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基于MultiDevices库实现快速调用多个计算设备进行推理 #732

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110 changes: 110 additions & 0 deletions MultiDevices-web_demo.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,110 @@
from transformers import AutoModel, AutoTokenizer
import gradio as gr
import mdtex2html
import MultiDevices
tokenizer = AutoTokenizer.from_pretrained("THUDM/ChatGLM-6B", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/ChatGLM-6B", trust_remote_code=True).half()
MultiDevices.GPU_precision = 'int8'
MultiDevices.embeddings = 'cpu'
#默认为使用6G显存+16G内存,修改请参阅https://github.com/ChaimEvans/ChatGLM_MultiGPUCPU_eval根据显存合理配置显卡和CPU的负载大小。
MultiDevices.layers={
'cuda:0': '1-20',
'cpu':'21-28'
}
MultiDevices.final_layernorm = 'cpu'
model = MultiDevices.ConfigMultiDevices(model)
model = model.eval()

"""Override Chatbot.postprocess"""


def postprocess(self, y):
if y is None:
return []
for i, (message, response) in enumerate(y):
y[i] = (
None if message is None else mdtex2html.convert((message)),
None if response is None else mdtex2html.convert(response),
)
return y


gr.Chatbot.postprocess = postprocess


def parse_text(text):
"""copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
lines = text.split("\n")
lines = [line for line in lines if line != ""]
count = 0
for i, line in enumerate(lines):
if "```" in line:
count += 1
items = line.split('`')
if count % 2 == 1:
lines[i] = f'<pre><code class="language-{items[-1]}">'
else:
lines[i] = f'<br></code></pre>'
else:
if i > 0:
if count % 2 == 1:
line = line.replace("`", "\`")
line = line.replace("<", "&lt;")
line = line.replace(">", "&gt;")
line = line.replace(" ", "&nbsp;")
line = line.replace("*", "&ast;")
line = line.replace("_", "&lowbar;")
line = line.replace("-", "&#45;")
line = line.replace(".", "&#46;")
line = line.replace("!", "&#33;")
line = line.replace("(", "&#40;")
line = line.replace(")", "&#41;")
line = line.replace("$", "&#36;")
lines[i] = "<br>"+line
text = "".join(lines)
return text


def predict(input, chatbot, max_length, top_p, temperature, history):
chatbot.append((parse_text(input), ""))
for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p,
temperature=temperature):
chatbot[-1] = (parse_text(input), parse_text(response))

yield chatbot, history


def reset_user_input():
return gr.update(value='')


def reset_state():
return [], []


with gr.Blocks() as demo:
gr.HTML("""<h1 align="center">ChatGLM</h1>""")

chatbot = gr.Chatbot()
with gr.Row():
with gr.Column(scale=4):
with gr.Column(scale=12):
user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style(
container=False)
with gr.Column(min_width=32, scale=1):
submitBtn = gr.Button("Submit", variant="primary")
with gr.Column(scale=1):
emptyBtn = gr.Button("Clear History")
max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True)
temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="Temperature", interactive=True)

history = gr.State([])

submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history], [chatbot, history],
show_progress=True)
submitBtn.click(reset_user_input, [], [user_input])

emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)

demo.queue().launch(share=False, inbrowser=True)
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