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

modules/deepspeed_parameters.py

@@ -1,5 +1,4 @@
 def generate_ds_config(ds_bf16, train_batch_size, nvme_offload_dir):
-
     '''
     DeepSpeed configration
     https://huggingface.co/docs/transformers/main_classes/deepspeed

modules/evaluate.py

@@ -20,6 +20,8 @@ def load_past_evaluations():
         return df
     else:
         return pd.DataFrame(columns=['Model', 'LoRAs', 'Dataset', 'Perplexity', 'stride', 'max_length', 'Date', 'Comment'])
+
+
 past_evaluations = load_past_evaluations()
 
 

modules/extensions.py

@@ -7,7 +7,6 @@ import gradio as gr
 import extensions
 import modules.shared as shared
 
-
 state = {}
 available_extensions = []
 setup_called = set()
@@ -91,7 +90,7 @@ def _apply_state_modifier_extensions(state):
             state = getattr(extension, "state_modifier")(state)
 
     return state
- 
+
 
 # Extension functions that override the default tokenizer output - currently only the first one will work
 def _apply_tokenizer_extensions(function_name, state, prompt, input_ids, input_embeds):
@@ -108,7 +107,7 @@ def _apply_custom_tokenized_length(prompt):
     for extension, _ in iterator():
         if hasattr(extension, 'custom_tokenized_length'):
             return getattr(extension, 'custom_tokenized_length')(prompt)
-    
+
     return None
 
 

modules/logging_colors.py

@@ -1,6 +1,8 @@
 # Copied from https://stackoverflow.com/a/1336640
 
 import logging
+import platform
+
 
 def add_coloring_to_emit_windows(fn):
     # add methods we need to the class
@@ -11,6 +13,7 @@ def add_coloring_to_emit_windows(fn):
 
     def _set_color(self, code):
         import ctypes
+
         # Constants from the Windows API
         self.STD_OUTPUT_HANDLE = -11
         hdl = ctypes.windll.kernel32.GetStdHandle(self.STD_OUTPUT_HANDLE)
@@ -94,7 +97,6 @@ def add_coloring_to_emit_ansi(fn):
     return new
 
 
-import platform
 if platform.system() == 'Windows':
     # Windows does not support ANSI escapes and we are using API calls to set the console color
     logging.StreamHandler.emit = add_coloring_to_emit_windows(logging.StreamHandler.emit)

modules/models.py

@@ -161,10 +161,10 @@ def load_model(model_name):
     # Custom
     else:
         params = {
-          "low_cpu_mem_usage": True,
-          "trust_remote_code": trust_remote_code
+            "low_cpu_mem_usage": True,
+            "trust_remote_code": trust_remote_code
         }
-        
+
         if not any((shared.args.cpu, torch.cuda.is_available(), torch.has_mps)):
             logging.warning("torch.cuda.is_available() returned False. This means that no GPU has been detected. Falling back to CPU mode.")
             shared.args.cpu = True
@@ -288,7 +288,7 @@ def load_soft_prompt(name):
                         logging.info(f"{field}: {', '.join(j[field])}")
                     else:
                         logging.info(f"{field}: {j[field]}")
-                        
+
             logging.info()
             tensor = np.load('tensor.npy')
             Path('tensor.npy').unlink()