mirror of
https://github.com/KoboldAI/KoboldAI-Client.git
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Merge pull request #23 from VE-FORBRYDERNE/scan-test
Dynamic world info scan
This commit is contained in:
henk717
GitHub
377
aiserver.py
377
aiserver.py
@ -13,7 +13,7 @@ from tkinter import messagebox
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import json
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import collections
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import zipfile
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from typing import Union, Tuple
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from typing import Union, Dict, Set
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import requests
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import html
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@ -124,6 +124,7 @@ class vars:
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acregex_ui = re.compile(r'^ *(>.*)$', re.MULTILINE) # Pattern for matching actions in the HTML-escaped story so we can apply colouring, etc (make sure to encase part to format in parentheses)
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actionmode = 1
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adventure = False
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dynamicscan = False
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remote = False
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#==================================================================#
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@ -512,7 +513,8 @@ print("{0}OK!{1}".format(colors.GREEN, colors.END))
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if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
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if(not vars.noai):
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print("{0}Initializing transformers, please wait...{1}".format(colors.PURPLE, colors.END))
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from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel, GPTNeoForCausalLM, GPTNeoModel, AutoModelForCausalLM
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from transformers import StoppingCriteria, GPT2Tokenizer, GPT2LMHeadModel, GPTNeoForCausalLM, GPTNeoModel, AutoModelForCausalLM
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import transformers.generation_utils
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# Patch transformers to use our soft prompt
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def patch_causallm(cls):
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@ -528,7 +530,7 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
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if(vars.sp is not None):
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vars.sp = vars.sp.to(inputs_embeds.dtype).to(inputs_embeds.device)
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inputs_embeds = torch.where(
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(shifted_input_ids >= 0)[:, :, None],
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(shifted_input_ids >= 0)[..., None],
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vars.sp[shifted_input_ids.clamp(min=0)],
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inputs_embeds,
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)
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@ -543,6 +545,52 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
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except:
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pass
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# Sets up dynamic world info scanner
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class DynamicWorldInfoScanCriteria(StoppingCriteria):
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def __init__(
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self,
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tokenizer,
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excluded_world_info: set,
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#head_length: torch.LongTensor,
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head_length: int,
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):
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self.any_new_entries = False
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self.tokenizer = tokenizer
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self.excluded_world_info = excluded_world_info
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self.head_length = head_length
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def __call__(
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self,
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input_ids: torch.LongTensor,
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scores: torch.FloatTensor,
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**kwargs,
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) -> bool:
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assert input_ids.ndim == 2
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#assert input_ids.shape[:-1] == self.head_length.shape
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self.any_new_entries = False
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if(not vars.dynamicscan):
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return False
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tail = input_ids[..., self.head_length:]
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for t in tail:
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decoded = tokenizer.decode(t)
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_, found = checkworldinfo(decoded, force_use_txt=True)
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found -= self.excluded_world_info
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if(len(found) != 0):
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self.any_new_entries = True
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break
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return self.any_new_entries
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old_get_stopping_criteria = transformers.generation_utils.GenerationMixin._get_stopping_criteria
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def new_get_stopping_criteria(self, *args, **kwargs):
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stopping_criteria = old_get_stopping_criteria(self, *args, **kwargs)
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global tokenizer
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self.kai_scanner = DynamicWorldInfoScanCriteria(
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tokenizer=tokenizer,
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excluded_world_info=self.kai_scanner_excluded_world_info,
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head_length=self.kai_scanner_head_length,
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)
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stopping_criteria.append(self.kai_scanner)
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return stopping_criteria
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transformers.generation_utils.GenerationMixin._get_stopping_criteria = new_get_stopping_criteria
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# If custom GPT Neo model was chosen
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if(vars.model == "NeoCustom"):
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model_config = open(vars.custmodpth + "/config.json", "r")
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@ -901,6 +949,10 @@ def get_message(msg):
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vars.adventure = msg['data']
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settingschanged()
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refresh_settings()
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elif(msg['cmd'] == 'setdynamicscan'):
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vars.dynamicscan = msg['data']
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settingschanged()
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refresh_settings()
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elif(not vars.remote and msg['cmd'] == 'importwi'):
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wiimportrequest()
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@ -958,6 +1010,7 @@ def savesettings():
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js["widepth"] = vars.widepth
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js["useprompt"] = vars.useprompt
