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3 | Stanford-Alpaca 4 |

5 | 6 | # Stanford Alpaca Trainer - Updated for use training Replit's Code Model 7 | 8 | [![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)](https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE) 9 | [![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/release/python-390/) 10 | [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) 11 | 12 | This was the repo for the Stanford Alpaca project, which is edited to become a trainer for Alpaca-format datasets over Replit's 3B Code Model: 13 | 14 | - The Base Model: [Replit 3B Code](https://huggingface.co/replit/replit-code-v1-3b) 15 | 16 | - The code for [fine-tuning the model](#fine-tuning). 17 | 18 | 19 | ## Overview 20 | 21 | A trainer for Replit's 3B parameter code model. 22 | 23 | ## Dataset Format 24 | 25 | Alpaca format datasets should be in the following format, in json: 26 | 27 | - `instruction`: `str`, describes the task the model should perform. Each of the 52K instructions is unique. 28 | - `input`: `str`, optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input. 29 | - `output`: `str`, the answer to the instruction as generated by `text-davinci-003`. 30 | 31 | ### Here is an example of a dataset: 32 | ``` 33 | [ 34 | { 35 | "instruction": "Give three tips for staying healthy.", 36 | "input": "", 37 | "output": "1. Eat a balanced and nutritious diet: Make sure your meals are inclusive of a variety of fruits and vegetables, lean protein, whole grains, and healthy fats. This helps to provide your body with the essential nutrients to function at its best and can help prevent chronic diseases.\n\n2. Engage in regular physical activity: Exercise is crucial for maintaining strong bones, muscles, and cardiovascular health. Aim for at least 150 minutes of moderate aerobic exercise or 75 minutes of vigorous exercise each week.\n\n3. Get enough sleep: Getting enough quality sleep is crucial for physical and mental well-being. It helps to regulate mood, improve cognitive function, and supports healthy growth and immune function. Aim for 7-9 hours of sleep each night." 38 | }, 39 | { 40 | "instruction": "What are the three primary colors?", 41 | "input": "", 42 | "output": "The three primary colors are red, blue, and yellow. These colors are called primary because they cannot be created by mixing other colors and all other colors can be made by combining them in various proportions. In the additive color system, used for light, the primary colors are red, green, and blue (RGB)." 43 | }, 44 | ] 45 | ``` 46 | 47 | We used the following prompts for fine-tuning the Replit model: 48 | 49 | - for examples with a non-empty input field: 50 | 51 | ``` 52 | ### Instruction: 53 | {instruction} 54 | 55 | ### Input: 56 | {input} 57 | 58 | ### Response: 59 | ``` 60 | 61 | - for examples with an empty input field: 62 | 63 | ``` 64 | ### Instruction: 65 | {instruction} 66 | 67 | ### Response: 68 | ``` 69 | 70 | ## Fine-tuning 71 | 72 | To fine-tune for Replit's model, first install the requirements 73 | 74 | ```bash 75 | pip install -r requirements.txt 76 | ``` 77 | 78 | The train.py script defaults to 2000 sequence length for training. It runs in small batch size at this sequence length on an a100 80gb. You will save a significant amount of vram, and thus, can train faster, with a smaller sequence length. Training on 2x a100 80gb with what is possible with 2000 token sequence length takes about 2.5 hours, with 512 token length, only 45~ minutes. 79 | 80 | Below is a command that fine-tunes Replit-3B with an alpaca-formated dataset on a machine with 2 A100 80G GPUs with 2000 token sequence length. 81 | 82 | Replace `` with a port of your own, `` with the path to your converted checkpoint and tokenizer or leave default for Replit's base code model, and `` with where you want to store your outputs. 