mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-22 08:07:56 +01:00
83 lines
2.4 KiB
Python
83 lines
2.4 KiB
Python
import multiprocessing
|
|
|
|
import llamacpp
|
|
|
|
from modules import shared
|
|
from modules.callbacks import Iteratorize
|
|
|
|
|
|
class LlamaCppTokenizer:
|
|
"""A thin wrapper over the llamacpp tokenizer"""
|
|
def __init__(self, model: llamacpp.LlamaInference):
|
|
self._tokenizer = model.get_tokenizer()
|
|
self.eos_token_id = 2
|
|
self.bos_token_id = 0
|
|
|
|
@classmethod
|
|
def from_model(cls, model: llamacpp.LlamaInference):
|
|
return cls(model)
|
|
|
|
def encode(self, prompt: str):
|
|
return self._tokenizer.tokenize(prompt)
|
|
|
|
def decode(self, ids):
|
|
return self._tokenizer.detokenize(ids)
|
|
|
|
|
|
class LlamaCppModel:
|
|
def __init__(self):
|
|
self.initialized = False
|
|
|
|
@classmethod
|
|
def from_pretrained(self, path):
|
|
params = llamacpp.InferenceParams()
|
|
params.path_model = str(path)
|
|
params.n_threads = shared.args.threads or multiprocessing.cpu_count() // 2
|
|
|
|
_model = llamacpp.LlamaInference(params)
|
|
|
|
result = self()
|
|
result.model = _model
|
|
result.params = params
|
|
|
|
tokenizer = LlamaCppTokenizer.from_model(_model)
|
|
return result, tokenizer
|
|
|
|
def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, repetition_penalty=1, callback=None):
|
|
params = self.params
|
|
params.n_predict = token_count
|
|
params.top_p = top_p
|
|
params.top_k = top_k
|
|
params.temp = temperature
|
|
params.repeat_penalty = repetition_penalty
|
|
# params.repeat_last_n = repeat_last_n
|
|
|
|
# self.model.params = params
|
|
self.model.add_bos()
|
|
self.model.update_input(context)
|
|
|
|
output = ""
|
|
is_end_of_text = False
|
|
ctr = 0
|
|
while ctr < token_count and not is_end_of_text:
|
|
if self.model.has_unconsumed_input():
|
|
self.model.ingest_all_pending_input()
|
|
else:
|
|
self.model.eval()
|
|
token = self.model.sample()
|
|
text = self.model.token_to_str(token)
|
|
output += text
|
|
is_end_of_text = token == self.model.token_eos()
|
|
if callback:
|
|
callback(text)
|
|
ctr += 1
|
|
|
|
return output
|
|
|
|
def generate_with_streaming(self, **kwargs):
|
|
with Iteratorize(self.generate, kwargs, callback=None) as generator:
|
|
reply = ''
|
|
for token in generator:
|
|
reply += token
|
|
yield reply
|