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Optimize gradio-based demo

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Y.W. Fang 2024-01-31 15:56:26 +08:00
parent e8fb7abea2
commit 9c0fb61c32
1 changed files with 17 additions and 14 deletions
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31
demo/gradio_based_demo.py
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@ -18,7 +18,7 @@ tokenizer = AutoTokenizer.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)
def hf_gen(query: str, top_p: float, temperature: float, max_dec_len: int): def hf_gen(dialog: str, top_p: float, temperature: float, max_dec_len: int):
"""generate model output with huggingface api """generate model output with huggingface api
Args: Args:
@ -30,10 +30,11 @@ def hf_gen(query: str, top_p: float, temperature: float, max_dec_len: int):
Yields: Yields:
str: real-time generation results of hf model str: real-time generation results of hf model
""" """
inputs = tokenizer([query], return_tensors="pt").to("cuda") inputs = tokenizer.apply_chat_template(dialog, tokenize=False, add_generation_prompt=False)
enc = tokenizer(inputs, return_tensors="pt").to("cuda")
streamer = TextIteratorStreamer(tokenizer) streamer = TextIteratorStreamer(tokenizer)
generation_kwargs = dict( generation_kwargs = dict(
inputs, enc,
do_sample=True, do_sample=True,
top_p=top_p, top_p=top_p,
temperature=temperature, temperature=temperature,
@ -45,7 +46,7 @@ def hf_gen(query: str, top_p: float, temperature: float, max_dec_len: int):
answer = "" answer = ""
for new_text in streamer: for new_text in streamer:
answer += new_text answer += new_text
yield answer yield answer[4 + len(inputs):]
def generate(chat_history: List, query: str, top_p: float, temperature: float, max_dec_len: int): def generate(chat_history: List, query: str, top_p: float, temperature: float, max_dec_len: int):
@ -63,14 +64,15 @@ def generate(chat_history: List, query: str, top_p: float, temperature: float, m
""" """
assert query != "", "Input must not be empty!!!" assert query != "", "Input must not be empty!!!"
# apply chat template # apply chat template
model_input = "" model_input = []
for q, a in chat_history: for q, a in chat_history:
model_input += "<用户>" + q + "<AI>" + a model_input.append({"role": "user", "content": q})
model_input += "<用户>" + query + "<AI>" model_input.append({"role": "assistant", "content": a})
model_input.append({"role": "user", "content": query})
# yield model generation # yield model generation
chat_history.append([query, ""]) chat_history.append([query, ""])
for answer in hf_gen(model_input, top_p, temperature, max_dec_len): for answer in hf_gen(model_input, top_p, temperature, max_dec_len):
chat_history[-1][1] = answer[4 + len(model_input):].strip("</s>") chat_history[-1][1] = answer.strip("</s>")
yield gr.update(value=""), chat_history yield gr.update(value=""), chat_history
@ -88,13 +90,14 @@ def regenerate(chat_history: List, top_p: float, temperature: float, max_dec_len
""" """
assert len(chat_history) >= 1, "History is empty. Nothing to regenerate!!" assert len(chat_history) >= 1, "History is empty. Nothing to regenerate!!"
# apply chat template # apply chat template
model_input = "" model_input = []
for q, a in chat_history[:-1]: for q, a in chat_history[:-1]:
model_input += "<用户>" + q + "<AI>" + a model_input.append({"role": "user", "content": q})
model_input += "<用户>" + chat_history[-1][0] + "<AI>" model_input.append({"role": "assistant", "content": a})
model_input.append({"role": "user", "content": chat_history[-1][0]})
# yield model generation # yield model generation
for answer in hf_gen(model_input, top_p, temperature, max_dec_len): for answer in hf_gen(model_input, top_p, temperature, max_dec_len):
chat_history[-1][1] = answer[4 + len(model_input):].strip("</s>") chat_history[-1][1] = answer.strip("</s>")
yield gr.update(value=""), chat_history yield gr.update(value=""), chat_history
@ -125,8 +128,8 @@ with gr.Blocks(theme="soft") as demo:
with gr.Row(): with gr.Row():
with gr.Column(scale=1): with gr.Column(scale=1):
top_p = gr.Slider(0, 1, value=0.5, step=0.1, label="top_p") top_p = gr.Slider(0, 1, value=0.8, step=0.1, label="top_p")
temperature = gr.Slider(0.1, 2.0, value=1.0, step=0.1, label="temperature") temperature = gr.Slider(0.1, 2.0, value=0.8, step=0.1, label="temperature")
max_dec_len = gr.Slider(1, 1024, value=1024, step=1, label="max_dec_len") max_dec_len = gr.Slider(1, 1024, value=1024, step=1, label="max_dec_len")
with gr.Column(scale=5): with gr.Column(scale=5):
chatbot = gr.Chatbot(bubble_full_width=False, height=400) chatbot = gr.Chatbot(bubble_full_width=False, height=400)