83 | 84 | ```bash 85 | torchrun --nproc_per_node=2 --master_port= train.py \ 86 | --model_name_or_path \ 87 | --data_path ./.json \ 88 | --bf16 True \ 89 | --output_dir \ 90 | --num_train_epochs 3 \ 91 | --per_device_train_batch_size 1 \ 92 | --gradient_accumulation_steps 4 \ 93 | --evaluation_strategy "no" \ 94 | --save_strategy "steps" \ 95 | --save_steps 50 \ 96 | --save_total_limit 2 \ 97 | --learning_rate 2e-5 \ 98 | --weight_decay 0. \ 99 | --warmup_ratio 0.03 \ 100 | --lr_scheduler_type "cosine" \ 101 | --logging_steps 1 \ 102 | ``` 103 | 104 | Note the given training script is meant to be simple and easy to use, and is not particularly optimized. 105 | To run on more gpus, you may prefer to turn down `gradient_accumulation_steps` to keep a global batch size of 128. Global batch size has not been tested for optimality. 106 | 107 | ### Addressing OOM 108 | 109 | Naively, fine-tuning a 7B model requires about 7 x 4 x 4 = 112 GB of VRAM. Commands given above enable parameter sharding, so no redundant model copy is stored on any GPU. 110 | If you'd like to further reduce the memory footprint, here are some options: 111 | 112 | - Turn on CPU offload for FSDP with `--fsdp "full_shard auto_wrap offload"`. This saves VRAM at the cost of longer runtime. 113 | - In our experience, DeepSpeed stage-3 (with offload) can at times be more memory efficient than FSDP with offload. Here's an example to use DeepSpeed stage-3 with 4 GPUs with both parameter and optimizer offload: 114 | ```bash 115 | pip install deepspeed 116 | torchrun --nproc_per_node=4 --master_port= train.py \ 117 | --model_name_or_path \ 118 | --data_path ./alpaca_data.json \ 119 | --bf16 True \ 120 | --output_dir \ 121 | --num_train_epochs 3 \ 122 | --per_device_train_batch_size 4 \ 123 | --per_device_eval_batch_size 4 \ 124 | --gradient_accumulation_steps 8 \ 125 | --evaluation_strategy "no" \ 126 | --save_strategy "steps" \ 127 | --save_steps 2000 \ 128 | --save_total_limit 1 \ 129 | --learning_rate 2e-5 \ 130 | --weight_decay 0. \ 131 | --warmup_ratio 0.03 \ 132 | --deepspeed "./configs/default_offload_opt_param.json" \ 133 | --tf32 True 134 | ``` 135 | - The DeepSpeed library also provides some [helpful functions](https://deepspeed.readthedocs.io/en/latest/memory.html) to estimate memory usage. 136 | - [LoRA](https://arxiv.org/abs/2106.09685) fine-tunes low-rank slices of the query, key, and value embedding heads. This can reduce the total memory footprint from 112GB to about 7x4=28GB. We may release our re-implemention of this in the future, but for now the [peft](https://github.com/huggingface/peft) codebase can be a useful resource. 137 | 138 | ### Original Authors of the Alpaca paper 139 | 140 | All grad students below contributed equally and the order is determined by random draw. 141 | 142 | - [Rohan Taori](https://www.rohantaori.com/) 143 | - [Ishaan Gulrajani](https://ishaan.io/) 144 | - [Tianyi Zhang](https://tiiiger.github.io/) 145 | - [Yann Dubois](https://yanndubs.github.io/) 146 | - [Xuechen Li](https://www.lxuechen.com/) 147 | 148 | All advised by [Tatsunori B. Hashimoto](https://thashim.github.io/). Yann is also advised by [Percy Liang](https://cs.stanford.edu/~pliang/) and Xuechen is also advised by [Carlos Guestrin](https://guestrin.su.domains/). 149 | 150 | ### Citation 151 | 152 | Please cite the repo if you use the data or code in this repo. 153 | 154 | ``` 155 | @misc{alpaca, 156 | author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, 157 | title = {Stanford Alpaca: An Instruction-following LLaMA model}, 158 | year = {2023}, 159 | publisher = {GitHub}, 160 | journal = {GitHub repository}, 161 | howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, 162 | } 163 | ``` 164 | 165 | Naturally, you should also cite the original LLaMA paper [1] and the Self-Instruct paper [2]. 166 | 167 | ### Acknowledgements 168 | 169 | We thank Yizhong Wang for his help in explaining the data generation pipeline in Self-Instruct and providing the code for the parse analysis plot. 170 | We thank Yifan Mai for helpful support, and members of the Stanford NLP Group as well as the Center for Research on Foundation Models (CRFM) for their helpful feedback. 