File size: 12,204 Bytes
cd53b5b 05657f4 41ed25e 9537b41 78877fd 9537b41 bbedb5b 9537b41 15247e4 41ed25e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 |
---
license: bigscience-bloom-rail-1.0
datasets:
- tatsu-lab/alpaca
language:
- en
pipeline_tag: text-generation
tags:
- crayon
- language-technologies
---
# Bloom 560M Finetuned on Instructions
## Credit
Code 99.99% copied from
*https://github.com/bofenghuang/vigogne*
*https://colab.research.google.com/drive/1jCkpikz0J2o20FBQmYmAGdiKmJGOMo-o?usp=sharing#scrollTo=DpYr24pR8T_0*
# Inference Code
```python
from peft import PeftModel
from transformers import PreTrainedTokenizer, PreTrainedModel, AutoTokenizer, AutoModelForCausalLM
from peft import PeftModelForCausalLM, LoraConfig
from typing import Optional
from transformers import GenerationConfig
import torch
PROMPT_DICT = {
"prompt_input": (
"Below is a^n instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
),
"prompt_no_input": (
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response:\n"
),
}
def get_model(model_name_or_path: str, load_in_8bit: bool = True, device_map="auto",
cpu: bool = False) -> PreTrainedModel:
if cpu:
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map=device_map,
low_cpu_mem_usage=True)
else:
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, load_in_8bit=load_in_8bit,
device_map=device_map, torch_dtype=torch.float16)
return model
def get_peft_model(model: PreTrainedModel, lora_model_name_or_path: Optional[str] = None) -> PeftModelForCausalLM:
model = PeftModel.from_pretrained(model, lora_model_name_or_path, torch_dtype=torch.float16)
return model
def get_tokenizer(model_name_or_path: str, max_input_len: int) -> PreTrainedTokenizer:
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, model_max_length=max_input_len,
padding_side="right", use_fast=False)
return tokenizer
def get_llm_inference_model(base_model_name_or_path: str, lora_model_name_or_path: str, load_in_8bit: bool,
device_map) -> PeftModel:
cpu = True if not torch.cuda.is_available() else False
model = get_model(base_model_name_or_path, load_in_8bit, device_map, cpu=cpu)
model = get_peft_model(model, lora_model_name_or_path=lora_model_name_or_path)
if not load_in_8bit:
model.half()
model.eval()
if torch.__version__ >= "2":
model = torch.compile(model)
return model
def generate_prompt(example):
return (
PROMPT_DICT["prompt_input"].format_map(example)
if example["input"]
else PROMPT_DICT["prompt_no_input"].format_map(example)
)
def infer(instruction: str, input_text: Optional[str] = None, temperature: float = 0.1, top_p: float = 0.95,
max_new_tokens: int = 512, early_stopping: bool = True, do_sample: bool = True,
repetition_penalty: float = 2.5) -> str:
prompt = generate_prompt({"instruction": instruction, "input": input_text})
tokenized_inputs = tokenizer(prompt, return_tensors="pt")
device = "cuda" if torch.cuda.is_available() else "cpu"
input_ids = tokenized_inputs["input_ids"].to(device)
generation_config = GenerationConfig(temperature=temperature, top_p=top_p, do_sample=do_sample,
repetition_penalty=repetition_penalty, early_stopping=early_stopping)
with torch.inference_mode():
generation_output = model.generate(input_ids=input_ids, generation_config=generation_config,
return_dict_in_generate=True, max_new_tokens=max_new_tokens)
output = generation_output.sequences[0]
output = tokenizer.decode(output, skip_special_tokens=True)
return output.split("### Response:")[1].strip()
base_model_name_or_path = "bigscience/bloom-560m"
lora_model_name_or_path = "crayon-coe/alpaca-bloom-560m-en"
model = get_llm_inference_model(base_model_name_or_path, lora_model_name_or_path, True, "auto")
tokenizer = get_tokenizer(base_model_name_or_path, 512)
context = "Write a letter expressing your love for computers"
output = infer(context)
print(output)
# Output
# I am so grateful to have been able access this wonderful computer system and its amazing features, which I can now use daily with ease.
#
# My heartfelt thanks go out in advance of all my friends who are using it as well.
# Thank you again!
```
# Training Parameters
```json
{
"max_input_len": 512,
"load_in_8bit": True,
"model_name_or_path": "bigscience/bloom-560m",
"device_map": "auto",
"bias": "none",
"lora_dropout": 0.05,
"lora_alpha": 32,
"target_modules": ["query_key_value"],
"task_type": "CAUSAL_LM",
"lora_r": 16,
"pad_to_multiple_of": 8,
"num_train_epochs": 3,
"learning_rate": 0.0003,
"gradient_accumulation_steps": 16,
"per_device_train_batch_size": 8,
"val_set_size": 500,
"save_steps": 200,
"eval_steps": 200,
"evaluation_strategy": "steps",
"save_strategy": "steps"
}
```
Note: If failing, you might need to add *offload_folder="some folder name"* when getting the PeftModel.
