generate_instruction_following_data( 111 | output_dir="./", 112 | seed_tasks_path="./seed_tasks.jsonl", 113 | num_instructions_to_generate=20000, 114 | model_name="text-davinci-003", 115 | num_prompt_instructions=3, 116 | request_batch_size=5, 117 | temperature=1.0, 118 | top_p=1.0, 119 | num_cpus=16, 120 | ): 121 | seed_tasks = [json.loads(l) for l in open(seed_tasks_path, "r")] 122 | seed_instruction_data = [ 123 | {"instruction": t["instruction"], "input": t["instances"][0]["input"], "output": t["instances"][0]["output"]} 124 | for t in seed_tasks 125 | ] 126 | print(f"Loaded {len(seed_instruction_data)} human-written seed instructions") 127 | 128 | os.makedirs(output_dir, exist_ok=True) 129 | request_idx = 0 130 | # load the LM-generated instructions 131 | machine_instruction_data = [] 132 | if os.path.exists(os.path.join(output_dir, "regen.json")): 133 | machine_instruction_data = utils.jload(os.path.join(output_dir, "regen.json")) 134 | print(f"Loaded {len(machine_instruction_data)} machine-generated instructions") 135 | 136 | # similarities = {} 137 | scorer = rouge_scorer.RougeScorer(["rougeL"], use_stemmer=False) 138 | 139 | # now let's generate new instructions! 140 | progress_bar = tqdm.tqdm(total=num_instructions_to_generate) 141 | if machine_instruction_data: 142 | progress_bar.update(len(machine_instruction_data)) 143 | 144 | # first we tokenize all the seed instructions and generated machine instructions 145 | all_instructions = [d["instruction"] for d in seed_instruction_data] + [ 146 | d["instruction"] for d in machine_instruction_data 147 | ] 148 | all_instruction_tokens = [scorer._tokenizer.tokenize(inst) for inst in all_instructions] 149 | 150 | while len(machine_instruction_data) < num_instructions_to_generate: 151 | request_idx += 1 152 | 153 | batch_inputs = [] 154 | for _ in range(request_batch_size): 155 | # only sampling from the seed tasks 156 | prompt_instructions = random.sample(seed_instruction_data, num_prompt_instructions) 157 | prompt = encode_prompt(prompt_instructions) 158 | batch_inputs.append(prompt) 159 | decoding_args = utils.OpenAIDecodingArguments( 160 | temperature=temperature, 161 | n=1, 162 | max_tokens=3072, # hard-code to maximize the length. the requests will be automatically adjusted 163 | top_p=top_p, 164 | stop=["\n20", "20.", "20."], 165 | ) 166 | request_start = time.time() 167 | print("Calling openai...") 168 | results = utils.openai_completion( 169 | prompts=batch_inputs, 170 | model_name=model_name, 171 | batch_size=request_batch_size, 172 | decoding_args=decoding_args, 173 | logit_bias={"50256": -100}, # prevent the <|endoftext|> token from being generated 174 | ) 175 | request_duration = time.time() - request_start 176 | print(f'request took - {request_duration}') 177 | process_start = time.time() 178 | instruction_data = [] 179 | for result in results: 180 | new_instructions = post_process_gpt3_response(num_prompt_instructions, result) 181 | instruction_data += new_instructions 182 | 183 | total = len(instruction_data) 184 | keep = 0 185 | for instruction_data_entry in instruction_data: 186 | # computing similarity with the pre-tokenzied instructions 187 | new_instruction_tokens = scorer._tokenizer.tokenize(instruction_data_entry["instruction"]) 188 | with Pool(num_cpus) as p: 189 | rouge_scores = p.map( 190 | partial(rouge_scorer._score_lcs, new_instruction_tokens), 191 | all_instruction_tokens, 192 | ) 193 | rouge_scores = [score.fmeasure for score in rouge_scores] 194 | most_similar_instructions = { 195 | all_instructions[i]: rouge_scores[i] for i in np.argsort(rouge_scores)[-10:][::-1] 196 | } 197 | if max(rouge_scores) > 0.7: 198 | continue 199 | else: 200 | keep += 1 201 | instruction_data_entry["most_similar_instructions"] = most_similar_instructions 202 | instruction_data_entry["avg_similarity_score"] = float(np.mean(rouge_scores)) 203 | machine_instruction_data.append(instruction_data_entry) 204 | all_instructions.append(instruction_data_entry["instruction"]) 205 | all_instruction_tokens.append(new_instruction_tokens) 206 | progress_bar.update(1) 207 | process_duration = time.time() - process_start 208 | print(f"Request {request_idx} took {request_duration:.2f}s, processing took {process_duration:.2f}s") 209 | print(f"Generated {total} instructions, kept {keep} instructions") 210 | utils.jdump(machine_instruction_data, os.path.join(output_dir, "regen.json")) 211 | 212 | 213 | def main(task, **kwargs): 214 | globals()[task](**kwargs) 215 | 216 | 217 | if __name__ == "__main__": 218 | fire.Fire(main) 219 | -------------------------------------------------------------------------------- /nolora.