171 | -------------------------------------------------------------------------------- /assets/alpaca_main.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/teknium1/stanford_alpaca-replit/855ccdf2184f185e6c4a9c1ddbca46f000a8d9fc/assets/alpaca_main.jpg -------------------------------------------------------------------------------- /assets/alpaca_right_email.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/teknium1/stanford_alpaca-replit/855ccdf2184f185e6c4a9c1ddbca46f000a8d9fc/assets/alpaca_right_email.png -------------------------------------------------------------------------------- /assets/alpaca_right_llama.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/teknium1/stanford_alpaca-replit/855ccdf2184f185e6c4a9c1ddbca46f000a8d9fc/assets/alpaca_right_llama.png -------------------------------------------------------------------------------- /assets/alpaca_wrong_42.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/teknium1/stanford_alpaca-replit/855ccdf2184f185e6c4a9c1ddbca46f000a8d9fc/assets/alpaca_wrong_42.png -------------------------------------------------------------------------------- /assets/alpaca_wrong_capital.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/teknium1/stanford_alpaca-replit/855ccdf2184f185e6c4a9c1ddbca46f000a8d9fc/assets/alpaca_wrong_capital.png -------------------------------------------------------------------------------- /assets/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/teknium1/stanford_alpaca-replit/855ccdf2184f185e6c4a9c1ddbca46f000a8d9fc/assets/logo.png -------------------------------------------------------------------------------- /assets/parse_analysis.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/teknium1/stanford_alpaca-replit/855ccdf2184f185e6c4a9c1ddbca46f000a8d9fc/assets/parse_analysis.png -------------------------------------------------------------------------------- /configs/default_offload_opt_param.json: -------------------------------------------------------------------------------- 1 | { 2 | "bf16": { 3 | "enabled": "auto" 4 | }, 5 | "optimizer": { 6 | "type": "AdamW", 7 | "params": { 8 | "lr": "auto", 9 | "betas": "auto", 10 | "eps": "auto", 11 | "weight_decay": "auto" 12 | } 13 | }, 14 | "scheduler": { 15 | "type": "WarmupDecayLR", 16 | "params": { 17 | "total_num_steps": "auto", 18 | "warmup_min_lr": "auto", 19 | "warmup_max_lr": "auto", 20 | "warmup_num_steps": "auto" 21 | } 22 | }, 23 | "zero_optimization": { 24 | "stage": 3, 25 | "offload_optimizer": { 26 | "device": "cpu", 27 | "pin_memory": true 28 | }, 29 | "offload_param": { 30 | "device": "cpu", 31 | "pin_memory": true 32 | }, 33 | "overlap_comm": true, 34 | "contiguous_gradients": true, 35 | "sub_group_size": 1e9, 36 | "reduce_bucket_size": "auto", 37 | "stage3_prefetch_bucket_size": "auto", 38 | "stage3_param_persistence_threshold": "auto", 39 | "stage3_max_live_parameters": 1e9, 40 | "stage3_max_reuse_distance": 1e9, 41 | "stage3_gather_16bit_weights_on_model_save": false 42 | }, 43 | "gradient_accumulation_steps": "auto", 44 | "gradient_clipping": "auto", 45 | "steps_per_print": 5, 46 | "train_batch_size": "auto", 47 | "train_micro_batch_size_per_gpu": "auto", 48 | "wall_clock_breakdown": false 49 | } 50 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | rouge_score 3 | fire 4 | transformers>=4.28.1 5 | torch 6 | sentencepiece 7 | tokenizers>=0.13.3 8 | wandb 9 | einops 10 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | # Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import copy 16 | import logging 17 | from dataclasses import dataclass, field 18 | from typing import Dict, Optional, Sequence 19 | 20 | import torch 21 | import transformers 22 | import utils 23 | from torch.utils.data import Dataset 24 | from transformers import Trainer 25 | 26 | IGNORE_INDEX = -100 27 | DEFAULT_PAD_TOKEN = "<|pad|>" 28 | DEFAULT_EOS_TOKEN = "<|endoftext|>" 29 | DEFAULT_UNK_TOKEN = "<|unk|>" 30 | PROMPT_DICT = { 31 | "prompt_input": ( 32 | "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:" 33 | ), 34 | "prompt_no_input": ( 35 | "### Instruction:\n{instruction}\n\n### Response:" 36 | ), 37 | } 38 | 39 | 40 | @dataclass 41 | class ModelArguments: 42 | model_name_or_path: Optional[str] = field(default="replit/replit-code-v1-3b") 43 | 44 | 45 | @dataclass 46 | class DataArguments: 47 | data_path: str = field(default=None, metadata={"help": "Path to the training data."