Optimize LazyValues and SparseValues with Caching Mechanism

Signed-off-by: Phoenix <861062923@qq.com>

tools/onnx-graphsurgeon/onnx_graphsurgeon/ir/tensor.py

@@ -231,14 +231,18 @@ def __init__(self, tensor):
         self.shape = get_onnx_tensor_shape(self.tensor)
         self.dtype = get_onnx_tensor_dtype(self.tensor)
         self.nbytes = misc.volume(self.shape) * get_itemsize(self.dtype)
+        self._cached_values = None  # Initialize the cache
 
     def load(self):
         """
-        Load a numpy array from the underlying tensor values.
+        Load a numpy array from the underlying tensor values, using cache.
 
         Returns:
             np.array: A numpy array containing the values of the tensor.
         """
+        if self._cached_values is not None:
+            return self._cached_values  # Return cached data if available
+
         import onnx
         import onnx.numpy_helper
         from onnx_graphsurgeon.importers.onnx_importer import (
@@ -254,7 +258,8 @@ def load(self):
                 f"If this is not what you intended, please avoid accessing the values of this constant tensor."
             )
 
-        return np.array(onnx.numpy_helper.to_array(self.tensor))
+        self._cached_values = np.array(onnx.numpy_helper.to_array(self.tensor))
+        return self._cached_values
 
     def __str__(self):
         return "LazyValues (shape={:}, dtype={:})".format(self.shape, self.dtype)
@@ -268,13 +273,20 @@ class SparseValues(LazyValues):
     A special object that represents constant tensor values that is sparse
     """
 
+    def __init__(self, tensor):
+        super().__init__(tensor)
+        self._cached_values = None  # Initialize the cache
+
     def load(self):
         """
-        Load a numpy array from the sparse structure.
+        Load a numpy array from the sparse structure, using cache.
 
         Returns:
             np.array: A numpy array containing the values of the tensor.
         """
+        if self._cached_values is not None:
+            return self._cached_values  # Return cached data if available
+
         import onnx
         import onnx.numpy_helper
         from onnx_graphsurgeon.importers.onnx_importer import (
@@ -316,7 +328,8 @@ def load(self):
                 f"Unsupported index data dims {self.tensor.indices.dims} in {self.tensor.values.name}"
             )
 
-        return values
+        self._cached_values = values
+        return self._cached_values
 
     def __str__(self):
         return "SparseValues (shape={:}, dtype={:})".format(self.shape, self.dtype)