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | from typing import List 4 | 5 | import fire 6 | import torch 7 | import transformers 8 | from datasets import load_dataset 9 | 10 | """ 11 | Unused imports: 12 | import torch.nn as nn 13 | import bitsandbytes as bnb 14 | """ 15 | 16 | # from peft import ( 17 | # LoraConfig, 18 | # get_peft_model, 19 | # get_peft_model_state_dict, 20 | # prepare_model_for_int8_training, 21 | # set_peft_model_state_dict, 22 | # ) 23 | from transformers import AutoModelForCausalLM, AutoTokenizer 24 | 25 | from utils.prompter import Prompter 26 | 27 | 28 | def train( 29 | # model/data params 30 | base_model: str = "", # the only required argument 31 | data_path: str = "yahma/alpaca-cleaned", 32 | output_dir: str = "./lora-alpaca", 33 | # training hyperparams 34 | batch_size: int = 128, 35 | micro_batch_size: int = 8, 36 | num_epochs: int = 1, 37 | learning_rate: float = 3e-4, 38 | cutoff_len: int = 2048, 39 | val_set_size: int = 2000, 40 | # lora hyperparams 41 | lora_r: int = 8, 42 | lora_alpha: int = 16, 43 | lora_dropout: float = 0.05, 44 | lora_target_modules: List[str] = [ 45 | "q_proj", 46 | "v_proj", 47 | ], 48 | # llm hyperparams 49 | train_on_inputs: bool = True, # if False, masks out inputs in loss 50 | group_by_length: bool = False, # faster, but produces an odd training loss curve 51 | # wandb params 52 | wandb_project: str = "", 53 | wandb_run_name: str = "", 54 | wandb_watch: str = "", # options: false | gradients | all 55 | wandb_log_model: str = "", # options: false | true 56 | resume_from_checkpoint: str = None, # either training checkpoint or final adapter 57 | prompt_template_name: str = "alpaca", # The prompt template to use, will default to alpaca. 58 | ): 59 | if int(os.environ.get("LOCAL_RANK", 0)) == 0: 60 | print( 61 | f"Training Alpaca-LoRA model with params:\n" 62 | f"base_model: {base_model}\n" 63 | f"data_path: {data_path}\n" 64 | f"output_dir: {output_dir}\n" 65 | f"batch_size: {batch_size}\n" 66 | f"micro_batch_size: {micro_batch_size}\n" 67 | f"num_epochs: {num_epochs}\n" 68 | f"learning_rate: {learning_rate}\n" 69 | f"cutoff_len: {cutoff_len}\n" 70 | f"val_set_size: {val_set_size}\n" 71 | f"lora_r: {lora_r}\n" 72 | f"lora_alpha: {lora_alpha}\n" 73 | f"lora_dropout: {lora_dropout}\n" 74 | f"lora_target_modules: {lora_target_modules}\n" 75 | f"train_on_inputs: {train_on_inputs}\n" 76 | f"group_by_length: {group_by_length}\n" 77 | f"wandb_project: {wandb_project}\n" 78 | f"wandb_run_name: {wandb_run_name}\n" 79 | f"wandb_watch: {wandb_watch}\n" 80 | f"wandb_log_model: {wandb_log_model}\n" 81 | f"resume_from_checkpoint: {resume_from_checkpoint or False}\n" 82 | f"prompt template: {prompt_template_name}\n" 83 | ) 84 | assert ( 85 | base_model 86 | ), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'" 87 | gradient_accumulation_steps = 16 88 | 89 | prompter = Prompter(prompt_template_name) 90 | 91 | device_map = "auto" 92 | world_size = int(os.environ.get("WORLD_SIZE", 1)) 93 | ddp = world_size != 1 94 | if ddp: 95 | device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)} 96 | gradient_accumulation_steps = gradient_accumulation_steps // world_size 97 | 98 | # Check if parameter passed or if set within environ 99 | use_wandb = len(wandb_project) > 0 or ( 100 | "WANDB_PROJECT" in os.environ and len(os.environ["WANDB_PROJECT"]) > 0 101 | ) 102 | # Only overwrite environ if wandb param passed 103 | if len(wandb_project) > 0: 104 | os.environ["WANDB_PROJECT"] = wandb_project 105 | if len(wandb_watch) > 0: 106 | os.environ["WANDB_WATCH"] = wandb_watch 107 | if len(wandb_log_model) > 0: 108 | os.environ["WANDB_LOG_MODEL"] = wandb_log_model 109 | 110 | model = AutoModelForCausalLM.from_pretrained( 111 | base_model, 112 | load_in_8bit=False, 113 | torch_dtype=torch.float16, 114 | device_map=device_map, 115 | ) 116 | 117 | tokenizer = AutoTokenizer.from_pretrained(base_model) 118 | 119 | tokenizer.pad_token_id = ( 120 | 0 # unk. we want this to be different from the eos token 121 | ) 122 | tokenizer.padding_side = "left" # Allow batched inference 123 | 124 | def tokenize(prompt, add_eos_token=True): 125 | # there's probably a way to do this with the tokenizer settings 126 | # but again, gotta move fast 127 | result = tokenizer( 128 | prompt, 129 | truncation=True, 130 | max_length=cutoff_len, 131 | padding=False, 132 | return_tensors=None, 133 | ) 134 | if ( 135 | result["input_ids"][-1] != tokenizer.eos_token_id 136 | and len(result["input_ids"]) < cutoff_len 137 | and add_eos_token 138 | ): 139 | result["input_ids"].append(tokenizer.eos_token_id) 140 | result["attention_mask"].append(1) 141 | 142 | result["labels"] = result["input_ids"].copy() 143 | 144 | return result 145 | 146 | def generate_and_tokenize_prompt(data_point): 147 | full_prompt = prompter.generate_prompt( 148 | data_point["instruction"], 149 | data_point["input"], 150 | data_point["output"], 151 | ) 152 | tokenized_full_prompt = tokenize(full_prompt) 153 | if not train_on_inputs: 154 | user_prompt = prompter.generate_prompt( 155 | data_point["instruction"], data_point["input"] 156 | ) 157 | tokenized_user_prompt = tokenize(user_prompt, add_eos_token=False) 158 | user_prompt_len = len(tokenized_user_prompt["input_ids"]) 159 | 160 | tokenized_full_prompt["labels"] = [ 161 | -100 162 | ] * user_prompt_len + tokenized_full_prompt["labels"][ 163 | user_prompt_len: 164 | ] # could be sped up, probably 165 | return tokenized_full_prompt 166 | 167 | if data_path.endswith(".json") or data_path.endswith(".jsonl"): 168 | data = load_dataset("json", data_files=data_path) 169 | else: 170 | data = load_dataset(data_path) 171 | 172 | if resume_from_checkpoint: 173 | # Check the available weights and load them 174 | checkpoint_name = os.path.join( 175 | resume_from_checkpoint, "pytorch_model.bin" 176 | ) # Full checkpoint 177 | if not os.path.exists(checkpoint_name): 178 | checkpoint_name = os.path.join( 179 | resume_from_checkpoint, "adapter_model.bin" 180 | ) # only LoRA model - LoRA config above has to fit 181 | resume_from_checkpoint = ( 182 | False # So the trainer won't try loading its state 183 | ) 184 | # The two files above have a different name depending on how they were saved, but are actually the same. 185 | if os.path.exists(checkpoint_name): 186 | print(f"Restarting from {checkpoint_name}") 187 | adapters_weights = torch.load(checkpoint_name) 188 | # model = set_peft_model_state_dict(model, adapters_weights) 189 | else: 190 | print(f"Checkpoint {checkpoint_name} not found") 191 | # Be more transparent about the % of trainable params. 192 | 193 | if val_set_size > 0: 194 | train_val = data["train"].train_test_split( 195 | test_size=val_set_size, shuffle=True, seed=42 196 | ) 197 | train_data = ( 198 | train_val["train"].shuffle().map(generate_and_tokenize_prompt) 199 | ) 200 | val_data = ( 201 | train_val["test"].shuffle().map(generate_and_tokenize_prompt) 202 | ) 203 | else: 204 | train_data = data["train"].shuffle().map(generate_and_tokenize_prompt) 205 | val_data = None 206 | 207 | if not ddp and torch.cuda.device_count() > 1: 208 | # keeps Trainer from trying its own DataParallelism when more than 1 gpu is available 209 | model.is_parallelizable = True 210 | model.model_parallel = True 211 | 212 | trainer = transformers.Trainer( 213 | model=model, 214 | train_dataset=train_data, 215 | eval_dataset=val_data, 216 | args=transformers.TrainingArguments( 217 | per_device_train_batch_size=batch_size, 218 | gradient_accumulation_steps=gradient_accumulation_steps, 219 | warmup_steps=100, 220 | num_train_epochs=num_epochs, 221 | learning_rate=learning_rate, 222 | fp16=True, 223 | logging_steps=10, 224 | optim="adamw_torch", 225 | evaluation_strategy="steps" if val_set_size > 0 else "no", 226 | save_strategy="steps", 227 | eval_steps=200 if val_set_size > 0 else None, 228 | save_steps=200, 229 | output_dir=output_dir, 230 | save_total_limit=3, 231 | load_best_model_at_end=True if val_set_size > 0 else False, 232 | ddp_find_unused_parameters=False if ddp else None, 233 | report_to="wandb" if use_wandb else None, 234 | run_name=wandb_run_name if use_wandb else None, 235 | ), 236 | data_collator=transformers.DataCollatorForSeq2Seq( 237 | tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True 238 | ), 239 | ) 240 | model.config.use_cache = False 241 | 242 | # old_state_dict = model.state_dict 243 | # model.state_dict = ( 244 | # lambda self, *_, **__: get_peft_model_state_dict( 245 | # self, old_state_dict() 246 | # ) 247 | # ).__get__(model, type(model)) 248 | 249 | if torch.__version__ >= "2" and sys.platform != "win32": 250 | model = torch.compile(model) 251 | 252 | trainer.train(resume_from_checkpoint=resume_from_checkpoint) 253 | 254 | model.save_pretrained(output_dir) 255 | 256 | print( 257 | "\n If there's a warning about missing keys above, please disregard :)" 258 | ) 259 | 260 | 261 | if __name__ == "__main__": 262 | fire.Fire(train) 263 | -------------------------------------------------------------------------------- /prompt.txt: -------------------------------------------------------------------------------- 1 | You are asked to come up with a set of 20 diverse code generation task instructions. These task instructions will be given to a GPT model and we will evaluate the GPT model for completing the instructions. 2 | 3 | Here are the requirements: 4 | 1. Try not to repeat the verb for each instruction to maximize diversity. 5 | 2. The language used for the instruction also should be diverse. For example, you should combine questions with imperative instrucitons. 6 | 3. The type of instructions should be diverse. The list should include diverse types of programming tasks like open-ended generation, classification, editing, optimization etc. 7 | 2. A GPT language model should be able to complete the instruction. For example, do not ask the assistant to create any visual or audio output. For another example, do not ask the assistant to wake you up at 5pm or set a reminder because it cannot perform any action. 8 | 3. The instructions should be in English. 