}) 48 | 49 | 50 | @dataclass 51 | class TrainingArguments(transformers.TrainingArguments): 52 | cache_dir: Optional[str] = field(default=None) 53 | optim: str = field(default="adamw_torch_fused") 54 | model_max_length: int = field( 55 | default=2000, 56 | metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."}, 57 | ) 58 | 59 | 60 | def smart_tokenizer_and_embedding_resize( 61 | special_tokens_dict: Dict, 62 | tokenizer: transformers.PreTrainedTokenizer, 63 | model: transformers.PreTrainedModel, 64 | ): 65 | """Resize tokenizer and embedding. 66 | 67 | Note: This is the unoptimized version that may make your embedding size not be divisible by 64. 68 | """ 69 | num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict) 70 | model.resize_token_embeddings(len(tokenizer)) 71 | 72 | if num_new_tokens > 0: 73 | input_embeddings = model.get_input_embeddings().weight.data 74 | output_embeddings = model.get_output_embeddings().weight.data 75 | 76 | input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) 77 | output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) 78 | 79 | input_embeddings[-num_new_tokens:] = input_embeddings_avg 80 | output_embeddings[-num_new_tokens:] = output_embeddings_avg 81 | 82 | 83 | def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict: 84 | """Tokenize a list of strings.""" 85 | tokenized_list = [ 86 | tokenizer( 87 | text, 88 | return_tensors="pt", 89 | padding="longest", 90 | max_length=tokenizer.model_max_length, 91 | truncation=True, 92 | ) 93 | for text in strings 94 | ] 95 | input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list] 96 | input_ids_lens = labels_lens = [ 97 | tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list 98 | ] 99 | return dict( 100 | input_ids=input_ids, 101 | labels=labels, 102 | input_ids_lens=input_ids_lens, 103 | labels_lens=labels_lens, 104 | ) 105 | 106 | 107 | def preprocess( 108 | sources: Sequence[str], 109 | targets: Sequence[str], 110 | tokenizer: transformers.PreTrainedTokenizer, 111 | ) -> Dict: 112 | """Preprocess the data by tokenizing.""" 113 | examples = [s + t for s, t in zip(sources, targets)] 114 | examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer) for strings in (examples, sources)] 115 | input_ids = examples_tokenized["input_ids"] 116 | labels = copy.deepcopy(input_ids) 117 | for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]): 118 | label[:source_len] = IGNORE_INDEX 119 | return dict(input_ids=input_ids, labels=labels) 120 | 121 | 122 | class SupervisedDataset(Dataset): 123 | """Dataset for supervised fine-tuning.""" 124 | 125 | def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer): 126 | super(SupervisedDataset, self).__init__() 127 | logging.warning("Loading data...") 128 | list_data_dict = utils.jload(data_path) 129 | 130 | logging.warning("Formatting inputs...") 131 | prompt_input, prompt_no_input = PROMPT_DICT["prompt_input"], PROMPT_DICT["prompt_no_input"] 132 | sources = [ 133 | prompt_input.format_map(example) if example.get("input", "") != "" else prompt_no_input.format_map(example) 134 | for example in list_data_dict 135 | ] 136 | targets = [f"{example['output']}{tokenizer.eos_token}" for example in list_data_dict] 137 | 138 | logging.warning("Tokenizing inputs... This may take some time...") 139 | data_dict = preprocess(sources, targets, tokenizer) 140 | 141 | self.input_ids = data_dict["input_ids"] 142 | self.labels = data_dict["labels"] 143 | 144 | def __len__(self): 145 | return len(self.input_ids) 146 | 147 | def __getitem__(self, i) -> Dict[str, torch.Tensor]: 148 | return dict(input_ids=self.input_ids[i], labels=self.labels[i]) 149 | 150 | 151 | @dataclass 152 | class DataCollatorForSupervisedDataset(object): 153 | """Collate examples for supervised fine-tuning.""" 