add dynamic shape for ScatterAdd/Update

mindspore/ops/operations/array_ops.py

@@ -69,6 +69,37 @@ class _ScatterOp(PrimitiveWithInfer):
         return x_dtype
 
 
+class _ScatterOp_Dynamic(PrimitiveWithCheck):
+    """
+    Defines Scatter operators with dynamic shape
+    """
+    __mindspore_signature__ = (
+        sig.make_sig('x', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
+        sig.make_sig('indices', dtype=sig.sig_dtype.T1),
+        sig.make_sig('updates', dtype=sig.sig_dtype.T)
+    )
+
+    def _check_scatter_shape(self, x_shape, indices_shape, updates_shape, prim_name):
+        if indices_shape != [-1] and updates_shape and updates_shape != indices_shape + x_shape[1:]:
+            raise ValueError(f"For '{prim_name}', "
+                             f"updates_shape = indices_shape + x_shape[1:], but got x_shape: {x_shape}, "
+                             f"indices_shape: {indices_shape}, updates_shape: {updates_shape}.")
+
+    @prim_attr_register
+    def __init__(self, use_locking=False):
+        """Initialize _ScatterOp_Dynamic"""
+        validator.check_value_type('use_locking', use_locking, [bool], self.name)
+        self.init_prim_io_names(inputs=['x', 'indices', 'updates'], outputs=['y'])
+
+    def check_shape(self, x_shape, indices_shape, updates_shape):
+        self._check_scatter_shape(x_shape, indices_shape, updates_shape, self.name)
+
+    def check_dtype(self, x_dtype, indices_dtype, updates_dtype):
+        validator.check_tensor_dtype_valid('indices', indices_dtype, [mstype.int32], self.name)
+        args = {"x": x_dtype, "updates": updates_dtype}
+        validator.check_tensors_dtypes_same_and_valid(args, mstype.number_type, self.name)
+
+
 class _ScatterNdOp(_ScatterOp):
     """
     Defines _ScatterNd operators
@@ -2723,7 +2754,7 @@ class TensorScatterUpdate(PrimitiveWithInfer):
         return x_dtype
 
 
-class ScatterUpdate(_ScatterOp):
+class ScatterUpdate(_ScatterOp_Dynamic):
     """
     Updates tensor value by using input indices and value.
 
@@ -2757,20 +2788,12 @@ class ScatterUpdate(_ScatterOp):
         [[2.0, 1.2, 1.0],
          [3.0, 1.2, 1.0]]
     """
-
     @prim_attr_register
     def __init__(self, use_locking=True):
         """Initialize ScatterUpdate"""
         validator.check_value_type('use_locking', use_locking, [bool], self.name)
         self.init_prim_io_names(inputs=['x', 'indices', 'updates'], outputs=['y'])
 
-    def infer_dtype(self, x_dtype, indices_dtype, value_dtype):
-        validator.check_tensor_dtype_valid('indices', indices_dtype, [mstype.int32], self.name)
-        args = {"x": x_dtype, "value": value_dtype}
-        validator.check_tensors_dtypes_same_and_valid(args, (mstype.bool_,) + mstype.number_type, self.name)
-        return x_dtype
-
-
 class ScatterNdUpdate(_ScatterNdOp):
     """
     Updates tensor value by using input indices and value.
@@ -2891,7 +2914,7 @@ class ScatterMin(_ScatterOp):
     """
 
 
-class ScatterAdd(_ScatterOp):
+class ScatterAdd(_ScatterOp_Dynamic):
     """
     Updates the value of the input tensor through the add operation.
 
@@ -2923,6 +2946,11 @@ class ScatterAdd(_ScatterOp):
         >>> output = scatter_add(input_x, indices, updates)
         [[1.0, 1.0, 1.0], [3.0, 3.0, 3.0]]
     """
+    @prim_attr_register
+    def __init__(self, use_locking=False):
+        """Initialize ScatterAdd"""
+        validator.check_value_type('use_locking', use_locking, [bool], self.name)
+        self.init_prim_io_names(inputs=['x', 'indices', 'updates'], outputs=['y'])
 
 
 class ScatterSub(_ScatterOp):