Support for multiple gens per action with dynamic scan

aiserver.py

@@ -14,7 +14,7 @@ from tkinter import messagebox
 import json
 import collections
 import zipfile
-from typing import Union, Dict, Set
+from typing import Union, Dict, Set, List
 
 import requests
 import html
@@ -565,8 +565,7 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
             def __init__(
                 self,
                 tokenizer,
-                excluded_world_info: set,
-                #head_length: torch.LongTensor,
+                excluded_world_info: List[Set],
                 head_length: int,
             ):
                 self.any_new_entries = False
@@ -580,15 +579,15 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
                 **kwargs,
             ) -> bool:
                 assert input_ids.ndim == 2
-                #assert input_ids.shape[:-1] == self.head_length.shape
+                assert len(self.excluded_world_info) == input_ids.shape[0]
                 self.any_new_entries = False
                 if(not vars.dynamicscan):
                     return False
                 tail = input_ids[..., self.head_length:]
-                for t in tail:
+                for i, t in enumerate(tail):
                     decoded = tokenizer.decode(t)
                     _, found = checkworldinfo(decoded, force_use_txt=True)
-                    found -= self.excluded_world_info
+                    found -= self.excluded_world_info[i]
                     if(len(found) != 0):
                         self.any_new_entries = True
                         break
@@ -1423,8 +1422,12 @@ def calcsubmit(txt):
 #==================================================================#
 # Send text to generator and deal with output
 #==================================================================#
-def generate(txt, min, max, found_entries=set()):    
-    print("{0}Min:{1}, Max:{2}, Txt:{3}{4}".format(colors.YELLOW, min, max, txt, colors.END))
+def generate(txt, minimum, maximum, found_entries=None):    
+    if(found_entries is None):
+        found_entries = set()
+    found_entries = tuple(found_entries.copy() for _ in range(vars.numseqs))
+
+    print("{0}Min:{1}, Max:{2}, Txt:{3}{4}".format(colors.YELLOW, minimum, maximum, txt, colors.END))
     
     # Store context in memory to use it for comparison with generated content
     vars.lastctx = txt
@@ -1466,13 +1469,13 @@ def generate(txt, min, max, found_entries=set()):
 
         with torch.no_grad():
             already_generated = 0
-            numseqs = vars.numseqs if not vars.dynamicscan else 1
+            numseqs = vars.numseqs
             while True:
                 genout = generator(
                     gen_in, 
                     do_sample=True, 
-                    min_length=min, 
-                    max_length=max-already_generated,
+                    min_length=minimum, 
+                    max_length=maximum-already_generated,
                     repetition_penalty=vars.rep_pen,
                     top_p=top_p,
                     top_k=top_k,
@@ -1485,11 +1488,17 @@ def generate(txt, min, max, found_entries=set()):
                 already_generated += len(genout[0]) - len(gen_in[0])
                 if(not model.kai_scanner.any_new_entries):
                     break
-                txt = tokenizer.decode(genout[0, -already_generated:])
-                winfo, mem, anotetxt, _found_entries = calcsubmitbudgetheader(txt, force_use_txt=True)
-                found_entries |= _found_entries
-                txt, _, _ = calcsubmitbudget(len(actions), winfo, mem, anotetxt, actions)
-                encoded = tokenizer.encode(txt, return_tensors="pt", truncation=True).long().to(genout.device)
+                assert genout.ndim >= 2
+                assert genout.shape[0] == vars.numseqs
+                encoded = []
+                for i in range(vars.numseqs):
+                    txt = tokenizer.decode(genout[i, -already_generated:])
+                    winfo, mem, anotetxt, _found_entries = calcsubmitbudgetheader(txt, force_use_txt=True)
+                    found_entries[i].update(_found_entries)
+                    txt, _, _ = calcsubmitbudget(len(actions), winfo, mem, anotetxt, actions)
+                    encoded.append(tokenizer.encode(txt, return_tensors="pt", truncation=True)[0].long().to(genout.device))
+                max_length = len(max(encoded, key=len))
+                encoded = torch.stack(tuple(torch.nn.functional.pad(e, (max_length - len(e), 0), value=model.config.pad_token_id or model.config.eos_token_id) for e in encoded))
                 genout = torch.cat(
                     (
                         encoded,
@@ -1503,10 +1512,10 @@ def generate(txt, min, max, found_entries=set()):
                         model.config.vocab_size + vars.sp.shape[0],
                         device=genout.device,
                     )
-                    genout = torch.cat((soft_tokens[None], genout), dim=-1)
+                    genout = torch.cat((soft_tokens.tile(vars.numseqs, 1), genout), dim=-1)
                 diff = genout.shape[-1] - gen_in.shape[-1]
-                min += diff
-                max += diff
+                minimum += diff
+                maximum += diff
                 gen_in = genout
                 model.kai_scanner_head_length = encoded.shape[-1]
                 numseqs = 1

gensettings.py

@@ -128,7 +128,7 @@ gensettingstf = [{
 	"max": 1,
 	"step": 1,
 	"default": 0,
-    "tooltip": "Scan the AI's output for world info keys as it's generating the output. Turning this on will set Gens Per Action to 1, as these two features are not currently compatible with each other."
+    "tooltip": "Scan the AI's output for world info keys as it's generating the output."
 	}]
 
 gensettingsik =[{