improve state cache performance

backend-python/routes/state_cache.py

@@ -94,28 +94,19 @@ def add_state(body: AddStateBody):
         state: Union[Any, None] = None
 
         if body.state is not None:
-            if type(body.state) == list or type(body.state) == np.ndarray:
-                devices = [
-                    (
-                        tensor.device
-                        if hasattr(tensor, "device")
-                        else torch.device("cpu")
-                    )
-                    for tensor in body.state
-                ]
-                state = (
-                    [tensor.cpu() for tensor in body.state]
-                    if hasattr(body.state[0], "device")
-                    else copy.deepcopy(body.state)
-                )
-            else:
-                state = body.state.back()  # WebGPU
+            if type(state) == list and hasattr(state[0], "device"):  # torch
+                devices = [tensor.device for tensor in body.state]
+                state = [tensor.cpu() for tensor in body.state]
+            elif type(body.state) == np.ndarray:  # rwkv.cpp
+                state = body.state
+            else:  # WebGPU
+                state = body.state.back()
 
         id: int = trie.insert(body.prompt)
         dtrie[id] = {
-            "tokens": copy.deepcopy(body.tokens),
+            "tokens": body.tokens,
             "state": state,
-            "logits": copy.deepcopy(body.logits),
+            "logits": body.logits,
             "devices": devices,
         }
 
@@ -199,12 +190,12 @@ def longest_prefix_state(body: LongestPrefixStateBody, request: Request):
     except:
         pass
     if id != -1:
+        prompt: str = trie[id]
         v = dtrie[id]
         devices: List[torch.device] = v["devices"]
-        prompt: str = trie[id]
         state: Union[Any, None] = v["state"]
 
-        if state is not None and type(state) == list and hasattr(state[0], "device"):
+        if type(state) == list and hasattr(state[0], "device"):  # torch
             state = [tensor.to(devices[i]) for i, tensor in enumerate(state)]
 
         quick_log(request, body, "Hit:\n" + prompt)

backend-python/utils/rwkv.py

@@ -239,14 +239,9 @@ class AbstractRWKV(ABC):
             self.model_tokens = []
         else:
             delta_prompt = prompt[len(cache["prompt"]) :]
-            state = cache["state"]
-            self.model_state = (
-                copy.deepcopy(state)
-                if type(state) == list or type(state) == np.ndarray
-                else state
-            )
-            self.model_tokens = copy.deepcopy(cache["tokens"])
-            logits = copy.deepcopy(cache["logits"])
+            self.model_state = cache["state"]
+            self.model_tokens = cache["tokens"]
+            logits = cache["logits"]
 
         prompt_token_len = 0
         if delta_prompt != "":