fix bug of sparsegather and broadcastto

mindspore/core/ops/broadcast_to.cc

@@ -48,7 +48,16 @@ abstract::ShapePtr BroadcastToInferShape(const PrimitivePtr &primitive,
       }
     }
   }
-  return std::make_shared<abstract::Shape>(input_x);
+  auto x_shape_ptr = std::make_shared<abstract::Shape>(input_x);
+  primitive->AddAttr("shape", MakeValue(input_x));
+  for (int64_t i = 0; i < (int64_t)x_shape.size(); i++) {
+    if (input_x[i + outer_dim_offset] != x_shape[i] && x_shape[i] != 1) {
+      MS_EXCEPTION(ValueError) << "Not support shapes for broadcast, x_shape: "
+                               << input_args[0]->BuildShape()->ToString()
+                               << ", target shape: " << x_shape_ptr->ToString();
+    }
+  }
+  return x_shape_ptr;
 }
 
 TypePtr BroadcastToInferType(const PrimitivePtr &prim, const std::vector<AbstractBasePtr> &input_args) {

mindspore/ops/_grad/grad_array_ops.py

@@ -980,11 +980,11 @@ def get_bprop_batch_to_space_nd(self):
 def get_bprop_broadcast_to(self):
     """Generate bprop for BroadcastTo"""
     reduce_keep_dim = P.ReduceSum(keep_dims=True)
-    broadcast_shape = self.shape
 
     def bprop(x, out, dout):
         x_shape = shape_op(x)
         dout_shape = shape_op(dout)
+        broadcast_shape = shape_op(out)
 
         if x_shape == dout_shape:
             return (dout,)

mindspore/ops/operations/array_ops.py

@@ -861,7 +861,7 @@ class GatherV2(PrimitiveWithCheck):
         validator.check_int_range(axis_v, -rank, rank, Rel.INC_LEFT, "axis", self.name)
 
 
-class SparseGatherV2(Gather):
+class SparseGatherV2(PrimitiveWithCheck):
     """
     Returns a slice of input tensor based on the specified indices and axis.
 
@@ -893,6 +893,22 @@ class SparseGatherV2(Gather):
          [2. 55.]]
     """
 
+    @prim_attr_register
+    def __init__(self):
+        """Initialize index_select"""
+        self.init_prim_io_names(inputs=['params', 'indices', 'axis'], outputs=['output'])
+
+
+    def __check__(self, params, indices, axis):
+        validator.check_subclass("params", params['dtype'], mstype.tensor, self.name)
+        validator.check_tensor_dtype_valid("indices", indices['dtype'], mstype.int_type, self.name)
+        validator.check_subclass("axis", axis['dtype'], [mstype.number], self.name)
+        axis_v = axis['value']
+        validator.check_value_type('axis', axis_v, [int], self.name)
+        rank = len(params['shape'])
+        validator.check_int_range(axis_v, -rank, rank, Rel.INC_LEFT, "axis", self.name)
+
+
 
 class Padding(PrimitiveWithInfer):
     """