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js["adventure"] = vars.adventure
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js["dynamicscan"] = vars.dynamicscan
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# Write it
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if not os.path.exists('settings'):
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@ -1008,6 +1061,8 @@ def loadsettings():
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vars.useprompt = js["useprompt"]
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if("adventure" in js):
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vars.adventure = js["adventure"]
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if("dynamicscan" in js):
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vars.dynamicscan = js["dynamicscan"]
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file.close()
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@ -1032,6 +1087,8 @@ def loadmodelsettings():
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vars.rep_pen = js["rep_pen"]
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if("adventure" in js):
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vars.adventure = js["adventure"]
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if("dynamicscan" in js):
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vars.dynamicscan = js["dynamicscan"]
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if("formatoptns" in js):
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vars.formatoptns = js["formatoptns"]
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model_config.close()
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@ -1148,135 +1205,140 @@ def actionback():
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vars.genseqs = []
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#==================================================================#
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# Take submitted text and build the text to be given to generator
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#
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#==================================================================#
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def calcsubmit(txt):
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anotetxt = "" # Placeholder for Author's Note text
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lnanote = 0 # Placeholder for Author's Note length
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forceanote = False # In case we don't have enough actions to hit A.N. depth
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anoteadded = False # In case our budget runs out before we hit A.N. depth
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actionlen = len(vars.actions)
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def calcsubmitbudgetheader(txt, **kwargs):
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# Scan for WorldInfo matches
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winfo = checkworldinfo(txt)
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winfo, found_entries = checkworldinfo(txt, **kwargs)
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# Add a newline to the end of memory
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if(vars.memory != "" and vars.memory[-1] != "\n"):
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mem = vars.memory + "\n"
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else:
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mem = vars.memory
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# Build Author's Note if set
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if(vars.authornote != ""):
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anotetxt = "\n[Author's note: "+vars.authornote+"]\n"
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else:
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anotetxt = ""
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return winfo, mem, anotetxt, found_entries
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def calcsubmitbudget(actionlen, winfo, mem, anotetxt, actions):
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forceanote = False # In case we don't have enough actions to hit A.N. depth
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anoteadded = False # In case our budget runs out before we hit A.N. depth
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anotetkns = [] # Placeholder for Author's Note tokens
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lnanote = 0 # Placeholder for Author's Note length
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# Calculate token budget
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prompttkns = tokenizer.encode(vars.prompt)
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lnprompt = len(prompttkns)
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memtokens = tokenizer.encode(mem)
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lnmem = len(memtokens)
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witokens = tokenizer.encode(winfo)
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lnwi = len(witokens)
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if(anotetxt != ""):
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anotetkns = tokenizer.encode(anotetxt)
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lnanote = len(anotetkns)
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lnsp = vars.sp.shape[0] if vars.sp is not None else 0
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if(vars.useprompt):
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budget = vars.max_length - lnsp - lnprompt - lnmem - lnanote - lnwi - vars.genamt
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else:
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budget = vars.max_length - lnsp - lnmem - lnanote - lnwi - vars.genamt
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if(actionlen == 0):
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# First/Prompt action
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subtxt = vars.memory + winfo + anotetxt + vars.prompt
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lnsub = lnsp + lnmem + lnwi + lnprompt + lnanote
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return subtxt, lnsub+1, lnsub+vars.genamt
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else:
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tokens = []
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# Check if we have the action depth to hit our A.N. depth
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if(anotetxt != "" and actionlen < vars.andepth):
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forceanote = True
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# Get most recent action tokens up to our budget
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n = 0
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for key in reversed(actions):
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chunk = actions[key]
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if(budget <= 0):
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break
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acttkns = tokenizer.encode(chunk)
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tknlen = len(acttkns)
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if(tknlen < budget):
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tokens = acttkns + tokens
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budget -= tknlen
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else:
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count = budget * -1
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tokens = acttkns[count:] + tokens
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budget = 0
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break
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# Inject Author's Note if we've reached the desired depth
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if(n == vars.andepth-1):
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if(anotetxt != ""):
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tokens = anotetkns + tokens # A.N. len already taken from bdgt
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anoteadded = True
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n += 1
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# If we're not using the prompt every time and there's still budget left,
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# add some prompt.