9 | 4. The instructions should at least 1 to 2 sentences long. Either an imperative sentence or a question is permitted. 10 | 5. You should generate an appropriate input to the instruction. The input field should contain a specific example provided for the instruction. It should involve realistic data and should not contain simple placeholders. The input should provide substantial content to make the instruction challenging but should ideally not exceed 100 words. 11 | 6. Not all instructions require input. For example, when a instruction asks about some general information, "write a python program to load a file.", it is not necssary to provide a specific context. In this case, we simply put "" in the input field. 12 | 7. The output should be an appropriate response to the instruction and the input. 13 | 8. All tasks should be coding or programming related. 14 | 15 | List of 20 tasks: -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | rouge_score 3 | fire 4 | openai 5 | transformers>=4.26.1 6 | torch 7 | sentencepiece 8 | tokenizers==0.12.1 9 | wandb 10 | -------------------------------------------------------------------------------- /seed_tasks.jsonl: -------------------------------------------------------------------------------- 1 | {"id": "seed_task_1", "name": "complete_python_code", "instruction": "Suggest a completion for the following python code.", "instances": [{"input": "import json\n\ndef read_task_from_jsonl(data_file):\n '''This function will read a .jsonl file and return the ``task`` fields in all the lines.'''", "output": "import json\n\ndef read_task_from_jsonl(data_file):\n '''This function will read a .jsonl file and return the ``task`` fields in all the lines.'''\n with open(data_file) as fin:\n return [json.loads(line)['task'] for line in fin]"}], "is_classification": false} 2 | {"id": "seed_task_2", "name": "code_todo", "instruction": "Using the comment as a guide, replace the \"TODO\" comment in the code with the proper code.", "instances": [{"input": "def greet_to(name):\n \"\"\"Print a greeting to the name provided.\"\"\"\n # TODO: assert name is a string\n print(\"Hello, \" + name + \"!\")", "output": "def greet_to(name):\n \"\"\"Print a greeting to the name provided.\"\"\"\n # assert name is a string\n assert isinstance(name, str), \"name must be a string\"\n print(\"Hello, \" + name + \"!\")"}], "is_classification": false} 3 | {"id": "seed_task_3", "name": "python_code_generation", "instruction": "Generate a correct python program to fulfill the following purpose:", "instances": [{"input": "Count the occurrence of an element x in a list y.", "output": "def count_x(x, y):\n count = 0\n for i in range(y):\n if i == x:\n count += 1\n return count"}], "is_classification": false} 4 | {"id": "seed_task_4", "name": "code_for_bitcoin_price", "instruction": "write a code to get the current value of a Bitcoin in US dollars using the bitcoincharts API.", "instances": [{"input": "", "output": "\"\"\"\nGet the current value of a Bitcoin in US dollars using the bitcoincharts api\n\"\"\"\n\nimport requests\nimport json\n\ndef get_bitcoin_price():\n url = 'http://api.bitcoincharts.com/v1/weighted_prices.json'\n response = requests.get(url)\n data = json.loads(response.text)\n return data['USD']['24h']\n \nif __name__ == '__main__':\n print(get_bitcoin_price())"}], "is_classification": false} 5 | {"id": "seed_task_5", "name": "program_for_summation", "instruction": "Write a program to compute the sum of integers from k to n.", "instances": [{"input": "", "output": "def sum(k, n):\n sum = 0\n for i in range(k, n+1):\n sum += i\n return sum"}], "is_classification": false} 6 | {"id": "seed_task_6", "name": "javascript_date_and_time", "instruction": "Write a script in JavaScript for printing the current date and time.", "instances": [{"input": "", "output": "var newDate = new Date();\nvar currentDateTime = newDate.toLocaleString();\nconsole.log(currentDateTime);"}], "is_classification": false} 7 | {"id": "seed_task_7", "name": "python_leetcode", "instruction": "Write Python code to solve this leetcode problem.", "instances": [{"input": "Problem: You are given two non-empty linked lists representing two non-negative integers. The digits are stored in reverse order, and each of their nodes contains a single digit. Add the two numbers and return the sum as a linked list.\n\nYou may assume the two numbers do not contain any leading zero, except the number 0 itself.", "output": "class Solution(object):\n def addTwoNumbers(self, l1, l2):\n \"\"\"\n :type l1: ListNode\n :type l2: ListNode\n :rtype: ListNode\n \"\"\"\n carry = 0\n root = n = ListNode(0)\n while l1 or l2 or carry:\n v1 = v2 = 0\n if l1:\n v1 = l1.val\n l1 = l1.next\n if l2:\n v2 = l2.val\n l2 = l2.next\n carry, val = divmod(v1+v2+carry, 10)\n n.next = ListNode(val)\n n = n.next\n return root.next"}], "is_classification": false} 8 | {"id": "seed_task_8", "name": "pos_tag_for_word", "instruction": "Identify the pos tag of the word in the given sentence.", "instances": [{"input": "Word: excited\nSentence: She felt so excited that her application got approval.", "output": "Adjective, JJ."