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on
Zero
Running
on
Zero
File size: 2,234 Bytes
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from ..models.model_manager import ModelManager
import torch
def tokenize_long_prompt(tokenizer, prompt, max_length=None):
# Get model_max_length from self.tokenizer
length = tokenizer.model_max_length if max_length is None else max_length
# To avoid the warning. set self.tokenizer.model_max_length to +oo.
tokenizer.model_max_length = 99999999
# Tokenize it!
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
# Determine the real length.
max_length = (input_ids.shape[1] + length - 1) // length * length
# Restore tokenizer.model_max_length
tokenizer.model_max_length = length
# Tokenize it again with fixed length.
input_ids = tokenizer(
prompt,
return_tensors="pt",
padding="max_length",
max_length=max_length,
truncation=True
).input_ids
# Reshape input_ids to fit the text encoder.
num_sentence = input_ids.shape[1] // length
input_ids = input_ids.reshape((num_sentence, length))
return input_ids
class BasePrompter:
def __init__(self):
self.refiners = []
self.extenders = []
def load_prompt_refiners(self, model_manager: ModelManager, refiner_classes=[]):
for refiner_class in refiner_classes:
refiner = refiner_class.from_model_manager(model_manager)
self.refiners.append(refiner)
def load_prompt_extenders(self,model_manager:ModelManager,extender_classes=[]):
for extender_class in extender_classes:
extender = extender_class.from_model_manager(model_manager)
self.extenders.append(extender)
@torch.no_grad()
def process_prompt(self, prompt, positive=True):
if isinstance(prompt, list):
prompt = [self.process_prompt(prompt_, positive=positive) for prompt_ in prompt]
else:
for refiner in self.refiners:
prompt = refiner(prompt, positive=positive)
return prompt
@torch.no_grad()
def extend_prompt(self, prompt:str, positive=True):
extended_prompt = dict(prompt=prompt)
for extender in self.extenders:
extended_prompt = extender(extended_prompt)
return extended_prompt |