Style improvements (#1957)

modules/RWKV.py

@@ -24,13 +24,12 @@ class RWKVModel:
     @classmethod
     def from_pretrained(self, path, dtype="fp16", device="cuda"):
         tokenizer_path = Path(f"{path.parent}/20B_tokenizer.json")
-
         if shared.args.rwkv_strategy is None:
             model = RWKV(model=str(path), strategy=f'{device} {dtype}')
         else:
             model = RWKV(model=str(path), strategy=shared.args.rwkv_strategy)
-        pipeline = PIPELINE(model, str(tokenizer_path))
 
+        pipeline = PIPELINE(model, str(tokenizer_path))
         result = self()
         result.pipeline = pipeline
         result.model = model
@@ -83,7 +82,6 @@ class RWKVModel:
             out = self.cached_output_logits
 
         for i in range(token_count):
-
             # forward
             tokens = self.pipeline.encode(ctx) if i == 0 else [token]
             while len(tokens) > 0:
@@ -91,35 +89,38 @@ class RWKVModel:
                 tokens = tokens[args.chunk_len:]
 
             # cache the model state after scanning the context
-            # we don't cache the state after processing our own generated tokens because 
-            # the output string might be post-processed arbitrarily. Therefore, what's fed into the model 
+            # we don't cache the state after processing our own generated tokens because
+            # the output string might be post-processed arbitrarily. Therefore, what's fed into the model
             # on the next round of chat might be slightly different what what it output on the previous round
             if i == 0:
                 self.cached_context += ctx
                 self.cached_model_state = copy.deepcopy(state)
                 self.cached_output_logits = copy.deepcopy(out)
-            
+
             # adjust probabilities
             for n in args.token_ban:
                 out[n] = -float('inf')
+
             for n in occurrence:
                 out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)
-            
+
             # sampler
             token = self.pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p, top_k=args.top_k)
             if token in args.token_stop:
                 break
+
             all_tokens += [token]
             if token not in occurrence:
                 occurrence[token] = 1
             else:
                 occurrence[token] += 1
-            
+
             # output
             tmp = self.pipeline.decode([token])
-            if '\ufffd' not in tmp: # is valid utf-8 string?
+            if '\ufffd' not in tmp:  # is valid utf-8 string?
                 if callback:
                     callback(tmp)
+
                 out_str += tmp
 
         return out_str
@@ -133,7 +134,6 @@ class RWKVTokenizer:
     def from_pretrained(self, path):
         tokenizer_path = path / "20B_tokenizer.json"
         tokenizer = Tokenizer.from_file(str(tokenizer_path))
-
         result = self()
         result.tokenizer = tokenizer
         return result