Minor code cleanups in generate.py and model.py

torchchat/generate.py

@@ -71,11 +71,11 @@ def encode_header(self, role) -> List[int]:
 
     def encode_message(self, message) -> List[int]:
         tokens = self.encode_header(message["role"])
-        if type(message["content"]) is str:
+        if isinstance(message["content"], str):
             tokens.extend(
                 self.tokenizer.encode(message["content"], bos=False, eos=False)
             )
-        elif type(message["content"]) is list:
+        elif isinstance(message["content"], list):
             for content in message["content"]:
                 if content["type"] == "text":
                     tokens.extend(
@@ -190,7 +190,7 @@ def from_args(cls, args):
                 for image_prompt in image_prompts
                 if (not os.path.exists(image_prompt))
             ]
-            if len(non_existent_image_prompts):
+            if non_existent_image_prompts:
                 raise RuntimeError(
                     f"Image prompt {non_existent_image_prompts} does not exist"
                 )
@@ -238,7 +238,7 @@ def __init__(
         draft_quantize: bool,
     ):
         torch._inductor.config.coordinate_descent_tuning = (
-            False if builder_args.device == "cpu" else True
+            builder_args.device != "cpu"
         )
         torch._inductor.config.triton.unique_kernel_names = True
         torch._inductor.config.fx_graph_cache = True  # Experimental feature to reduce compilation times, will be on by default in future
@@ -1002,11 +1002,8 @@ def chat(
                 max_seq_length,
             )
 
-        max_seq_length = (
-            max_seq_length + self.speculative_builder_args.speculate_k + 1
-            if self.draft_model is not None
-            else max_seq_length
-        )
+        if self.draft_model is not None:
+            max_seq_length += self.speculative_builder_args.speculate_k + 1
 
         aggregate_metrics = {
             "tokens_per_sec": [],

torchchat/model.py

@@ -94,7 +94,7 @@ def __init__(
         self.encoder = encoder
         self.decoder = decoder
 
-        # esclate the embedding layer outside decoder llava model need to fuse
+        # escalate the embedding layer outside decoder llava model need to fuse
         # the text and image embedding together before passing to decoder.
         self.tok_embeddings = getattr(self.decoder, token_embedding_name)