solve broadcast two same shape bprop error

make unsupported shape error info explicit

mindspore/ops/_grad/grad_array_ops.py

@@ -673,6 +673,10 @@ def get_bprop_broadcast_to(self):
 
     def bprop(x, out, dout):
         x_shape = shape_op(x)
+        dout_shape = shape_op(dout)
+
+        if x_shape == dout_shape:
+            return (dout,)
         _, reduction_axes = broadcast_gradient_args(broadcast_shape, x_shape)
         reduced_grad = reduce_keep_dim(dout, reduction_axes)
         dx = reshape(reduced_grad, x_shape)

mindspore/ops/operations/array_ops.py

@@ -2719,6 +2719,8 @@ class BatchToSpaceND(PrimitiveWithInfer):
 class BroadcastTo(PrimitiveWithInfer):
     """
     Broadcasts input tensor to a given shape.
+    Input shape can be broadcast to target shape if for each dimension pair they are either equal or input is one.
+    When input shape is broadcast to target shape, it starts with the trailing dimensions.
 
     Args:
         shape (tuple): The target shape to broadcast.
@@ -2741,11 +2743,20 @@ class BroadcastTo(PrimitiveWithInfer):
     def __init__(self, shape):
         """Init BroadcastTo"""
         validator.check_value_type("shape", shape, (tuple), self.name)
+        validator.check("shape length", len(shape), "", 0, Rel.GT, self.name)
         for i in shape:
             validator.check_integer("shape element", i, 0, Rel.GT, self.name)
         self.shape = shape
 
     def infer_shape(self, x_shape):
+        validator.check("input_x shape length", len(x_shape), "target shape", len(self.shape), Rel.LE, self.name)
+
+        reversed_x_shape = tuple(reversed(x_shape))
+        reversed_target = tuple(reversed(self.shape))
+        for i, v in enumerate(reversed_x_shape):
+            if v not in (reversed_target[i], 1):
+                raise ValueError(f"Not supported shapes for broadcast, "
+                                 f"x_shape: {tuple(x_shape)}, target shape {self.shape}.")
         return self.shape
 
     def infer_dtype(self, x_dtype):