}], "is_classification": true} 9 | {"id": "seed_task_9", "name": "code_syntax_error", "instruction": "You need to read a code and detect if there is a syntax error or not. Outpus true if there is error, output false if there is not.", "instances": [{"input": "def quick_sort(arr):\n if len(arr) < 2:\n return arr\n else:\n pivot = arr[0]\n less = [i for i in arr[1:] if i <= pivot]\n greater = [i for i in arr[1:] if i > pivot]\n return quick_sort(less) + [pivot] + quick_sort(greater)", "output": "false"}], "is_classification": true} 10 | {"id": "seed_task_10", "name": "find_prime_numbers", "instruction": "Write a Python function to find all prime numbers up to the given number n.", "instances": [{"input": "", "output": "def is_prime(n):\n if n <= 1:\n return False\n for i in range(2, int(n**0.5) + 1):\n if n % i == 0:\n return False\n return True\n\ndef find_prime_numbers(n):\n primes = []\n for i in range(2, n + 1):\n if is_prime(i):\n primes.append(i)\n return primes"}], "is_classification": false} 11 | {"id": "seed_task_11", "name": "csharp_hello_world", "instruction": "Write a simple C# program that prints \"Hello, World!\".", "instances": [{"input": "", "output": "using System;\n\nnamespace HelloWorld\n{\n class Program\n {\n static void Main(string[] args)\n {\n Console.WriteLine(\"Hello, World!\");\n }\n }\n}"}], "is_classification": false} 12 | {"id": "seed_task_12", "name": "sql_query", "instruction": "Write an SQL query to select all columns from a table named 'employees' where the age is greater than 30.", "instances": [{"input": "", "output": "SELECT * FROM employees WHERE age > 30;"}], "is_classification": false} 13 | {"id": "seed_task_13", "name": "palindrome_check", "instruction": "Write a Python function to check if a given word is a palindrome or not. A palindrome is a word that reads the same backwards as forwards.", "instances": [{"input": "", "output": "def is_palindrome(word):\n return word == word[::-1]"}], "is_classification": false} 14 | {"id": "seed_task_14", "name": "javascript_alert", "instruction": "Write a JavaScript code snippet to show an alert message with the text \"Welcome to my website!\".", "instances": [{"input": "", "output": "alert('Welcome to my website!');"}], "is_classification": false} 15 | {"id": "seed_task_15", "name": "python_reverse_list", "instruction": "Write a Python function to reverse a given list of integers.", "instances": [{"input": "", "output": "def reverse_list(numbers):\n return numbers[::-1]"}], "is_classification": false} 16 | {"id": "seed_task_16", "name": "html_basic_structure", "instruction": "Write a basic HTML structure with a title \"My First Web Page\".", "instances": [{"input": "", "output": "\n\n\n \n \n My First Web Page\n\n\n\n\n"}], "is_classification": false} 17 | {"id": "seed_task_17", "name": "c_plus_plus_hello_world", "instruction": "Write a simple C++ program that prints \"Hello, World!\".", "instances": [{"input": "", "output": "#include \n\nint main() {\n std::cout << \"Hello, World!\" << std::endl;\n return 0;\n}"}], "is_classification": false} 18 | {"id": "seed_task_18", "name": "python_summation", "instruction": "Write a Python function that takes two numbers as input and returns their sum.", "instances": [{"input": "5, 7", "output": "def add_numbers(a, b):\n return a + b\n\nresult = add_numbers(5, 7)\nprint(result)"}], "is_classification": false} 19 | {"id": "seed_task_19", "name": "java_loop_array", "instruction": "Create a JavaScript function that takes an array of numbers and returns the product of all elements.", "instances": [{"input": "[2, 3, 4]", "output": "function multiplyArrayElements(arr) {\n let product = 1;\n for (const num of arr) {\n product *= num;\n }\n return product;\n}\n\nconst result = multiplyArrayElements([2, 3, 4]);\nconsole.log(result);"}], "is_classification": false} 20 | {"id": "seed_task_20", "name": "shell_total_lines_in_txt_file", "instruction": "Can you provide a shell script that displays the total number of lines in all '*.txt' files in the current directory?", "instances": [{"input": "\"file1.txt:\\nHello\\nWorld\\n\\nfile2.txt:\\nAnother\\nExample\"", "output": "#!/bin/sh\n\ntotal_lines=0\n\nfor file in *.txt; do\n lines=$(wc -l < \"$file\")\n total_lines=$((total_lines + lines))\ndone\n\necho \"Total lines: $total_lines\""}], "is_classification": false} 21 | {"id": "seed_task_21", "name": "java_reverse_string_no_builtin", "instruction": "Implement a Java method to reverse a string without using any built-in functions.", "instances": [{"input": "\"Hello, World!