diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index 7a3613ad5d..b7dd01b000 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -2167,6 +2167,7 @@ class Concat(PrimitiveWithInfer): x_shp = input_x['shape'] x_type = input_x['dtype'] _, all_shp, _ = get_concat_offset(x_shp, x_type, axis, self.name) + self.add_prim_attr('T', x_type[0].element_type()) self.add_prim_attr('inputNums', len(x_shp)) ret_shp = x_shp[0].copy() value = None @@ -2615,6 +2616,7 @@ class Select(PrimitiveWithInfer): return x_shape def infer_dtype(self, cond_type, x_type, y_type): + self.add_prim_attr('T', x_type) validator.check_subclass("x_type", x_type, mstype.tensor, self.name) validator.check_subclass("y_type", y_type, mstype.tensor, self.name) validator.check_tensor_dtype_valid("cond", cond_type, [mstype.bool_], self.name) diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 03613b1153..23e1353332 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -313,6 +313,7 @@ class _Reduce(PrimitiveWithInfer): """Initialize Reduce""" validator.check_value_type('keep_dims', keep_dims, [bool], self.name) self.init_prim_io_names(inputs=['input_x', 'axis'], outputs=['y']) + self.add_prim_attr("io_format", "ND") def __call__(self, x, axis=()): args = [x, axis] @@ -752,6 +753,7 @@ class MatMul(PrimitiveWithCheck): cls_name = self.name validator.check_value_type("transpose_a", transpose_a, [bool], cls_name) validator.check_value_type("transpose_b", transpose_b, [bool], cls_name) + self.add_prim_attr("io_format", "ND") def check_shape_size(self, x1, x2): if len(x1) != 2 or len(x2) != 2: diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 78fa2b0645..322261e862 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -1457,6 +1457,7 @@ class Conv2D(PrimitiveWithCheck): self.out_channel = validator.check_positive_int(out_channel, 'out_channel', self.name) self.group = validator.check_positive_int(group, 'group', self.name) self.add_prim_attr('groups', self.group) + self.add_prim_attr('offset_a', 0) def check_shape(self, x_shape, w_shape, b_shape=None): x_shape_norm = x_shape if self.format == "NCHW" else (x_shape[0], x_shape[3], x_shape[1], x_shape[2])