Add the option to use samplers in the logit viewer

modules/logits.py

@@ -1,19 +1,30 @@
 import torch
 
-from modules import shared
+from modules import sampler_hijack, shared
+from modules.text_generation import generate_reply
+
+global_scores = None
 
 
-def get_next_logits(prompt):
-    tokens = shared.tokenizer.encode(prompt, return_tensors='pt').cuda()
-    output = shared.model(input_ids=tokens)
+def get_next_logits(prompt, state, use_samplers, previous):
+    if use_samplers:
+        state['max_new_tokens'] = 1
+        state['auto_max_new_tokens'] = False
+        for _ in generate_reply(prompt, state):
+            pass
+
+        scores = sampler_hijack.global_scores[-1]
+    else:
+        tokens = shared.tokenizer.encode(prompt, return_tensors='pt').cuda()
+        output = shared.model(input_ids=tokens)
+        scores = output['logits'][-1][-1]
 
-    scores = output['logits'][-1][-1]
     probs = torch.softmax(scores, dim=-1, dtype=torch.float)
-
     topk_values, topk_indices = torch.topk(probs, k=20, largest=True, sorted=True)
-    topk_values = [f"{float(i):.5f}" % i for i in topk_values]
+    topk_values = [f"{float(i):.5f}" for i in topk_values]
+
     output = ''
     for row in list(zip(topk_values, shared.tokenizer.convert_ids_to_tokens(topk_indices))):
-        output += f"{row[0]} {row[1]}\n"
+        output += f"{row[0]}  -  {row[1]}\n"
 
-    return output
+    return output, previous