bump webgpu(python) (https://github.com/cryscan/web-rwkv-py)

backend-python/routes/state_cache.py

@@ -96,7 +96,9 @@ def copy_tensor_to_cpu(tensors):
     elif tensors_type == np.ndarray:  # rwkv.cpp
         copied = tensors
     else:  # WebGPU state
-        copied = tensors.back()
+        model = global_var.get(global_var.Model)
+        if model:
+            copied = model.model.model.back_state()
 
     return copied, devices
 
@@ -238,11 +240,14 @@ def longest_prefix_state(body: LongestPrefixStateBody, request: Request):
         state: Union[Any, None] = v["state"]
         logits: Union[Any, None] = v["logits"]
 
-        if type(state) == list and hasattr(state[0], "device"):  # torch
+        state_type = type(state)
+        if state_type == list and hasattr(state[0], "device"):  # torch
             state = [
-                tensor.to(devices[i])
+                (
-                if devices[i] != torch.device("cpu")
+                    tensor.to(devices[i])
-                else tensor.clone()
+                    if devices[i] != torch.device("cpu")
+                    else tensor.clone()
+                )
                 for i, tensor in enumerate(state)
             ]
             logits = (
@@ -250,7 +255,9 @@ def longest_prefix_state(body: LongestPrefixStateBody, request: Request):
                 if logits_device != torch.device("cpu")
                 else logits.clone()
             )
-        else:  # rwkv.cpp, WebGPU
+        elif state_type == np.ndarray:  # rwkv.cpp
+            logits = np.copy(logits)
+        else:  # WebGPU
             logits = np.copy(logits)
 
         quick_log(request, body, "Hit:\n" + prompt)

backend-python/rwkv_pip/webgpu/model.py

@@ -13,13 +13,6 @@ except ModuleNotFoundError:
 
 class RWKV:
     def __init__(self, model_path: str, strategy: str = None):
-        self.info = wrp.peek_info(model_path)
-        self.w = {}  # fake weight
-        self.w["emb.weight"] = [0] * self.info.num_vocab
-        self.version = str(self.info.version).lower()
-        self.wrp = getattr(wrp, self.version)
-        self.version = float(self.version.replace("v", ""))
-
         layer = (
             int(s.lstrip("layer"))
             for s in strategy.split()
@@ -33,21 +26,25 @@ class RWKV:
             for s in s.split(",")
             if s.startswith("chunk")
         )
+        self.token_chunk_size = next(chunk_size, 32)
 
         args = {
-            "file": model_path,
+            "path": model_path,
-            "turbo": True,
             "quant": next(layer, 31) if "i8" in strategy else 0,
             "quant_nf4": next(layer, 26) if "i4" in strategy else 0,
-            "token_chunk_size": next(chunk_size, 32),
-            "lora": None,
         }
-        self.model = self.wrp.Model(**args)
+        self.model = wrp.Model(**args)
+        self.info = self.model.info()
+        self.w = {}  # fake weight
+        self.w["emb.weight"] = [0] * self.info.num_vocab
+        self.version = str(self.info.version).lower()
+        self.version = float(self.version.lower().replace("v", ""))
 
     def forward(self, tokens: List[int], state: Union[Any, None] = None):
-        if type(state).__name__ == "BackedState":  # memory state
+        if state is None:
-            gpu_state = self.wrp.ModelState(self.model, 1)
+            self.model.clear_state()
-            gpu_state.load(state)
+        elif type(state).__name__ == "State_Cpu":
-        else:
+            self.model.load_state(state)
-            gpu_state = state
+        logits = self.model.run(tokens, self.token_chunk_size)
-        return self.wrp.run_one(self.model, tokens, gpu_state)
+        ret_state = "State_Gpu"
+        return logits, ret_state