TensorDot checking refactor

lintfix

mindspore/ops/composite/math_ops.py

@@ -120,12 +120,14 @@ def _typecheck_input(x1_type, x2_type):
 
 
 @constexpr
-def _validate_input(x1_shape, x2_shape, axes):
+def _axes_int_check(x1_shape, x2_shape, axes):
     """
-    Convert from single int axes to 2d tuple if required and check for validity with inputs.
+    Convert from single int axes to 2d tuple if required
     """
     if isinstance(axes, int):
-        if axes <= 0:
+        if axes < 0:
+            raise ValueError(f"axes must be at least 0 for tensor dot, got {axes}")
+        if axes == 0:
             # outer product, no input validation required
             return ([], [])
         if axes > len(x1_shape) or axes > len(x2_shape):
@@ -135,13 +137,42 @@ def _validate_input(x1_shape, x2_shape, axes):
         x2_ind = tuple(range(len(x2_shape))[:axes])
         axes = tuple((x1_ind, x2_ind))
         axes = _int_to_tuple_conv(axes)
-        for i in range(len(axes[0])):  # sizes already validated
-            if x1_shape[axes[0][i]] != x2_shape[axes[1][i]]:
-                raise ValueError(
-                    "Given Axes are incompatible with given input arrays")
     return axes
 
 
+@constexpr
+def _validate_axes(x1_shape, x2_shape, axes):
+    """
+    Checks for axes having the correct length according to input, for any value in axis
+    being out of range with given shape and also checking for compatiable axes values
+    with given inputs.
+    """
+    shapes = [x1_shape, x2_shape]
+
+    # axis length check
+    for ix_input, x_axes in enumerate(axes):
+        axes_len = len(x_axes)
+        shape_dim_len = len(shapes[ix_input])
+        if axes_len > shape_dim_len:
+            raise ValueError(f"axes for input: {ix_input + 1} are of length: {axes_len} "
+                             f"can only be max: {shape_dim_len} due to input shape.")
+
+    # axis values range check
+    for ix_input, x_axes in enumerate(axes):
+        comp_shape = shapes[ix_input]
+        max_val = len(comp_shape) - 1
+        min_val = -1 * len(comp_shape)
+        for _, x_value in enumerate(x_axes):
+            if not min_val <= x_value <= max_val:
+                raise ValueError(f"axes for input: {ix_input + 1} contains index: "
+                                 f"{x_value}, but range is: [{min_val}, {max_val}]")
+
+    # axis value input compatibility
+    for i in range(len(axes[0])):  # sizes already validated
+        if x1_shape[axes[0][i]] != x2_shape[axes[1][i]]:
+            raise ValueError(
+                "Given Axes are incompatible with given input arrays")
+
 @constexpr
 def _calc_new_shape(shape, axes, position=0):
     """
@@ -208,7 +239,8 @@ def tensor_dot(x1, x2, axes):
     axes = _check_axes(axes)
     _typecheck_input(x1_type, x2_type)
     # input compability check & axes format update
-    axes = _validate_input(x1_shape, x2_shape, axes)
+    axes = _axes_int_check(x1_shape, x2_shape, axes)
+    _validate_axes(x1_shape, x2_shape, axes)
     x1_reshape_fwd, x1_transpose_fwd, x1_ret = _calc_new_shape(x1_shape, axes, 0)
     x2_reshape_fwd, x2_transpose_fwd, x2_ret = _calc_new_shape(x2_shape, axes, 1)
     output_shape = x1_ret + x2_ret  # combine free axes from both inputs