\"", "output": "public class StringReversal {\n public static String reverseString(String input) {\n char[] charArray = input.toCharArray();\n int left = 0;\n int right = charArray.length - 1;\n\n while (left < right) {\n char temp = charArray[left];\n charArray[left] = charArray[right];\n charArray[right] = temp;\n\n left++;\n right--;\n }\n return new String(charArray);\n }\n\n public static void main(String[] args) {\n String str = \"Hello, World!\";\n System.out.println(\"Reversed string: \" + reverseString(str));\n }\n}"}], "is_classification": false} -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | # 2 | # Licensed under the Apache License, Version 2.0 (the "License"); 3 | # you may not use this file except in compliance with the License. 4 | # You may obtain a copy of the License at 5 | # 6 | # http://www.apache.org/licenses/LICENSE-2.0 7 | # 8 | # Unless required by applicable law or agreed to in writing, software 9 | # distributed under the License is distributed on an "AS IS" BASIS, 10 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 11 | # See the License for the specific language governing permissions and 12 | # limitations under the License. 13 | 14 | import copy 15 | import logging 16 | from dataclasses import dataclass, field 17 | from typing import Optional, Dict, Sequence 18 | 19 | import torch 20 | import transformers 21 | from torch.utils.data import Dataset 22 | from transformers import Trainer 23 | 24 | import utils 25 | 26 | IGNORE_INDEX = -100 27 | DEFAULT_PAD_TOKEN = "[PAD]" 28 | DEFAULT_EOS_TOKEN = "" 29 | DEFAULT_BOS_TOKEN = "" 30 | DEFAULT_UNK_TOKEN = "" 31 | PROMPT_DICT = { 32 | "prompt_input": ( 33 | "Below is an instruction that describes a task, paired with an input that provides further context. " 34 | "Write a response that appropriately completes the request.\n\n" 35 | "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:" 36 | ), 37 | "prompt_no_input": ( 38 | "Below is an instruction that describes a task. " 39 | "Write a response that appropriately completes the request.\n\n" 40 | "### Instruction:\n{instruction}\n\n### Response:" 41 | ), 42 | } 43 | 44 | 45 | @dataclass 46 | class ModelArguments: 47 | model_name_or_path: Optional[str] = field(default="facebook/opt-125m") 48 | 49 | 50 | @dataclass 51 | class DataArguments: 52 | data_path: str = field(default=None, metadata={"help": "Path to the training data."}) 53 | 54 | 55 | @dataclass 56 | class TrainingArguments(transformers.TrainingArguments): 57 | cache_dir: Optional[str] = field(default=None) 58 | optim: str = field(default="adamw_torch") 59 | model_max_length: int = field( 60 | default=512, 61 | metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."}, 62 | ) 63 | 64 | 65 | def smart_tokenizer_and_embedding_resize( 66 | special_tokens_dict: Dict, 67 | tokenizer: transformers.PreTrainedTokenizer, 68 | model: transformers.PreTrainedModel, 69 | ): 70 | """Resize tokenizer and embedding. 71 | 72 | Note: This is the unoptimized version that may make your embedding size not be divisible by 64. 73 | """ 74 | num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict) 75 | model.resize_token_embeddings(len(tokenizer)) 76 | 77 | if num_new_tokens > 0: 78 | input_embeddings = model.get_input_embeddings().weight.data 79 | output_embeddings = model.get_output_embeddings().weight.data 80 | 81 | input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) 82 | output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) 83 | 84 | input_embeddings[-num_new_tokens:] = input_embeddings_avg 85 | output_embeddings[-num_new_tokens:] = output_embeddings_avg 86 | 87 | 88 | def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict: 89 | """Tokenize a list of strings.""" 90 | tokenized_list = [ 91 | tokenizer( 92 | text, 93 | return_tensors="pt", 94 | padding="longest", 95 | max_length=tokenizer.model_max_length, 96 | truncation=True, 97 | ) 98 | for text in strings 99 | ] 100 | input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list] 101 | input_ids_lens = labels_lens = [ 102 | tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list 103 | ] 104 | return dict( 105 | input_ids=input_ids, 106 | labels=labels, 107 | input_ids_lens=input_ids_lens, 108 | labels_lens=labels_lens, 109 | ) 110 | 111 | 112 | def preprocess( 113 | sources: Sequence[str], 114 | targets: Sequence[str], 115 | tokenizer: transformers.PreTrainedTokenizer, 116 | ) -> Dict: 117 | """Preprocess the data by tokenizing.""" 118 | examples = [s + t for s, t in zip(sources, targets)] 119 | examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer) for strings in (examples, sources)] 120 | input_ids = examples_tokenized["input_ids"] 121 | labels = copy.deepcopy(input_ids) 122 | for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]): 123 | label[:source_len] = IGNORE_INDEX 124 | return dict(input_ids=input_ids, labels=labels) 125 | 126 | 127 | class SupervisedDataset(Dataset): 128 | """Dataset for supervised fine-tuning.""" 129 | 130 | def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer): 131 | super(SupervisedDataset, self).