add debugging print statements

keras/src/backend/mlx/core.py

@@ -72,63 +72,61 @@ def to_mlx_dtype(dtype):
     return standardized_dtype
 
 
+import logging
+
+logging.basicConfig(level=logging.DEBUG)
+
+
+def debug_print(message):
+    logging.debug(message)
+
+
 def convert_to_tensor(x, dtype=None, sparse=None):
     """Converts the input x to an MLX tensor, handling various input types."""
     try:
-        print(
-            f"Starting conversion: Input type {type(x)}, dtype requested: {dtype}"
+        debug_print(
+            f"Starting conversion: Input type {type(x)}, dtype requested: {dtype}, input: {x}, sparse: {sparse}"
         )
-        print(f"Input: {x}, dtype: {dtype}, sparse: {sparse}")
 
         if sparse:
-            print("`sparse=True` is not supported with mlx backend")
+            debug_print("`sparse=True` is not supported with mlx backend")
             raise ValueError("`sparse=True` is not supported with mlx backend")
 
         mlx_dtype = to_mlx_dtype(dtype) if dtype is not None else None
-        print(f"mlx_dtype: {mlx_dtype}")
+        debug_print(f"mlx_dtype resolved to: {mlx_dtype}")
 
         if is_tensor(x):
-            print("x is a tensor")
-            if dtype is None:
-                return x
-            return x.astype(mlx_dtype)
+            debug_print("Input is already a tensor")
+            return x.astype(mlx_dtype) if dtype else x
 
         if isinstance(x, Variable):
-            print("x is an instance of Variable")
-            if dtype and standardize_dtype(dtype) != x.dtype:
-                return x.value.astype(mlx_dtype)
-            return x.value
+            debug_print("Input is an instance of Variable")
+            return (
+                x.value.astype(mlx_dtype)
+                if dtype and standardize_dtype(dtype) != x.dtype
+                else x.value
+            )
 
         if isinstance(x, np.ndarray):
-            print("x is an instance of np.ndarray")
-            # Ensure compatibility with MLX by converting int64 to int32
+            debug_print("Input is an instance of np.ndarray")
             if x.dtype == np.int64:
                 x = x.astype(np.int32)
-            x = x.astype(standardize_dtype(x.dtype))  # Standardize dtype
+                debug_print("Converted int64 to int32 due to MLX compatibility")
+            x = x.astype(standardize_dtype(x.dtype))
             return mx.array(x, dtype=mlx_dtype)
 
         if isinstance(x, list):
-            print("x is a list")
-
-            def to_scalar_list(x):
-                print(f"Converting to scalar list: {x}")
-                if isinstance(x, list):
-                    return [to_scalar_list(xi) for xi in x]
-                elif isinstance(x, mx.array):
-                    if x.ndim == 0:
-                        return x.item()  # Convert 0-dim array to scalar
-                    else:
-                        return x.tolist()
-                else:
-                    return x
-
-            return mx.array(to_scalar_list(x), dtype=mlx_dtype)
+            debug_print("Input is a list")
+            converted_list = [
+                convert_to_tensor(item, dtype=mlx_dtype) for item in x
+            ]
+            return mx.array(converted_list, dtype=mlx_dtype)
 
-        print("Returning mx.array")
+        debug_print("Returning mx.array for the input")
         return mx.array(x, dtype=mlx_dtype)
 
     except Exception as e:
-        print(f"Failed to convert tensor: {e}")
+        debug_print(f"Failed to convert tensor: {e}")
         raise