rixty
/
KoboldAI-Client
mirror of https://github.com/KoboldAI/KoboldAI-Client.git
1
1
Fork 0
Code Issues Projects Releases Wiki Activity

Merge pull request #123 from VE-FORBRYDERNE/tokenizer 

Fix OPT tokenization problems
This commit is contained in:
henk717 2022-06-18 11:38:14 +02:00 committed by GitHub
parent 49a3cf132e 5e71f7fe97
commit a10446f258
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 45 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

41
aiserver.py
View File

@ -67,6 +67,17 @@ def new_init(self, *args, **kwargs):
self.ncols = 99
tqdm.__init__ = new_init
# Fix some issues with the OPT tokenizer
from transformers import PreTrainedTokenizerBase
old_pretrainedtokenizerbase_from_pretrained = PreTrainedTokenizerBase.from_pretrained.__func__
@classmethod
def new_pretrainedtokenizerbase_from_pretrained(cls, *args, **kwargs):
tokenizer = old_pretrainedtokenizerbase_from_pretrained(cls, *args, **kwargs)
tokenizer._koboldai_header = tokenizer.encode("")
tokenizer.add_bos_token = False
tokenizer.add_prefix_space = False
return tokenizer
PreTrainedTokenizerBase.from_pretrained = new_pretrainedtokenizerbase_from_pretrained
#==================================================================#
# Variables & Storage
@ -1624,6 +1635,10 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go
if(os.path.isdir(vars.custmodpth)):
try:
tokenizer = AutoTokenizer.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
except Exception as e:
pass
try:
tokenizer = AutoTokenizer.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
@ -1636,6 +1651,10 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go
elif(os.path.isdir("models/{}".format(vars.model.replace('/', '_')))):
try:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
except Exception as e:
pass
try:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
@ -1661,6 +1680,10 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go
try:
tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
except Exception as e:
pass
try:
tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
@ -3326,24 +3349,26 @@ def calcsubmitbudget(actionlen, winfo, mem, anotetxt, actions, submission=None,
global tokenizer
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.revision, cache_dir="cache")
lnheader = len(tokenizer._koboldai_header)
# Calculate token budget
prompttkns = tokenizer.encode(utils.encodenewlines(vars.comregex_ai.sub('', vars.prompt)), max_length=int(2e9), truncation=True)
lnprompt = len(prompttkns)
memtokens = tokenizer.encode(utils.encodenewlines(mem), max_length=int(2e9), truncation=True)
lnmem = len(memtokens)
if(lnmem > vars.max_length - lnsp - vars.genamt - budget_deduction):
if(lnmem > vars.max_length - lnheader - lnsp - vars.genamt - budget_deduction):
raise OverflowError("The memory in your story is too long. Please either write a shorter memory text or increase the Max Tokens setting. If you are using a soft prompt, additionally consider using a smaller soft prompt.")
witokens = tokenizer.encode(utils.encodenewlines(winfo), max_length=int(2e9), truncation=True)
lnwi = len(witokens)
if(lnmem + lnwi > vars.max_length - lnsp - vars.genamt - budget_deduction):
if(lnmem + lnwi > vars.max_length - lnheader - lnsp - vars.genamt - budget_deduction):
raise OverflowError("The current active world info keys take up too many tokens. Please either write shorter world info, decrease World Info Depth or increase the Max Tokens setting. If you are using a soft prompt, additionally consider using a smaller soft prompt.")
if(anotetxt != ""):
anotetkns = tokenizer.encode(utils.encodenewlines(anotetxt), max_length=int(2e9), truncation=True)
lnanote = len(anotetkns)
if(lnmem + lnwi + lnanote > vars.max_length - lnsp - vars.genamt - budget_deduction):
if(lnmem + lnwi + lnanote > vars.max_length - lnheader - lnsp - vars.genamt - budget_deduction):
raise OverflowError("The author's note in your story is too long. Please either write a shorter author's note or increase the Max Tokens setting. If you are using a soft prompt, additionally consider using a smaller soft prompt.")
if(vars.useprompt):
@ -3354,14 +3379,14 @@ def calcsubmitbudget(actionlen, winfo, mem, anotetxt, actions, submission=None,
lnsubmission = len(tokenizer.encode(utils.encodenewlines(vars.comregex_ai.sub('', submission)), max_length=int(2e9), truncation=True)) if submission is not None else 0
maybe_lnprompt = lnprompt if vars.useprompt and actionlen > 0 else 0
if(lnmem + lnwi + lnanote + maybe_lnprompt + lnsubmission > vars.max_length - lnsp - vars.genamt - budget_deduction):
if(lnmem + lnwi + lnanote + maybe_lnprompt + lnsubmission > vars.max_length - lnheader - lnsp - vars.genamt - budget_deduction):
raise OverflowError("Your submission is too long. Please either write a shorter submission or increase the Max Tokens setting. If you are using a soft prompt, additionally consider using a smaller soft prompt. If you are using the Always Add Prompt setting, turning it off may help.")
assert budget >= 0
if(actionlen == 0):
# First/Prompt action
tokens = memtokens + witokens + anotetkns + prompttkns
tokens = tokenizer._koboldai_header + memtokens + witokens + anotetkns + prompttkns
assert len(tokens) <= vars.max_length - lnsp - vars.genamt - budget_deduction
ln = len(tokens) + lnsp
return tokens, ln+1, ln+vars.genamt
@ -3409,12 +3434,12 @@ def calcsubmitbudget(actionlen, winfo, mem, anotetxt, actions, submission=None,
# Did we get to add the A.N.? If not, do it here
if(anotetxt != ""):
if((not anoteadded) or forceanote):
tokens = memtokens + witokens + anotetkns + prompttkns + tokens
tokens = tokenizer._koboldai_header + memtokens + witokens + anotetkns + prompttkns + tokens
else:
tokens = memtokens + witokens + prompttkns + tokens
tokens = tokenizer._koboldai_header + memtokens + witokens + prompttkns + tokens
else:
# Prepend Memory, WI, and Prompt before action tokens
tokens = memtokens + witokens + prompttkns + tokens
tokens = tokenizer._koboldai_header + memtokens + witokens + prompttkns + tokens
# Send completed bundle to generator
assert len(tokens) <= vars.max_length - lnsp - vars.genamt - budget_deduction

12
tpu_mtj_backend.py
View File

@ -1324,6 +1324,10 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
if(os.path.isdir(vars.custmodpth)):
try:
tokenizer = AutoTokenizer.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
except Exception as e:
pass
try:
tokenizer = AutoTokenizer.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
@ -1336,6 +1340,10 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
elif(os.path.isdir("models/{}".format(vars.model.replace('/', '_')))):
try:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
except Exception as e:
pass
try:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
@ -1348,6 +1356,10 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
else:
try:
tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
except Exception as e:
pass
try:
tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")