__init__() 132 | logging.warning("Loading data...") 133 | list_data_dict = utils.jload(data_path) 134 | 135 | logging.warning("Formatting inputs...") 136 | prompt_input, prompt_no_input = PROMPT_DICT["prompt_input"], PROMPT_DICT["prompt_no_input"] 137 | sources = [ 138 | prompt_input.format_map(example) if example.get("input", "") != "" else prompt_no_input.format_map(example) 139 | for example in list_data_dict 140 | ] 141 | targets = [f"{example['output']}{tokenizer.eos_token}" for example in list_data_dict] 142 | 143 | logging.warning("Tokenizing inputs... This may take some time...") 144 | data_dict = preprocess(sources, targets, tokenizer) 145 | 146 | self.input_ids = data_dict["input_ids"] 147 | self.labels = data_dict["labels"] 148 | 149 | def __len__(self): 150 | return len(self.input_ids) 151 | 152 | def __getitem__(self, i) -> Dict[str, torch.Tensor]: 153 | return dict(input_ids=self.input_ids[i], labels=self.labels[i]) 154 | 155 | 156 | @dataclass 157 | class DataCollatorForSupervisedDataset(object): 158 | """Collate examples for supervised fine-tuning.""" 159 | 160 | tokenizer: transformers.PreTrainedTokenizer 161 | 162 | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]: 163 | input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels")) 164 | input_ids = torch.nn.utils.rnn.pad_sequence( 165 | input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id 166 | ) 167 | labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX) 168 | return dict( 169 | input_ids=input_ids, 170 | labels=labels, 171 | attention_mask=input_ids.ne(self.tokenizer.pad_token_id), 172 | ) 173 | 174 | 175 | def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer, data_args) -> Dict: 176 | """Make dataset and collator for supervised fine-tuning.""" 177 | train_dataset = SupervisedDataset(tokenizer=tokenizer, data_path=data_args.data_path) 178 | data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) 179 | return dict(train_dataset=train_dataset, eval_dataset=None, data_collator=data_collator) 180 | 181 | 182 | def train(): 183 | parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments)) 184 | model_args, data_args, training_args = parser.parse_args_into_dataclasses() 185 | 186 | model = transformers.AutoModelForCausalLM.from_pretrained( 187 | model_args.model_name_or_path, 188 | cache_dir=training_args.cache_dir, 189 | ) 190 | 191 | tokenizer = transformers.AutoTokenizer.from_pretrained( 192 | model_args.model_name_or_path, 193 | cache_dir=training_args.cache_dir, 194 | model_max_length=training_args.model_max_length, 195 | padding_side="right", 196 | use_fast=False, 197 | ) 198 | if tokenizer.pad_token is None: 199 | smart_tokenizer_and_embedding_resize( 200 | special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN), 201 | tokenizer=tokenizer, 202 | model=model, 203 | ) 204 | if "llama" in model_args.model_name_or_path: 205 | tokenizer.add_special_tokens( 206 | { 207 | "eos_token": DEFAULT_EOS_TOKEN, 208 | "bos_token": DEFAULT_BOS_TOKEN, 209 | "unk_token": DEFAULT_UNK_TOKEN, 210 | } 211 | ) 212 | 213 | data_module = make_supervised_data_module(tokenizer=tokenizer, data_args=data_args) 214 | trainer = Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module) 215 | trainer.train() 216 | trainer.save_model(training_args.output_dir) 217 | 218 | 219 | if __name__ == "__main__": 220 | train() 221 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import dataclasses 2 | import logging 3 | import math 4 | import os 5 | import io 6 | import sys 7 | import time 8 | import json 9 | from typing import Optional, Sequence, Union 10 | 11 | import openai 12 | import tqdm 13 | from openai import openai_object 14 | import copy 15 | 16 | StrOrOpenAIObject = Union[str, openai_object.OpenAIObject] 17 | 18 | openai_org = os.getenv("OPENAI_ORG") 19 | if openai_org is not None: 20 | openai.organization = openai_org 21 | logging.warning(f"Switching to organization: {openai_org} for OAI API key.") 22 | 23 | 24 | @dataclasses.dataclass 25 | class OpenAIDecodingArguments(object): 26 | max_tokens: int = 1800 27 | temperature: float = 0.2 28 | top_p: float = 1.0 29 | n: int = 1 30 | stream: bool = False 31 | stop: Optional[Sequence[str]] = None 32 | presence_penalty: float = 0.0 33 | frequency_penalty: float = 0.0 34 | suffix: Optional[str] = None 35 | logprobs: Optional[int] = None 36 | echo: bool = False 37 | 38 | 39 | def openai_completion( 40 | prompts: Union[str, Sequence[str], Sequence[dict[str, str]], dict[str, str]], 41 | decoding_args: OpenAIDecodingArguments, 42 | model_name="text-davinci-003", 43 | sleep_time=2, 44 | batch_size=1, 45 | max_instances=sys.maxsize, 46 | max_batches=sys.maxsize, 47 | return_text=False, 48 | **decoding_kwargs, 49 | ) -> Union[Union[StrOrOpenAIObject], Sequence[StrOrOpenAIObject], Sequence[Sequence[StrOrOpenAIObject]],]: 50 | """Decode with OpenAI API. 51 | 52 | Args: 53 | prompts: A string or a list of strings to complete. If it is a chat model the strings should be formatted 54 | as explained here: https://github.com/openai/openai-python/blob/main/chatml.md. If it is a chat model 55 | it can also be a dictionary (or list thereof) as explained here: 56 | https://github.com/openai/openai-cookbook/blob/main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb 57 | decoding_args: Decoding arguments. 