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import os
import sys
import fire
import gradio as gr
import torch
import transformers
from peft import PeftModel
from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer, AutoModel, AutoTokenizer, AutoModelForCausalLM
from utils.callbacks import Iteratorize, Stream
from utils.prompter import Prompter
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
try:
if torch.backends.mps.is_available():
device = "mps"
except:
pass
def main(
load_8bit: bool = False,
base_model: str = "huggyllama/llama-7b",
lora_weights: str = "entity303/lawgpt-lora-7b-v2",
prompt_template: str = "", # The prompt template to use, will default to alpaca.
server_name: str = "0.0.0.0", # Allows to listen on all interfaces by providing '0.
share_gradio: bool = True,
):
base_model = base_model or os.environ.get("BASE_MODEL", "")
assert (
base_model
), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
prompter = Prompter(prompt_template)
tokenizer = LlamaTokenizer.from_pretrained(base_model)
if device == "cuda":
model = LlamaForCausalLM.from_pretrained(
base_model,
load_in_8bit=load_8bit,
torch_dtype=torch.float16,
device_map="auto",
)
try:
model = PeftModel.from_pretrained(
model,
lora_weights,
torch_dtype=torch.float16,
)
except:
print("*"*50, "\n Attention! No Lora Weights \n", "*"*50)
elif device == "mps":
model = LlamaForCausalLM.from_pretrained(
base_model,
device_map={"": device},
torch_dtype=torch.float16,
)
try:
model = PeftModel.from_pretrained(
model,
lora_weights,
device_map={"": device},
torch_dtype=torch.float16,
)
except:
print("*"*50, "\n Attention! No Lora Weights \n", "*"*50)
else:
model = LlamaForCausalLM.from_pretrained(
base_model, device_map={"": device}, low_cpu_mem_usage=True
)
try:
model = PeftModel.from_pretrained(
model,
lora_weights,
device_map={"": device},
)
except:
print("*"*50, "\n Attention! No Lora Weights \n", "*"*50)
# unwind broken decapoda-research config
model.config.pad_token_id = tokenizer.pad_token_id = 0 # unk
model.config.bos_token_id = 1
model.config.eos_token_id = 2
if not load_8bit:
model.half() # seems to fix bugs for some users.
model.eval()
if torch.__version__ >= "2" and sys.platform != "win32":
model = torch.compile(model)
def evaluate(
instruction,
# input=None,
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=4,
max_new_tokens=128,
stream_output=False,
**kwargs,
):
input=None
prompt = prompter.generate_prompt(instruction, input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
**kwargs,
)
generate_params = {
"input_ids": input_ids,
"generation_config": generation_config,
"return_dict_in_generate": True,
"output_scores": True,
"max_new_tokens": max_new_tokens,
}
if stream_output:
# Stream the reply 1 token at a time.
# This is based on the trick of using 'stopping_criteria' to create an iterator,
# from https://github.com/oobabooga/text-generation-webui/blob/ad37f396fc8bcbab90e11ecf17c56c97bfbd4a9c/modules/text_generation.py#L216-L243.
def generate_with_callback(callback=None, **kwargs):
kwargs.setdefault(
"stopping_criteria", transformers.StoppingCriteriaList()
)
kwargs["stopping_criteria"].append(
Stream(callback_func=callback)
)
with torch.no_grad():
model.generate(**kwargs)
def generate_with_streaming(**kwargs):
return Iteratorize(
generate_with_callback, kwargs, callback=None
)
with generate_with_streaming(**generate_params) as generator:
for output in generator:
# new_tokens = len(output) - len(input_ids[0])
decoded_output = tokenizer.decode(output)
if output[-1] in [tokenizer.eos_token_id]:
break
yield prompter.get_response(decoded_output)
print(decoded_output)
return # early return for stream_output
# Without streaming
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s)
print(output)
yield prompter.get_response(output)
gr.Interface(
fn=evaluate,
inputs=[
gr.components.Textbox(
lines=2,
label="Instruction",
placeholder="此处输入法律相关问题",
),
# gr.components.Textbox(lines=2, label="Input", placeholder="none"),
gr.components.Slider(
minimum=0, maximum=1, value=0.1, label="Temperature"
),
gr.components.Slider(
minimum=0, maximum=1, value=0.75, label="Top p"
),
gr.components.Slider(
minimum=0, maximum=100, step=1, value=40, label="Top k"
),
gr.components.Slider(
minimum=1, maximum=4, step=1, value=1, label="Beams"
),
gr.components.Slider(
minimum=1, maximum=2000, step=1, value=256, label="Max tokens"
),
gr.components.Checkbox(label="Stream output", value=True),
],
outputs=[
gr.components.Textbox(
lines=8,
label="Output",
)
],
title="🦙🌲 LaWGPT",
description="",
).queue().launch(server_name="0.0.0.0", share=share_gradio)
if __name__ == "__main__":
fire.Fire(main)
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