Spaces:
Build error
Build error
File size: 9,815 Bytes
3860729 c73d190 3860729 fddc7fb 54b1b8a 3860729 fddc7fb a69b127 fddc7fb a69b127 3860729 54b1b8a 3860729 54b1b8a 3860729 54b1b8a 3860729 54b1b8a 3860729 a69b127 3860729 54b1b8a 3860729 54b1b8a 12a5ff3 3860729 54b1b8a 3860729 54b1b8a 3860729 54b1b8a 3860729 54b1b8a 3860729 54b1b8a 990ef6d 54b1b8a a69b127 54b1b8a 3860729 54b1b8a a69b127 54b1b8a 3860729 54b1b8a c73d190 a69b127 a37d279 c73d190 a69b127 a37d279 c73d190 3860729 54b1b8a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 |
import os
import re
import numpy as np
import torch
import tiktoken
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
TextStreamer,
)
from tqdm import tqdm
def get_template(model_name):
model_name = model_name.lower()
if "llama" in model_name:
return "llama3"
if "internlm" in model_name:
return "intern2"
if "glm" in model_name:
return "glm4"
return "chatml"
class OpenAITokenizer:
def __init__(self, model_name):
self.model_name = model_name
self.encoding = tiktoken.get_encoding(model_name)
def __call__(self, text, return_tensors="pt"):
return {"input_ids": self.encoding.encode(text)}
def load_tokenizer(model_name):
if "gpt" in model_name:
return OpenAITokenizer("cl100k_base")
return AutoTokenizer.from_pretrained(
model_name, trust_remote_code=True, padding_side="left"
)
def load_model(
model_name,
dtype=torch.bfloat16,
load_in_4bit=False,
adapter_name_or_path=None,
using_llama_factory=False,
):
print(f"loading model: {model_name} with adapter: {adapter_name_or_path}")
if using_llama_factory:
from llamafactory.chat import ChatModel
template = get_template(model_name)
args = dict(
model_name_or_path=model_name,
adapter_name_or_path=adapter_name_or_path, # load the saved LoRA adapters
template=template, # same to the one in training
finetuning_type="lora", # same to the one in training
quantization_bit=4 if load_in_4bit else None, # load 4-bit quantized model
)
chat_model = ChatModel(args)
if os.getenv("RESIZE_TOKEN_EMBEDDINGS") == "true":
chat_model.engine.model.resize_token_embeddings(
len(chat_model.engine.tokenizer), pad_to_multiple_of=32
)
return chat_model.engine.model, chat_model.engine.tokenizer
tokenizer = load_tokenizer(model_name)
bnb_config = BitsAndBytesConfig(
load_in_4bit=load_in_4bit,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=False,
bnb_4bit_compute_dtype=dtype,
)
model = (
AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
torch_dtype=dtype,
trust_remote_code=True,
device_map="auto",
)
if load_in_4bit
else AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=dtype,
trust_remote_code=True,
device_map="auto",
)
)
if adapter_name_or_path:
adapter_name = model.load_adapter(adapter_name_or_path)
model.active_adapters = adapter_name
if not tokenizer.pad_token:
print("Adding pad token to tokenizer for model: ", model_name)
tokenizer.add_special_tokens({"pad_token": "<pad>"})
model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=32)
model.generation_config.pad_token_id = tokenizer.pad_token_id
return model, tokenizer
def check_gpu():
# torch.cuda.is_available() checks and returns a Boolean True if a GPU is available, else it'll return False
is_cuda = torch.cuda.is_available()
