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import torch
from transformers import StoppingCriteria, StoppingCriteriaList
import copy
import json
import global_vars
from chats import pre, post
from pingpong import PingPong
from gens.batch_gen import get_output_batch
from chats.utils import build_prompts, text_stream, internet_search
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
stop_ids = [50278, 50279, 50277, 1, 0]
for stop_id in stop_ids:
if input_ids[0][-1] == stop_id:
return True
return False
def chat_stream(
idx, local_data, user_message, state,
global_context, ctx_num_lconv, ctx_sum_prompt,
res_temp, res_topp, res_topk, res_rpen, res_mnts, res_beams, res_cache, res_sample, res_eosid, res_padid,
sum_temp, sum_topp, sum_topk, sum_rpen, sum_mnts, sum_beams, sum_cache, sum_sample, sum_eosid, sum_padid,
internet_option, serper_api_key
):
res = [
state["ppmanager_type"].from_json(json.dumps(ppm))
for ppm in local_data
]
ppm = res[idx]
# add_ping returns a prompt structured in Alpaca form
ppm.add_pingpong(
PingPong(user_message, "")
)
prompt = build_prompts(ppm, global_context, ctx_num_lconv)
#######
if internet_option:
search_prompt = None
for tmp_prompt, uis in internet_search(ppm, serper_api_key, global_context, ctx_num_lconv):
search_prompt = tmp_prompt
yield "", uis, prompt, str(res)
# prepare text generating streamer & start generating
gen_kwargs, streamer = pre.build(
search_prompt if internet_option else prompt,
res_temp, res_topp, res_topk, res_rpen, res_mnts,
res_beams, res_cache, res_sample, res_eosid, res_padid,
return_token_type_ids=False
)
pre.start_gen(gen_kwargs)
# handling stream
for ppmanager, uis in text_stream(ppm, streamer):
yield "", uis, prompt, str(res)
ppm = post.strip_pong(ppm)
yield "", ppm.build_uis(), prompt, str(res) |