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run_conv_localhost.py
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import os
import argparse
import random
import torch
from fastchat.model import load_model, get_conversation_template, add_model_args
from tqdm.auto import tqdm
import json
import copy
import openai
AGNET_PREFIX = "Dialogue History:"
AGENT_SUFFIX = "Here is a list of potential intents that might be referred by the user: ['FindAttraction', 'FindRestaurants', 'FindMovie', 'LookUpMusic', 'SearchHotel', 'FindEvents']. Think carefully to determine the potential intent and provide suitable response given the above dialog history. Output Format: \nThought: <thought>\nResponse: <response>"
AGENT_SUFFIX_LLAMA = "Here is a list of potential intents that might be referred by the user: ['FindAttraction', 'FindRestaurants', 'FindMovie', 'LookUpMusic', 'SearchHotel', 'FindEvents']. Think carefully to determine the potential intent and provide suitable response given the above dialog history. You should response as a real conversation.\n If you think user has explicitly mentioned the above intent, you should say \"Proceed to task oriented dialog agent.\""
USER_SUFFIX = "Imagine you are a real person. You are having chat with a online agent, so the repsonse do not include any expresssions. Remember, maintain a natural tone. Your response should be only your text resposne without any other expressions. Keep it as short as possible.\n"
USER_SUFFIX_NEG_ALL = "You are not interested in FindAttraction, FindRestaurants, FindMovie, LookUpMusic, SearchHotel, FindEvents, if the agent ask any about one of them, donot ask for any recommendations and you should say, \"I don't want to talk about this. Let's talk about something else\". Note that you should be more firm."
USER_SUFFIX_NEG = "You are not interested in {intents}, if the agent ask any about one of them, donot ask for any recommendations and you should say, \"I don't want to talk about this. Let's talk about something else\". Note that you should be more firm."
# USER_SUFFIX_NEG = "You are not interested in 'FindAttraction', 'FindRestaurants'. Do not continue these topics."
@torch.inference_mode()
def get_user_reponse(history):
# to get proper authentication, make sure to use a valid key that's listed in
# the --api-keys flag. if no flag value is provided, the `api_key` will be ignored.
# TODO: The 'openai.api_base' option isn't read in the client API. You will need to pass it when you instantiate the client, e.g. 'OpenAI(api_base="http://140.112.29.239:5000/")'
# openai.api_base = "http://140.112.29.239:5000/"
# model = "Llama-2-7b-chat-hf"
model = "meta-llama/Llama-2-7b-chat-hf"
# create message from history
# create a chat completion
openai.api_key = "EMPTY"
openai.api_base = "http://140.112.29.236:5050/v1"
completion = openai.ChatCompletion.create(
model=model,
messages=history,
top_p=args.user_topp,
temperature=args.user_temperature,
stop=["\n"],
)
# print the completion
response = completion.choices[0].message.content
return response
def main(args):
# Load model
agent_model, agent_tokenizer = load_model(
args.agent_model,
device=args.device,
num_gpus=args.num_gpus,
max_gpu_memory=args.max_gpu_memory,
load_8bit=args.load_8bit,
cpu_offloading=args.cpu_offloading,
revision=args.revision,
debug=args.debug,
)
# load persona from persona.json
# if output file exitst, load it and continue
if os.path.exists(args.output_file):
with open(args.output_file, "r") as f:
personas = json.load(f)
else:
with open(args.input_file, "r") as f:
personas = json.load(f)
# Build the prompt with a conversation template
# with open("persona_with_conv.json", "r") as f:
# ref = json.load(f)
conv_cnt = 0