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if(not vars.useprompt):
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if(budget > 0):
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prompttkns = prompttkns[-budget:]
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else:
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prompttkns = []
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# Did we get to add the A.N.? If not, do it here
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if(anotetxt != ""):
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if((not anoteadded) or forceanote):
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tokens = memtokens + witokens + anotetkns + prompttkns + tokens
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else:
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tokens = memtokens + witokens + prompttkns + tokens
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else:
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# Prepend Memory, WI, and Prompt before action tokens
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tokens = memtokens + witokens + prompttkns + tokens
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# Send completed bundle to generator
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ln = len(tokens) + lnsp
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return tokenizer.decode(tokens), ln+1, ln+vars.genamt
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#==================================================================#
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# Take submitted text and build the text to be given to generator
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#==================================================================#
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def calcsubmit(txt):
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anotetxt = "" # Placeholder for Author's Note text
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forceanote = False # In case we don't have enough actions to hit A.N. depth
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anoteadded = False # In case our budget runs out before we hit A.N. depth
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actionlen = len(vars.actions)
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winfo, mem, anotetxt, found_entries = calcsubmitbudgetheader(txt)
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# For all transformers models
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if(vars.model != "InferKit"):
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anotetkns = [] # Placeholder for Author's Note tokens
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# Calculate token budget
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prompttkns = tokenizer.encode(vars.prompt)
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lnprompt = len(prompttkns)
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memtokens = tokenizer.encode(mem)
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lnmem = len(memtokens)
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witokens = tokenizer.encode(winfo)
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lnwi = len(witokens)
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if(anotetxt != ""):
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anotetkns = tokenizer.encode(anotetxt)
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lnanote = len(anotetkns)
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lnsp = vars.sp.shape[0] if vars.sp is not None else 0
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if(vars.useprompt):
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budget = vars.max_length - lnsp - lnprompt - lnmem - lnanote - lnwi - vars.genamt
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else:
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budget = vars.max_length - lnsp - lnmem - lnanote - lnwi - vars.genamt
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subtxt, min, max = calcsubmitbudget(actionlen, winfo, mem, anotetxt, vars.actions)
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if(actionlen == 0):
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# First/Prompt action
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subtxt = vars.memory + winfo + anotetxt + vars.prompt
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lnsub = lnsp + lnmem + lnwi + lnprompt + lnanote
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if(not vars.model in ["Colab", "OAI"]):
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generate(subtxt, lnsub+1, lnsub+vars.genamt)
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generate(subtxt, min, max, found_entries=found_entries)
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elif(vars.model == "Colab"):
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sendtocolab(subtxt, lnsub+1, lnsub+vars.genamt)
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sendtocolab(subtxt, min, max)
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elif(vars.model == "OAI"):
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oairequest(subtxt, lnsub+1, lnsub+vars.genamt)
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oairequest(subtxt, min, max)
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else:
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tokens = []
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# Check if we have the action depth to hit our A.N. depth
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if(anotetxt != "" and actionlen < vars.andepth):
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forceanote = True
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# Get most recent action tokens up to our budget
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n = 0
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for key in reversed(vars.actions):
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chunk = vars.actions[key]
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if(budget <= 0):
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break
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acttkns = tokenizer.encode(chunk)
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tknlen = len(acttkns)
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if(tknlen < budget):
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tokens = acttkns + tokens
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budget -= tknlen
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else:
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count = budget * -1
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tokens = acttkns[count:] + tokens
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budget = 0
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break
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# Inject Author's Note if we've reached the desired depth
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if(n == vars.andepth-1):
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if(anotetxt != ""):
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tokens = anotetkns + tokens # A.N. len already taken from bdgt
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anoteadded = True
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n += 1
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# If we're not using the prompt every time and there's still budget left,
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# add some prompt.
