add rwkv version field

backend-python/rwkv_pip/cpp/rwkv_cpp_model.py

@@ -52,9 +52,14 @@ class RWKVModel:
         if 'gpu_layers_count' in kwargs:
             gpu_layer_count = kwargs['gpu_layers_count']
 
-        assert os.path.isfile(model_path), f'{model_path} is not a file'
-        assert thread_count > 0, 'Thread count must be > 0'
-        assert gpu_layer_count >= 0, 'GPU layer count must be >= 0'
+        if not os.path.isfile(model_path):
+            raise ValueError(f'{model_path} is not a file')
+        
+        if not (thread_count > 0):
+            raise ValueError('Thread count must be > 0')  
+
+        if not (gpu_layer_count >= 0):
+            raise ValueError('GPU layer count must be >= 0')
 
         self._library: rwkv_cpp_shared_library.RWKVSharedLibrary = shared_library
 
@@ -84,10 +89,19 @@ class RWKVModel:
             Count of layers to offload onto the GPU, must be >= 0.
         """
 
-        assert layer_count >= 0, 'Layer count must be >= 0'
+        if not (layer_count >= 0):
+            raise ValueError('Layer count must be >= 0')
 
         return self._library.rwkv_gpu_offload_layers(self._ctx, layer_count)
 
+    @property
+    def arch_version_major(self) -> int:
+        return self._library.rwkv_get_arch_version_major(self._ctx)
+
+    @property
+    def arch_version_minor(self) -> int:
+        return self._library.rwkv_get_arch_version_minor(self._ctx)
+
     @property
     def n_vocab(self) -> int:
         return self._library.rwkv_get_n_vocab(self._ctx)
@@ -133,7 +147,8 @@ class RWKVModel:
             Logits vector of shape (n_vocab); state for the next step.
         """
 
-        assert self._valid, 'Model was freed'
+        if not self._valid:
+            raise ValueError('Model was freed')
 
         use_numpy = self._detect_numpy_usage([state_in, state_out, logits_out], use_numpy)
 
@@ -207,7 +222,8 @@ class RWKVModel:
             Logits vector of shape (n_vocab); state for the next step.
         """
 
-        assert self._valid, 'Model was freed'
+        if not self._valid:
+            raise ValueError('Model was freed')
 
         use_numpy = self._detect_numpy_usage([state_in, state_out, logits_out], use_numpy)
 
@@ -281,7 +297,8 @@ class RWKVModel:
             Logits vector of shape (n_vocab); state for the next step.
         """
 
-        assert self._valid, 'Model was freed'
+        if not self._valid:
+            raise ValueError('Model was freed')
 
         use_numpy = self._detect_numpy_usage([state_in, state_out, logits_out], use_numpy)
 
@@ -320,7 +337,8 @@ class RWKVModel:
         The object must not be used anymore after calling this method.
         """
 
-        assert self._valid, 'Already freed'
+        if not self._valid:
+            raise ValueError('Already freed')
 
         self._valid = False
 
@@ -344,16 +362,25 @@ class RWKVModel:
     def _validate_tensor(self, tensor: NumpyArrayOrPyTorchTensor, name: str, size: int) -> None:
         if self._is_pytorch_tensor(tensor):
             tensor: torch.Tensor = tensor
-            assert tensor.device == torch.device('cpu'), f'{name} is not on CPU'
-            assert tensor.dtype == torch.float32, f'{name} is not of type float32'
-            assert tensor.shape == (size,), f'{name} has invalid shape {tensor.shape}, expected ({size})'
-            assert tensor.is_contiguous(), f'{name} is not contiguous'
+            
+            if tensor.device != torch.device('cpu'):
+                raise ValueError(f'{name} is not on CPU')
+            if tensor.dtype != torch.float32:
+                raise ValueError(f'{name} is not of type float32')
+            if tensor.shape != (size,):
+                raise ValueError(f'{name} has invalid shape {tensor.shape}, expected ({size})')
+            if not tensor.is_contiguous():
+                raise ValueError(f'{name} is not contiguous')
         else:
             import numpy as np
             tensor: np.ndarray = tensor
-            assert tensor.dtype == np.float32, f'{name} is not of type float32'
-            assert tensor.shape == (size,), f'{name} has invalid shape {tensor.shape}, expected ({size})'
-            assert tensor.data.contiguous, f'{name} is not contiguous'
+
+            if tensor.dtype != np.float32:
+                raise ValueError(f'{name} is not of type float32')
+            if tensor.shape != (size,):
+                raise ValueError(f'{name} has invalid shape {tensor.shape}, expected ({size})')
+            if not tensor.data.contiguous:
+                raise ValueError(f'{name} is not contiguous')
 
     def _get_data_ptr(self, tensor: NumpyArrayOrPyTorchTensor):
         if self._is_pytorch_tensor(tensor):