154 | 155 | tokenizer: transformers.PreTrainedTokenizer 156 | 157 | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]: 158 | input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels")) 159 | input_ids = torch.nn.utils.rnn.pad_sequence( 160 | input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id 161 | ) 162 | labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX) 163 | return dict( 164 | input_ids=input_ids, 165 | labels=labels, 166 | attention_mask=input_ids.ne(self.tokenizer.pad_token_id), 167 | ) 168 | 169 | 170 | def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer, data_args) -> Dict: 171 | """Make dataset and collator for supervised fine-tuning.""" 172 | train_dataset = SupervisedDataset(tokenizer=tokenizer, data_path=data_args.data_path) 173 | data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) 174 | return dict(train_dataset=train_dataset, eval_dataset=None, data_collator=data_collator) 175 | 176 | 177 | def train(): 178 | parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments)) 179 | model_args, data_args, training_args = parser.parse_args_into_dataclasses() 180 | 181 | model = transformers.AutoModelForCausalLM.from_pretrained( 182 | model_args.model_name_or_path, 183 | cache_dir=training_args.cache_dir, 184 | trust_remote_code=True, 185 | ) 186 | 187 | tokenizer = transformers.AutoTokenizer.from_pretrained( 188 | model_args.model_name_or_path, 189 | cache_dir=training_args.cache_dir, 190 | model_max_length=training_args.model_max_length, 191 | padding_side="right", 192 | use_fast=False, 193 | trust_remote_code=True, 194 | ) 195 | special_tokens_dict = dict() 196 | if tokenizer.pad_token is None: 197 | special_tokens_dict["pad_token"] = DEFAULT_PAD_TOKEN 198 | if tokenizer.eos_token is None: 199 | special_tokens_dict["eos_token"] = DEFAULT_EOS_TOKEN 200 | if tokenizer.unk_token is None: 201 | special_tokens_dict["unk_token"] = DEFAULT_UNK_TOKEN 202 | 203 | smart_tokenizer_and_embedding_resize( 204 | special_tokens_dict=special_tokens_dict, 205 | tokenizer=tokenizer, 206 | model=model, 207 | ) 208 | 209 | data_module = make_supervised_data_module(tokenizer=tokenizer, data_args=data_args) 210 | trainer = Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module) 211 | trainer.train() 212 | trainer.save_state() 213 | trainer.save_model(output_dir=training_args.output_dir) 214 | 215 | 216 | if __name__ == "__main__": 217 | train() 218 | -------------------------------------------------------------------------------- /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 | import tqdm 11 | import copy 12 | 13 | def _make_w_io_base(f, mode: str): 14 | if not isinstance(f, io.IOBase): 15 | f_dirname = os.path.dirname(f) 16 | if f_dirname != "": 17 | os.makedirs(f_dirname, exist_ok=True) 18 | f = open(f, mode=mode) 19 | return f 20 | 21 | 22 | def _make_r_io_base(f, mode: str): 23 | if not isinstance(f, io.IOBase): 24 | f = open(f, mode=mode) 25 | return f 26 | 27 | 28 | def jdump(obj, f, mode="w", indent=4, default=str): 29 | """Dump a str or dictionary to a file in json format. 30 | 31 | Args: 32 | obj: An object to be written. 33 | f: A string path to the location on disk. 34 | mode: Mode for opening the file. 35 | indent: Indent for storing json dictionaries. 36 | default: A function to handle non-serializable entries; defaults to `str`. 37 | """ 38 | f = _make_w_io_base(f, mode) 39 | if isinstance(obj, (dict, list)): 40 | json.dump(obj, f, indent=indent, default=default) 41 | elif isinstance(obj, str): 42 | f.write(obj) 43 | else: 44 | raise ValueError(f"Unexpected type: {type(obj)}") 45 | f.close() 46 | 47 | 48 | def jload(f, mode="r"): 49 | """Load a .json file into a dictionary.""" 50 | f = _make_r_io_base(f, mode) 51 | jdict = json.load(f) 52 | f.close() 53 | return jdict 54 | --------------------------------------------------------------------------------