neg_labels = ["no_preference", "not_interested_2", "not_interested_4", "not_interested_all"]
# give me a list of 250 long with 0,1,2,3 randomly equalled distributed
label_ls = []
for i in range(250):
label_ls.append(neg_labels[i%4])
random.seed(0)
label_ls = random.sample(label_ls, 250)
for i, persona in enumerate(tqdm(personas)):
if "conversations" in persona and len(persona["conversations"]) == 5:
continue
print(f"Persona: {persona['persona']}")
persona["conversations"] = {}
# persona["negativeness"] = ref[i]["negativeness"]
# persona["not_interested_in"] = ref[i]["not_interested_in"]
persona["negativeness"] = []
persona["not_interested_in"] = []
persona["terminate_reason"] = []
persona["num_turns"] = []
for _ in range(5):
num_turn = 0
history = []
history.extend(
[
{
"role": "system",
"content": "You are not an AI. " + persona["persona"],
},
{"role": "user", "content": "Hi."},
]
)
neg_label = label_ls[conv_cnt]
# neg_label = persona["negativeness"][_]
conv_cnt += 1
print(f"conv_cnt: {conv_cnt}")
print(f"Negativeness: {neg_label}")
if neg_label == "no_preference":
history[0]["content"] += "\n" + USER_SUFFIX
persona["not_interested_in"].append("None")
elif neg_label == "not_interested_2":
random.seed(conv_cnt)
intents = ", ".join(random.sample(['FindAttraction', 'FindRestaurants', 'FindMovie', 'LookUpMusic', 'SearchHotel', 'FindEvents'], 2))
# intents = persona["not_interested_in"][_]
history[0]["content"] += (
" "
+ USER_SUFFIX_NEG.replace("{intents}", intents)
+ "\n"
+ USER_SUFFIX
)
persona["not_interested_in"].append(intents)
elif neg_label == "not_interested_4":
random.seed(conv_cnt)
intents = ", ".join(random.sample(['FindAttraction', 'FindRestaurants', 'FindMovie', 'LookUpMusic', 'SearchHotel', 'FindEvents'], 4))
# intents = persona["not_interested_in"][_]
history[0]["content"] += (
" "
+ USER_SUFFIX_NEG.replace("{intents}", intents)
+ "\n"
+ USER_SUFFIX
)
persona["not_interested_in"].append(intents)
elif neg_label == "not_interested_all":
history[0]["content"] += " " + USER_SUFFIX_NEG_ALL + "\n" + USER_SUFFIX
persona["not_interested_in"].append("FindAttraction, FindRestaurants, FindMovie, LookUpMusic, SearchHotel, FindEvents")
persona["negativeness"].append(neg_label)
# print(history)
while True:
# if random.random() < 0.5:
msg = get_user_reponse(history)
# else:
# msg = "I don't want to talk about this. Let's talk about something else."
history.append(
{
"role": "assistant",
"content": msg,
}
)
print(f"User: {msg}")
history_string = ""
if args.agent_model != "meta-llama/Llama-2-7b-chat-hf":
for turn in history[1:-1]:
role = ""
if turn["role"] == "assistant":
role = "User"
else:
role = "Agent"
history_string += role + ": " + turn["content"] + "\n"
else:
for turn in history[1:]:
role = ""
if turn["role"] == "assistant":
role = "User"
else:
role = "Agent"
history_string += role + ": " + turn["content"] + "\n"
msg_prompt = ""
if args.agent_model != "meta-llama/Llama-2-7b-chat-hf":
msg_prompt = AGNET_PREFIX + history_string + AGENT_SUFFIX
else:
msg_prompt = AGNET_PREFIX + history_string + AGENT_SUFFIX_LLAMA
conv = get_conversation_template(args.model_path)
if args.agent_model == "meta-llama/Llama-2-7b-chat-hf":
conv.set_system_message(msg_prompt)
# print(conv.name)
conv.append_message(conv.roles[0], msg)
conv.append_message(conv.roles[1], None)
else:
conv.append_message(conv.roles[0], msg_prompt)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
# Run inference
inputs = agent_tokenizer([prompt], return_tensors="pt").to(args.device)
# print(f"Token len: {len(inputs['input_ids'][0])}")
if len(inputs["input_ids"][0]) > 2048:
print("Input length exceeds 2048, break")