# Training Code
```python
# coding=utf-8
# Code 99.99% copied and adapted from:
# https://github.com/bofenghuang/vigogne
# https://colab.research.google.com/drive/1jCkpikz0J2o20FBQmYmAGdiKmJGOMo-o?usp=sharing#scrollTo=DpYr24pR8T_0
import os
import sys
from dataclasses import dataclass
from typing import Dict, List, Optional, Sequence
import bitsandbytes as bnb
import fire
import torch
import transformers
from datasets import load_dataset
from peft import LoraConfig, TaskType, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer
IGNORE_INDEX = -100
DEFAULT_PAD_TOKEN = "[PAD]"
PROMPT_DICT = {
"prompt_input": (
"Below is a^n instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
),
"prompt_no_input": (
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response:\n"
),
}
def generate_prompt(example):
return (
PROMPT_DICT["prompt_input"].format_map(example)
if example["input"]
else PROMPT_DICT["prompt_no_input"].format_map(example)
)
# Modified from: https://github.com/bofenghuang/stanford_alpaca/blob/eb5b171d9b103a12a8e14e0edca9cbc45fe1d512/train.py#L166-L182
# Almost same to transformers.DataCollatorForSeq2Seq
@dataclass
class DataCollatorForSupervisedDataset(object):
"""Collate examples for supervised fine-tuning."""
tokenizer: transformers.PreTrainedTokenizer
pad_to_multiple_of: Optional[int] = None
def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
# dtype = torch.long
# input_ids, labels = tuple([torch.LongTensor(instance[key]) for instance in instances] for key in ("input_ids", "labels"))
input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels"))
if self.pad_to_multiple_of is not None:
max_length_index, max_length = max(enumerate([len(input_ids_) for input_ids_ in input_ids]),
key=lambda x: x[1])
# int(math.ceil
n_padding = ((max_length // self.pad_to_multiple_of) + 1) * self.pad_to_multiple_of - max_length
# Pad the longest example to pad_to_multiple_of * N
input_ids[max_length_index].extend([self.tokenizer.pad_token_id] * n_padding)
labels[max_length_index].extend([IGNORE_INDEX] * n_padding)
input_ids = [torch.LongTensor(input_ids_) for input_ids_ in input_ids]
labels = [torch.LongTensor(labels_) for labels_ in labels]
input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=True,
padding_value=self.tokenizer.pad_token_id)
labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
return dict(input_ids=input_ids, labels=labels, attention_mask=input_ids.ne(self.tokenizer.pad_token_id))
def train(model_name_or_path: str, output_dir: str, data_path: str, val_set_size: int = 500,
model_max_length: int = 512, lora_r: int = 16, lora_alpha: int = 32, lora_dropout: float = 0.05,
target_modules: List[str] = ["query_key_value"], num_train_epochs: int = 3, learning_rate: float = 0.0001,
per_device_train_batch_size: int = 8, gradient_accumulation_steps: int = 16, **kwargs):
device_map = "auto"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, load_in_8bit=True, device_map=device_map)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, model_max_length=model_max_length,
padding_side="right", use_fast=False)
model = prepare_model_for_int8_training(model)
lora_config = LoraConfig(r=lora_r, lora_alpha=lora_alpha, target_modules=target_modules, lora_dropout=lora_dropout,
bias="none", task_type=TaskType.CAUSAL_LM)
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
# Load data
data = load_dataset("json", data_files=data_path)
def preprocess_function(example):
# Format prompt
user_prompt = generate_prompt(example)
# Get prompt length for masking
len_user_prompt_tokens = len(tokenizer(user_prompt, truncation=True)["input_ids"])
input_ids = tokenizer(user_prompt + example["output"] + tokenizer.eos_token, truncation=True)["input_ids"]
labels = [IGNORE_INDEX] * len_user_prompt_tokens + input_ids[len_user_prompt_tokens:]
return {"input_ids": input_ids, "labels": labels}
if val_set_size > 0:
train_val = data["train"].train_test_split(test_size=val_set_size, shuffle=True, seed=42)
train_data = train_val["train"].shuffle().map(preprocess_function, remove_columns=data["train"].column_names)
val_data = train_val["test"].map(preprocess_function, remove_columns=data["train"].column_names)
else:
train_data = data["train"].shuffle().map(preprocess_function, remove_columns=data["train"].column_names)
val_data = None
trainer = transformers.Trainer(
model=model,
train_dataset=train_data,
eval_dataset=val_data,
args=transformers.TrainingArguments(
per_device_train_batch_size=per_device_train_batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
num_train_epochs=num_train_epochs,
learning_rate=learning_rate,
fp16=True,
output_dir=output_dir,
load_best_model_at_end=True if val_set_size > 0 else False,
**kwargs,
),
data_collator=DataCollatorForSupervisedDataset(tokenizer=tokenizer, pad_to_multiple_of=8),
)
print(trainer.args)
# Silence the warnings. Please re-enable for inference!
model.config.use_cache = False
old_state_dict = model.state_dict
model.state_dict = (lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())).__get__(model,
type(model))
if torch.__version__ >= "2" and sys.platform != "win32":
model = torch.compile(model)
trainer.train()
model.save_pretrained(output_dir)
if __name__ == "__main__":
fire.Fire(train)
``` |