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if(not vars.useprompt):
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if(budget > 0):
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prompttkns = prompttkns[-budget:]
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else:
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prompttkns = []
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# Did we get to add the A.N.? If not, do it here
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if(anotetxt != ""):
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if((not anoteadded) or forceanote):
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tokens = memtokens + witokens + anotetkns + prompttkns + tokens
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else:
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tokens = memtokens + witokens + prompttkns + tokens
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else:
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# Prepend Memory, WI, and Prompt before action tokens
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tokens = memtokens + witokens + prompttkns + tokens
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# Send completed bundle to generator
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ln = len(tokens) + lnsp
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if(not vars.model in ["Colab", "OAI"]):
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generate (
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tokenizer.decode(tokens),
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ln+1,
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ln+vars.genamt
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)
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generate(subtxt, min, max, found_entries=found_entries)
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elif(vars.model == "Colab"):
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sendtocolab(
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tokenizer.decode(tokens),
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ln+1,
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ln+vars.genamt
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)
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sendtocolab(subtxt, min, max)
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elif(vars.model == "OAI"):
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oairequest(
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tokenizer.decode(tokens),
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ln+1,
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ln+vars.genamt
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)
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oairequest(subtxt, min, max)
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# For InferKit web API
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else:
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@ -1337,7 +1399,7 @@ def calcsubmit(txt):
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#==================================================================#
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# Send text to generator and deal with output
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#==================================================================#
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def generate(txt, min, max):
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def generate(txt, min, max, found_entries=set()):
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print("{0}Min:{1}, Max:{2}, Txt:{3}{4}".format(colors.YELLOW, min, max, txt, colors.END))
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# Store context in memory to use it for comparison with generated content
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@ -1360,7 +1422,7 @@ def generate(txt, min, max):
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model.config.vocab_size,
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model.config.vocab_size + vars.sp.shape[0],
|
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)
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gen_in = torch.cat((soft_tokens[None], gen_in), dim=1)
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gen_in = torch.cat((soft_tokens[None], gen_in), dim=-1)
|
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if(vars.hascuda and vars.usegpu):
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gen_in = gen_in.to(0)
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@ -1371,22 +1433,60 @@ def generate(txt, min, max):
|
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else:
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gen_in = gen_in.to('cpu')
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model.kai_scanner_head_length = gen_in.shape[-1]
|
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model.kai_scanner_excluded_world_info = found_entries
|
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|
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actions = vars.actions
|
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if(vars.dynamicscan):
|
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actions = actions.copy()
|
||||
|
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with torch.no_grad():
|
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genout = generator(
|
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gen_in,
|
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do_sample=True,
|
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min_length=min,
|
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max_length=max,
|
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repetition_penalty=vars.rep_pen,
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top_p=top_p,
|
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top_k=top_k,
|
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tfs=tfs,
|
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temperature=vars.temp,
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bad_words_ids=vars.badwordsids,
|
