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"""
Inference code for ChatGLM.
Adapted from https://huggingface.co/THUDM/chatglm-6b/blob/main/modeling_chatglm.py.
"""
import re
import torch
from transformers.generation.logits_process import LogitsProcessor
class InvalidScoreLogitsProcessor(LogitsProcessor):
def __call__(
self, input_ids: torch.LongTensor, scores: torch.FloatTensor
) -> torch.FloatTensor:
if torch.isnan(scores).any() or torch.isinf(scores).any():
scores.zero_()
scores[..., 5] = 5e4
return scores
invalid_score_processor = InvalidScoreLogitsProcessor()
def process_response(response):
response = response.strip()
response = response.replace("[[训练时间]]", "2023年")
punkts = [
[",", "，"],
["!", "！"],
[":", "："],
[";", "；"],
["\?", "？"],
]
for item in punkts:
response = re.sub(r"([\u4e00-\u9fff])%s" % item[0], r"\1%s" % item[1], response)
response = re.sub(r"%s([\u4e00-\u9fff])" % item[0], r"%s\1" % item[1], response)
return response
def recover_message_list(prompt):
role_token_pattern = "|".join(
[re.escape(r) for r in ["<|system|>", "<|user|>", "<|assistant|>"]]
)
role = None
last_end_idx = -1
message_list = []
for match in re.finditer(role_token_pattern, prompt):
if role:
messge = {}
if role == "<|system|>":
messge["role"] = "system"
elif role == "<|user|>":
messge["role"] = "user"
else:
messge["role"] = "assistant"
messge["content"] = prompt[last_end_idx + 1 : match.start()]
message_list.append(messge)
role = prompt[match.start() : match.end()]
last_end_idx = match.end()
return message_list
@torch.inference_mode()
def generate_stream_chatglm(
model,
tokenizer,
params,
device,
context_len=2048,
stream_interval=2,
judge_sent_end=False,
):
prompt = params["prompt"]
temperature = float(params.get("temperature", 1.0))
repetition_penalty = float(params.get("repetition_penalty", 1.0))
top_p = float(params.get("top_p", 1.0))
max_new_tokens = int(params.get("max_new_tokens", 256))
echo = params.get("echo", True)
model_type = str(type(model)).lower()
if "peft" in model_type:
model_type = str(type(model.base_model.model)).lower()
if "chatglm3" in model_type:
message_list = recover_message_list(prompt)
inputs = tokenizer.build_chat_input(
query=message_list[-1]["content"], history=message_list[:-1], role="user"
).to(model.device)
else:
inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
input_echo_len = len(inputs["input_ids"][0])
gen_kwargs = {
"max_length": max_new_tokens + input_echo_len,
"do_sample": True if temperature > 1e-5 else False,
"top_p": top_p,
"repetition_penalty": repetition_penalty,
"logits_processor": [invalid_score_processor],
}
if temperature > 1e-5:
gen_kwargs["temperature"] = temperature
total_len = 0
for total_ids in model.stream_generate(**inputs, **gen_kwargs):
total_ids = total_ids.tolist()[0]
total_len = len(total_ids)
if echo:
output_ids = total_ids
else:
output_ids = total_ids[input_echo_len:]
response = tokenizer.decode(output_ids)
response = process_response(response)
yield {
"text": response,
"usage": {
"prompt_tokens": input_echo_len,
"completion_tokens": total_len - input_echo_len,
"total_tokens": total_len,
},
"finish_reason": None,
}
# TODO: ChatGLM stop when it reach max length
# Only last stream result contains finish_reason, we set finish_reason as stop
ret = {
"text": response,
"usage": {
"prompt_tokens": input_echo_len,
"completion_tokens": total_len - input_echo_len,
"total_tokens": total_len,
},
"finish_reason": "stop",
}
yield ret
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