DB-GPT/dbgpt/model/llm_out/gorilla_llm.py
FangYin Cheng cd725db1fb
refactor: The first refactored version for sdk release (#907)
Co-authored-by: chengfangyin2 <chengfangyin3@jd.com>
2023-12-08 14:45:59 +08:00

63 lines
2.0 KiB
Python

import torch
@torch.inference_mode()
def generate_stream(
model, tokenizer, params, device, context_len=42048, stream_interval=2
):
"""Fork from https://github.com/ShishirPatil/gorilla/blob/main/inference/serve/gorilla_cli.py"""
prompt = params["prompt"]
l_prompt = len(prompt)
max_new_tokens = int(params.get("max_new_tokens", 1024))
stop_str = params.get("stop", None)
input_ids = tokenizer(prompt).input_ids
output_ids = list(input_ids)
input_echo_len = len(input_ids)
max_src_len = context_len - max_new_tokens - 8
input_ids = input_ids[-max_src_len:]
past_key_values = out = None
for i in range(max_new_tokens):
if i == 0:
out = model(torch.as_tensor([input_ids], device=device), use_cache=True)
logits = out.logits
past_key_values = out.past_key_values
else:
out = model(
input_ids=torch.as_tensor([[token]], device=device),
use_cache=True,
past_key_values=past_key_values,
)
logits = out.logits
past_key_values = out.past_key_values
last_token_logits = logits[0][-1]
probs = torch.softmax(last_token_logits, dim=-1)
token = int(torch.multinomial(probs, num_samples=1))
output_ids.append(token)
if token == tokenizer.eos_token_id:
stopped = True
else:
stopped = False
if i % stream_interval == 0 or i == max_new_tokens - 1 or stopped:
tmp_output_ids = output_ids[input_echo_len:]
output = tokenizer.decode(
tmp_output_ids,
skip_special_tokens=True,
spaces_between_special_tokens=False,
)
pos = output.rfind(stop_str, l_prompt)
if pos != -1:
output = output[:pos]
stopped = True
yield output
if stopped:
break
del past_key_values