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from dataclasses import dataclass, field
from typing import Dict, Optional, Sequence
import logging
import os, sys
import copy
import torch
import transformers
from transformers import LlamaForCausalLM, LlamaTokenizer, TextStreamer
from torch.utils.data import Dataset
from transformers import Trainer
import torch
from rich.console import Console
from rich.table import Table
from datetime import datetime
from threading import Thread
sys.path.append(os.path.dirname(__file__))
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from utils.special_tok_llama2 import (
B_CODE,
E_CODE,
B_RESULT,
E_RESULT,
B_INST,
E_INST,
B_SYS,
E_SYS,
DEFAULT_PAD_TOKEN,
DEFAULT_BOS_TOKEN,
DEFAULT_EOS_TOKEN,
DEFAULT_UNK_TOKEN,
IGNORE_INDEX,
)
from finetuning.conversation_template import (
json_to_code_result_tok_temp,
msg_to_code_result_tok_temp,
)
import warnings
warnings.filterwarnings("ignore", category=UserWarning, module="transformers")
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
console = Console() # for pretty print
@dataclass
class ModelArguments:
model_name_or_path: Optional[str] = field(default="./output/llama-2-7b-chat-ci")
load_peft: Optional[bool] = field(default=False)
peft_model_name_or_path: Optional[str] = field(
default="./output/llama-2-7b-chat-ci"
)
def create_peft_config(model):
from peft import (
get_peft_model,
LoraConfig,
TaskType,
prepare_model_for_int8_training,
)
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
inference_mode=False,
r=8,
lora_alpha=32,
lora_dropout=0.05,
target_modules=["q_proj", "v_proj"],
)
# prepare int-8 model for training
model = prepare_model_for_int8_training(model)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
return model, peft_config
def build_model_from_hf_path(
hf_base_model_path: str = "./ckpt/llama-2-13b-chat",
load_peft: Optional[bool] = False,
peft_model_path: Optional[str] = None,
):
start_time = datetime.now()
# build tokenizer
console.log("[bold cyan]Building tokenizer...[/bold cyan]")
tokenizer = LlamaTokenizer.from_pretrained(
hf_base_model_path,
padding_side="right",
use_fast=False,
)
# Handle special tokens
console.log("[bold cyan]Handling special tokens...[/bold cyan]")
special_tokens_dict = dict()
if tokenizer.pad_token is None:
special_tokens_dict["pad_token"] = DEFAULT_PAD_TOKEN # 32000
if tokenizer.eos_token is None:
special_tokens_dict["eos_token"] = DEFAULT_EOS_TOKEN # 2
if tokenizer.bos_token is None:
special_tokens_dict["bos_token"] = DEFAULT_BOS_TOKEN # 1
if tokenizer.unk_token is None:
special_tokens_dict["unk_token"] = DEFAULT_UNK_TOKEN
tokenizer.add_special_tokens(special_tokens_dict)
tokenizer.add_tokens(
[B_CODE, B_RESULT, E_RESULT, B_INST, E_INST, B_SYS, E_SYS],
special_tokens=True,
)
# build model
console.log("[bold cyan]Building model...[/bold cyan]")
model = LlamaForCausalLM.from_pretrained(
hf_base_model_path,
load_in_4bit=True,
device_map="auto",
)
model.resize_token_embeddings(len(tokenizer))
if load_peft and (peft_model_path is not None):
from peft import PeftModel
model = PeftModel.from_pretrained(model, peft_model_path)
console.log("[bold green]Peft Model Loaded[/bold green]")
end_time = datetime.now()
elapsed_time = end_time - start_time
# Log time performance
table = Table(title="Time Performance")
table.add_column("Task", style="cyan")
table.add_column("Time Taken", justify="right")
table.add_row("Loading model", str(elapsed_time))
console.print(table)
console.log("[bold green]Model Loaded[/bold green]")
return {"tokenizer": tokenizer, "model": model}
@torch.inference_mode()
def inference(
user_input="What is 100th fibo num?",
max_new_tokens=512,
do_sample: bool = True,
use_cache: bool = True,
top_p: float = 1.0,
temperature: float = 0.1,
top_k: int = 50,
repetition_penalty: float = 1.0,
):
parser = transformers.HfArgumentParser(ModelArguments)
model_args = parser.parse_args_into_dataclasses()[0]
model_dict = build_model_from_hf_path(
hf_base_model_path=model_args.model_name_or_path,
load_peft=model_args.load_peft,
peft_model_path=model_args.peft_model_name_or_path,
)
model = model_dict["model"]
tokenizer = model_dict["tokenizer"]
streamer = TextStreamer(tokenizer, skip_prompt=True)
# peft
# create peft config
model.eval()
user_prompt = msg_to_code_result_tok_temp(
[{"role": "user", "content": f"{user_input}"}]
)
# Printing user's content in blue
console.print("\n" + "-" * 20, style="#808080")
console.print(f"###User : {user_input}\n", style="blue")
prompt = f"{user_prompt}\n###Assistant :"
# prompt = f"{user_input}\n### Assistant : Here is python code to get the 55th fibonacci number {B_CODE}\n"
inputs = tokenizer([prompt], return_tensors="pt")
generated_text = model.generate(
**inputs,
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=do_sample,
top_p=top_p,
temperature=temperature,
use_cache=use_cache,
top_k=top_k,
repetition_penalty=repetition_penalty,
)
return generated_text
if __name__ == "__main__":
inference(user_input="what is sin(44)?")
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