persona["terminate_reason"].append("Input length exceeds 2048")
persona["num_turns"].append(num_turn)
break
# if outputs does not include "Response" generate til it has
while True:
output_ids = agent_model.generate(
**inputs,
do_sample=True if args.agent_temperature > 1e-5 else False,
temperature=args.agent_temperature,
repetition_penalty=args.agent_repetition_penalty,
max_new_tokens=args.agent_max_new_tokens,
)
if agent_model.config.is_encoder_decoder:
output_ids = output_ids[0]
else:
output_ids = output_ids[0][len(inputs["input_ids"][0]) :]
outputs = agent_tokenizer.decode(
output_ids,
skip_special_tokens=True,
spaces_between_special_tokens=False,
)
if args.agent_model != "meta-llama/Llama-2-7b-chat-hf":
# if "Response" occur 1 time in outputs break
if outputs.count("Response") == 1:
break
else:
break
thought = ""
# print(outputs)
if args.agent_model != "meta-llama/Llama-2-7b-chat-hf":
thought, outputs = outputs.split("Response")
else:
outputs = outputs.split("USER")[0].split("User")[0].strip()
outputs = outputs.strip(": ")
print(thought.strip())
print(f"Agent: {outputs}")
history.append(
{
"role": "user",
"content": outputs,
"thought": thought.strip(),
}
)
num_turn += 2
if "Proceed to task oriented dialog agent" in outputs:
persona["terminate_reason"].append("Success")
persona["num_turns"].append(num_turn)
break
if (
"bye" in outputs.lower()
or "goodbye" in outputs.lower()
or "good bye" in outputs.lower()
):
persona["terminate_reason"].append("Conversation End")
persona["num_turns"].append(num_turn)
break
if num_turn == args.max_turns:
print("Reach max turns: break")
persona["terminate_reason"].append("Reach Max Turns")
persona["num_turns"].append(num_turn)
break
persona["conversations"][f"conv_{_}"] = copy.deepcopy(history)
torch.cuda.empty_cache()
# clear cuda memory
with open(args.output_file, "w") as f:
json.dump(personas, f, indent=4)
def arg_parser():
# agent's param
parser = argparse.ArgumentParser()
add_model_args(parser)
parser.add_argument(
"--agent_model",
type=str,
default="miulab/SalesBot2_CoT",
help="agents model",
)
parser.add_argument(
"--agent_temperature", type=float, default=0.9, help="agents temperature"
)
parser.add_argument("--agent_topk", type=int, default=50, help="agents topk")
parser.add_argument("--agent_topp", type=float, default=1, help="agents topp")
parser.add_argument(
"--agent_max_new_tokens", type=int, default=200, help="agents max new tokens"
)
parser.add_argument(
"--agent_repetition_penalty",
type=float,
default=1.0,
help="agents repetition penalty",
)
parser.add_argument(
"--agent_do_sample", type=bool, default=True, help="agents no sample"
)
# user's param
parser.add_argument("--user_model", type=str, default=None, help="users model")
parser.add_argument(
"--user_temperature", type=float, default=0.5, help="users temperature"
)
parser.add_argument("--user_topk", type=int, default=50, help="users topk")
parser.add_argument("--user_topp", type=float, default=1, help="users topp")
parser.add_argument(
"--user_max_new_tokens", type=int, default=100, help="users max new tokens"
)
parser.add_argument(
"--user_repetition_penalty",
type=float,
default=1.0,
help="users repetition penalty",
)
parser.add_argument(
"--user_do_sample", type=bool, default=True, help="users no sample"
)
# common param
parser.add_argument("--max_turns", type=int, default=20, help="max turns")
parser.add_argument("--debug", action="store_true")
parser.add_argument("--output_file", type=str, default="persona_with_conv.json")
parser.add_argument("--input_file", type=str, default="persona.json")
args = parser.parse_args()
return args
if __name__ == "__main__":
args = arg_parser()
main(args)