58 | model_name: Model name. Can be either in the format of "org/model" or just "model". 59 | sleep_time: Time to sleep once the rate-limit is hit. 60 | batch_size: Number of prompts to send in a single request. Only for non chat model. 61 | max_instances: Maximum number of prompts to decode. 62 | max_batches: Maximum number of batches to decode. This argument will be deprecated in the future. 63 | return_text: If True, return text instead of full completion object (which contains things like logprob). 64 | decoding_kwargs: Additional decoding arguments. Pass in `best_of` and `logit_bias` if you need them. 65 | 66 | Returns: 67 | A completion or a list of completions. 68 | Depending on return_text, return_openai_object, and decoding_args.n, the completion type can be one of 69 | - a string (if return_text is True) 70 | - an openai_object.OpenAIObject object (if return_text is False) 71 | - a list of objects of the above types (if decoding_args.n > 1) 72 | """ 73 | is_single_prompt = isinstance(prompts, (str, dict)) 74 | if is_single_prompt: 75 | prompts = [prompts] 76 | 77 | if max_batches < sys.maxsize: 78 | logging.warning( 79 | "`max_batches` will be deprecated in the future, please use `max_instances` instead." 80 | "Setting `max_instances` to `max_batches * batch_size` for now." 81 | ) 82 | max_instances = max_batches * batch_size 83 | 84 | prompts = prompts[:max_instances] 85 | num_prompts = len(prompts) 86 | prompt_batches = [ 87 | prompts[batch_id * batch_size : (batch_id + 1) * batch_size] 88 | for batch_id in range(int(math.ceil(num_prompts / batch_size))) 89 | ] 90 | 91 | completions = [] 92 | for batch_id, prompt_batch in tqdm.tqdm( 93 | enumerate(prompt_batches), 94 | desc="prompt_batches", 95 | total=len(prompt_batches), 96 | ): 97 | batch_decoding_args = copy.deepcopy(decoding_args) # cloning the decoding_args 98 | 99 | while True: 100 | try: 101 | shared_kwargs = dict( 102 | model=model_name, 103 | **batch_decoding_args.__dict__, 104 | **decoding_kwargs, 105 | ) 106 | completion_batch = openai.Completion.create(prompt=prompt_batch, **shared_kwargs) 107 | choices = completion_batch.choices 108 | 109 | for choice in choices: 110 | choice["total_tokens"] = completion_batch.usage.total_tokens 111 | completions.extend(choices) 112 | break 113 | except openai.error.OpenAIError as e: 114 | logging.warning(f"OpenAIError: {e}.") 115 | if "Please reduce your prompt" in str(e): 116 | batch_decoding_args.max_tokens = int(batch_decoding_args.max_tokens * 0.8) 117 | logging.warning(f"Reducing target length to {batch_decoding_args.max_tokens}, Retrying...") 118 | else: 119 | logging.warning("Hit request rate limit; retrying...") 120 | time.sleep(sleep_time) # Annoying rate limit on requests. 121 | 122 | if return_text: 123 | completions = [completion.text for completion in completions] 124 | if decoding_args.n > 1: 125 | # make completions a nested list, where each entry is a consecutive decoding_args.n of original entries. 126 | completions = [completions[i : i + decoding_args.n] for i in range(0, len(completions), decoding_args.n)] 127 | if is_single_prompt: 128 | # Return non-tuple if only 1 input and 1 generation. 129 | (completions,) = completions 130 | return completions 131 | 132 | 133 | def _make_w_io_base(f, mode: str): 134 | if not isinstance(f, io.IOBase): 135 | f_dirname = os.path.dirname(f) 136 | if f_dirname != "": 137 | os.makedirs(f_dirname, exist_ok=True) 138 | f = open(f, mode=mode) 139 | return f 140 | 141 | 142 | def _make_r_io_base(f, mode: str): 143 | if not isinstance(f, io.IOBase): 144 | f = open(f, mode=mode) 145 | return f 146 | 147 | 148 | def jdump(obj, f, mode="w", indent=4, default=str): 149 | """Dump a str or dictionary to a file in json format. 150 | 151 | Args: 152 | obj: An object to be written. 153 | f: A string path to the location on disk. 154 | mode: Mode for opening the file. 155 | indent: Indent for storing json dictionaries. 156 | default: A function to handle non-serializable entries; defaults to `str`. 157 | """ 158 | f = _make_w_io_base(f, mode) 159 | if isinstance(obj, (dict, list)): 160 | json.dump(obj, f, indent=indent, default=default) 161 | elif isinstance(obj, str): 162 | f.write(obj) 163 | else: 164 | raise ValueError(f"Unexpected type: {type(obj)}") 165 | f.close() 166 | 167 | 168 | def jload(f, mode="r"): 169 | """Load a .json file into a dictionary.""" 170 | f = _make_r_io_base(f, mode) 171 | jdict = json.load(f) 172 | f.close() 173 | return jdict 174 | --------------------------------------------------------------------------------