# If we have a GPU available, we'll set our device to GPU. We'll use this device variable later in our code.
if is_cuda:
device = torch.device("cuda")
print("CUDA is available, we have found ", torch.cuda.device_count(), " GPU(s)")
print(torch.cuda.get_device_name(0))
print("CUDA version: " + torch.version.cuda)
elif torch.backends.mps.is_available():
device = torch.device("mps")
print("MPS is available")
else:
device = torch.device("cpu")
print("GPU/MPS not available, CPU used")
return device
def test_model(model, tokenizer, prompt, device="cuda"):
inputs = tokenizer(
[prompt],
return_tensors="pt",
).to(device)
text_streamer = TextStreamer(tokenizer)
_ = model.generate(
**inputs, max_new_tokens=2048, streamer=text_streamer, use_cache=True
)
def extract_answer(text, debug=False):
if text:
# Remove the begin and end tokens
text = re.sub(
r".*?(assistant|\[/INST\]).+?\b",
"",
text,
flags=re.DOTALL | re.MULTILINE,
)
if debug:
print("--------\nstep 1:", text)
text = re.sub(r"<.+?>.*", "", text, flags=re.DOTALL | re.MULTILINE)
if debug:
print("--------\nstep 2:", text)
text = re.sub(
r".*?end_header_id\|>\n\n", "", text, flags=re.DOTALL | re.MULTILINE
)
if debug:
print("--------\nstep 3:", text)
text = text.split("。")[0].strip()
if debug:
print("--------\nstep 4:", text)
text = re.sub(
r"^Response:.+?\b",
"",
text,
flags=re.DOTALL | re.MULTILINE,
)
if debug:
print("--------\nstep 5:", text)
return text
def eval_model(
model,
tokenizer,
eval_dataset,
device="cuda",
max_new_tokens=2048,
repetition_penalty=1.0,
do_sample=True,
top_p=0.95,
top_k=0, # select from top 0 tokens (because zero, relies on top_p)
temperature=0.01,
batch_size=1,
):
total = len(eval_dataset)
predictions = []
model.eval()
with torch.no_grad():
for i in tqdm(range(0, total, batch_size)): # Iterate in batches
batch_end = min(i + batch_size, total) # Ensure not to exceed dataset
batch_prompts = eval_dataset["prompt"][i:batch_end]
inputs = tokenizer(
batch_prompts,
return_tensors="pt",
padding=True, # Ensure all inputs in the batch have the same length
).to(device)
outputs = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=do_sample,
temperature=temperature,
top_p=top_p,
top_k=top_k,
repetition_penalty=repetition_penalty,
use_cache=False,
)
outputs = outputs[:, inputs["input_ids"].shape[1] :]
decoded_output = tokenizer.batch_decode(
outputs, skip_special_tokens=True
) # Skip special tokens for clean output
if i == 0:
print("Batch output:", decoded_output)
predictions.extend(decoded_output)
return predictions
def evaluate_model_with_repetition_penalty(
model,
tokenizer,
model_name,
dataset,
on_repetition_penalty_step_completed,
start_repetition_penalty=1.0,
end_repetition_penalty=1.3,
step_repetition_penalty=0.02,
batch_size=1,
max_new_tokens=2048,
device="cuda",
):
print(f"Evaluating model: {model_name} on {device}")
for repetition_penalty in np.arange(
start_repetition_penalty,
end_repetition_penalty + step_repetition_penalty / 2,
step_repetition_penalty,
):
# round to 2 decimal places
repetition_penalty = round(repetition_penalty, 2)
print(f"*** Evaluating with repetition_penalty: {repetition_penalty}")
predictions = eval_model(
model,
tokenizer,
dataset,
device=device,
repetition_penalty=repetition_penalty,
batch_size=batch_size,
max_new_tokens=max_new_tokens,
)
model_name_with_rp = f"{model_name}/rpp-{repetition_penalty:.2f}"
try:
on_repetition_penalty_step_completed(
model_name_with_rp,
predictions,
)
except Exception as e:
print(e)
def save_model(
model,
tokenizer,
include_gguf=True,
include_merged=True,
publish=True,
):
try:
token = os.getenv("HF_TOKEN") or None
model_name = os.getenv("MODEL_NAME")
save_method = "lora"
quantization_method = "q5_k_m"
model_names = get_model_names(
model_name, save_method=save_method, quantization_method=quantization_method
)
model.save_pretrained(model_names["local"])
tokenizer.save_pretrained(model_names["local"])
if publish:
model.push_to_hub(
model_names["hub"],
token=token,
)
tokenizer.push_to_hub(
model_names["hub"],
token=token,
)
if include_merged:
model.save_pretrained_merged(
model_names["local"] + "-merged", tokenizer, save_method=save_method
)
if publish:
model.push_to_hub_merged(
model_names["hub"] + "-merged",
tokenizer,
save_method="lora",
token="",
)
if include_gguf:
model.save_pretrained_gguf(
model_names["local-gguf"],
tokenizer,
quantization_method=quantization_method,
)
if publish:
model.push_to_hub_gguf(
model_names["hub-gguf"],
tokenizer,
quantization_method=quantization_method,
token=token,
)
except Exception as e:
print(e)
def print_row_details(df, indices=[0]):
for index in indices:
for col in df.columns:
print("-" * 50)
print(f"{col}: {df[col].iloc[index]}")
|