Adjust chat loop routing for fastchat and model type

qllm/plugin/chatcli/generation.py

@@ -10,7 +10,7 @@ def generate_stream(model, tokenizer, prompt: str, device, max_new_tokens: int,
 
     lhs_tokens = torch.tensor(inputs.input_ids, dtype=torch.int64, device=device).unsqueeze(0)
 
-    past_kvs = transformers.DynamicCache()
+    past_kvs = None
     output_ids = list(inputs.input_ids)
     input_echo_len = len(output_ids)
 
@@ -70,7 +70,7 @@ def generate(model, tokenizer, prompt: str, max_new_tokens:int, context_len: int
 
     lhs_tokens = torch.tensor(inputs.input_ids, dtype=torch.int64, device=device).unsqueeze(0)
 
-    past_kvs = transformers.DynamicCache()
+    past_kvs = None
     output_ids = list(inputs.input_ids)
     input_echo_len = len(output_ids)
 

qllm/plugin/chatcli/inference.py

@@ -1,5 +1,7 @@
 import time
 import torch
+import transformers
+from packaging.version import Version, InvalidVersion
 try:
     import fastchat
     #from fastchat.conversation import Conversation, SeparatorStyle
@@ -28,9 +30,16 @@ def chat_loop(
     debug: bool = True,
     echo: bool = False,
 ):
+    model_type = str(type(model)).lower()
+    use_fastchat_v2 = False
     if _fastchat_available:
+        try:
+            use_fastchat_v2 = Version(transformers.__version__) < Version("4.3") and "llama" not in model_type
+        except InvalidVersion:
+            use_fastchat_v2 = False
+
+    if use_fastchat_v2:
         return chat_loop_v2(model, tokenizer)
-    model_type = str(type(model)).lower()
     if "llama" not in model_type and hasattr(tokenizer, 'apply_chat_template'):
         return chat_loop_v3(model, tokenizer)
     assert "llama" in model_type, 'have you installed fschat? please run `pip install fschat` and try again.'