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use_cache=True,
|
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return_full_text=False,
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num_return_sequences=vars.numseqs
|
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already_generated = 0
|
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numseqs = vars.numseqs if not vars.dynamicscan else 1
|
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while True:
|
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genout = generator(
|
||||
gen_in,
|
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do_sample=True,
|
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min_length=min,
|
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max_length=max-already_generated,
|
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repetition_penalty=vars.rep_pen,
|
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top_p=top_p,
|
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top_k=top_k,
|
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tfs=tfs,
|
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temperature=vars.temp,
|
||||
bad_words_ids=vars.badwordsids,
|
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use_cache=True,
|
||||
num_return_sequences=numseqs
|
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)
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already_generated += len(genout[0]) - len(gen_in[0])
|
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if(not model.kai_scanner.any_new_entries):
|
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break
|
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txt = tokenizer.decode(genout[0, -already_generated:])
|
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winfo, mem, anotetxt, _found_entries = calcsubmitbudgetheader(txt, force_use_txt=True)
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found_entries |= _found_entries
|
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txt, _, _ = calcsubmitbudget(len(actions), winfo, mem, anotetxt, actions)
|
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encoded = tokenizer.encode(txt, return_tensors="pt", truncation=True).long().to(genout.device)
|
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genout = torch.cat(
|
||||
(
|
||||
encoded,
|
||||
genout[..., -already_generated:],
|
||||
),
|
||||
dim=-1
|
||||
)
|
||||
if(vars.sp is not None):
|
||||
soft_tokens = torch.arange(
|
||||
model.config.vocab_size,
|
||||
model.config.vocab_size + vars.sp.shape[0],
|
||||
device=genout.device,
|
||||
)
|
||||
genout = torch.cat((soft_tokens[None], genout), dim=-1)
|
||||
diff = genout.shape[-1] - gen_in.shape[-1]
|
||||
min += diff
|
||||
max += diff
|
||||
gen_in = genout
|
||||
model.kai_scanner_head_length = encoded.shape[-1]
|
||||
numseqs = 1
|
||||
|
||||
except Exception as e:
|
||||
emit('from_server', {'cmd': 'errmsg', 'data': 'Error occured during generator call, please check console.'}, broadcast=True)
|
||||
print("{0}{1}{2}".format(colors.RED, e, colors.END))
|
||||
@ -1394,7 +1494,8 @@ def generate(txt, min, max):
|
||||
return
|
||||
|
||||
# Need to manually strip and decode tokens if we're not using a pipeline
|
||||
genout = [{"generated_text": tokenizer.decode(tokens[len(gen_in[0])-len(tokens):])} for tokens in genout]
|
||||
#already_generated = -(len(gen_in[0]) - len(tokens))
|
||||
genout = [{"generated_text": tokenizer.decode(tokens[-already_generated:])} for tokens in genout]
|
||||
|
||||
if(len(genout) == 1):
|
||||
genresult(genout[0]["generated_text"])
|
||||
@ -1654,6 +1755,7 @@ def refresh_settings():
|
||||
emit('from_server', {'cmd': 'updatewidepth', 'data': vars.widepth}, broadcast=True)
|
||||
emit('from_server', {'cmd': 'updateuseprompt', 'data': vars.useprompt}, broadcast=True)
|
||||
emit('from_server', {'cmd': 'updateadventure', 'data': vars.adventure}, broadcast=True)
|
||||
emit('from_server', {'cmd': 'updatedynamicscan', 'data': vars.dynamicscan}, broadcast=True)
|
||||
|
||||
emit('from_server', {'cmd': 'updatefrmttriminc', 'data': vars.formatoptns["frmttriminc"]}, broadcast=True)
|
||||
emit('from_server', {'cmd': 'updatefrmtrmblln', 'data': vars.formatoptns["frmtrmblln"]}, broadcast=True)
|
||||
@ -1862,10 +1964,12 @@ def deletewi(num):
|
||||
#==================================================================#
|
||||
# Look for WI keys in text to generator
|
||||
#==================================================================#
|
||||
def checkworldinfo(txt):
|
||||
def checkworldinfo(txt, force_use_txt=False):
|
||||
original_txt = txt
|
||||
|
||||
# Dont go any further if WI is empty
|
||||
if(len(vars.worldinfo) == 0):
|
||||
return
|
||||
return "", set()
|
||||
|
||||
# Cache actions length
|
||||
ln = len(vars.actions)
|
||||
@ -1875,7 +1979,7 @@ def checkworldinfo(txt):
|
||||
depth = vars.widepth
|
||||
# If this is not a continue, add 1 to widepth since submitted
|
||||
# text is already in action history @ -1
|
||||
if(txt != "" and vars.prompt != txt):
|
||||
if(not force_use_txt and (txt != "" and vars.prompt != txt)):
|
||||
txt = ""
|
||||
depth += 1
|
||||
|
||||
@ -1895,12 +1999,17 @@ def checkworldinfo(txt):
|
||||
txt = vars.prompt + "".join(chunks)
|
||||
elif(ln == 0):
|
||||
txt = vars.prompt
|
||||
|
||||
|
||||
if(force_use_txt):
|
||||
txt += original_txt
|
||||
|
||||
# Scan text for matches on WI keys
|
||||
wimem = ""
|
||||
found_entries = set()
|
||||
for wi in vars.worldinfo:
|
||||
if(wi.get("constant", False)):
|
||||
wimem = wimem + wi["content"] + "\n"
|
||||
found_entries.add(id(wi))
|
||||
continue
|
||||
|
||||
if(wi["key"] != ""):
|
||||
@ -1922,15 +2031,17 @@ def checkworldinfo(txt):
|
||||
ksy = ks.strip()
|
||||
if ksy in txt:
|
||||
wimem = wimem + wi["content"] + "\n"
|
||||
found_entries.add(id(wi))
|
||||
found = True
|
||||
break
|
||||
if found:
|
||||
break
|
||||
else:
|
||||
wimem = wimem + wi["content"] + "\n"
|
||||
found_entries.add(id(wi))
|
||||
break
|
||||
|
||||
return wimem
|
||||
return wimem, found_entries
|
||||
|
||||
#==================================================================#
|
||||
# Commit changes to Memory storage
|
||||
|
||||
Reference in New Issue
Block a user