!1015 Support weights to be compiled according to shape

Merge pull request !1015 from candanzg/me_with_shape

mindspore/ccsrc/debug/draw.cc

@@ -319,6 +319,10 @@ void BaseDigraph::FuncGraphParameters(const FuncGraphPtr &key) {
         std::shared_ptr<tensor::Tensor> m_tensor = py_p.cast<std::shared_ptr<tensor::Tensor>>();
         py::tuple shape = m_tensor->GetPyTupleShape();
         buffer_ << "[" << std::string(py::str(shape)) << "]";
+      } else if (py::hasattr(py_p, PYTHON_META_TENSOR_FLAG)) {
+        std::shared_ptr<tensor::MetaTensor> m_tensor = py_p.cast<std::shared_ptr<tensor::MetaTensor>>();
+        py::tuple shape = m_tensor->GetPyTupleShape();
+        buffer_ << "[" << std::string(py::str(shape)) << "]";
       }
     }
     buffer_ << "</td></tr>";

mindspore/ccsrc/ir/meta_tensor.cc

@@ -102,6 +102,26 @@ int MetaTensor::DimensionSize(const size_t index) const {
   return dim_size;
 }
 
+abstract::AbstractBasePtr MetaTensor::ToAbstract() {
+  auto tens = shared_from_base<MetaTensor>();
+  auto dtype = tens->Dtype();
+  if (!IsSubType(dtype, kNumber)) {
+    MS_LOG(EXCEPTION) << "Expect MetaTensor type kNumber but got: " << dtype->ToString() << ".";
+  }
+  auto tensor_shape = tens->shape();
+  auto abs_tensor = std::make_shared<abstract::AbstractTensor>(dtype, tensor_shape);
+  abs_tensor->set_value(shared_from_base<MetaTensor>());
+  return abs_tensor;
+}
+
+py::tuple MetaTensor::GetPyTupleShape() const {
+  py::tuple dims(shape_.size());
+  for (size_t i = 0; i < dims.size(); ++i) {
+    dims[i] = py::int_(shape_[i]);
+  }
+  return dims;
+}
+
 int MetaTensor::ElementsNum() const {
   return std::accumulate(shape_.begin(), shape_.end(), 1LL, std::multiplies<int>());
 }
@@ -197,14 +217,6 @@ int Tensor::DataDim() const { return static_cast<int>(data_.ndim()); }
 
 int Tensor::DataSize() const { return static_cast<int>(data_.size()); }
 
-py::tuple Tensor::GetPyTupleShape() const {
-  py::tuple dims(shape_.size());
-  for (size_t i = 0; i < dims.size(); ++i) {
-    dims[i] = py::int_(shape_[i]);
-  }
-  return dims;
-}
-
 py::array Tensor::data() const { return data_; }
 
 int Tensor::data_type_c() const { return static_cast<int>(data_type_); }
@@ -547,7 +559,10 @@ REGISTER_PYBIND_DEFINE(Tensor, ([](const py::module *m) {
                                return tensor;
                              }));
                          (void)py::class_<MetaTensor, std::shared_ptr<MetaTensor>>(*m, "MetaTensor")
-                           .def(py::init<TypePtr, py::tuple>(), py::arg("dtype"), py::arg("shape"));
+                           .def(py::init<TypePtr, py::tuple>(), py::arg("dtype"), py::arg("shape"))
+                           .def_readonly(PYTHON_META_TENSOR_FLAG, &MetaTensor::parse_info_)
+                           .def("dtype", &MetaTensor::Dtype, "Get the MetaTensor's dtype.")
+                           .def("shape", &MetaTensor::GetPyTupleShape, "Get the MetaTensor's shape.");
                        }));
 
 }  // namespace tensor

mindspore/ccsrc/ir/meta_tensor.h

@@ -163,6 +163,8 @@ class MetaTensor : public Value {
   //
   // All the types are defined in "ir/dtype.h".
   TypePtr Dtype() const;
+  abstract::AbstractBasePtr ToAbstract() override;
+  py::tuple GetPyTupleShape() const;
   TypeId data_type() const { return data_type_; }
   std::string ToString() const override;
   std::string DumpText() const override;
@@ -230,6 +232,7 @@ class MetaTensor : public Value {
       return false;
     }
   }
+  const bool parse_info_ = true;
 
  protected:
   // brief Data type of the tensor.
@@ -348,11 +351,6 @@ class Tensor : public MetaTensor {
   // return The total number of elements of the tensor data.
   int DataSize() const;
 
-  // brief Get tensor's shape
-  //
-  // return [py::tuple] The tensor's shape
-  py::tuple GetPyTupleShape() const;
-
   // brief Tensor's data value.
   //
   // return [py::array] The tensor's data in py::array.
@@ -423,6 +421,7 @@ class Tensor : public MetaTensor {
 };
 
 using TensorPtr = std::shared_ptr<Tensor>;
+using MetaTensorPtr = std::shared_ptr<MetaTensor>;
 using TensorPtrList = std::vector<std::shared_ptr<Tensor>>;
 
 }  // namespace tensor

mindspore/ccsrc/pipeline/parse/data_converter.cc

@@ -36,6 +36,8 @@ namespace mindspore {
 namespace parse {
 using Tensor = mindspore::tensor::Tensor;
 using TensorPtr = mindspore::tensor::TensorPtr;
+using MetaTensor = mindspore::tensor::MetaTensor;
+using MetaTensorPtr = mindspore::tensor::MetaTensorPtr;
 
 namespace {
 bool ConvertTuple(const py::object &obj, ValuePtr *const data, bool use_signature) {
@@ -181,6 +183,18 @@ bool ConvertDataType(const py::object &obj, ValuePtr *const data) {
   return true;
 }
 
+bool ConvertMetaTensor(const py::object &obj, ValuePtr *const data) {
+  MS_LOG(DEBUG) << "Converting MetaTensor object.";
+
+  auto m_tensor = obj.cast<MetaTensorPtr>();
+  if (m_tensor == nullptr) {
+    MS_LOG(ERROR) << "Resolve MetaTensor error, get ptr is null.";
+    return false;
+  }
+  *data = m_tensor;
+  return true;
+}
+
 bool ConvertTensor(const py::object &obj, ValuePtr *const data) {
   MS_LOG(DEBUG) << "Converting tensor object";
 
@@ -283,6 +297,8 @@ bool ConvertData(const py::object &obj, ValuePtr *const data, bool use_signature
     ret = ConvertDataType(obj, &converted);
   } else if (py::hasattr(obj, PYTHON_TENSOR_FLAG)) {
     ret = ConvertTensor(obj, &converted);
+  } else if (py::hasattr(obj, PYTHON_META_TENSOR_FLAG)) {
+    ret = ConvertMetaTensor(obj, &converted);
   } else if (py::hasattr(obj, PYTHON_ENVINSTANCE_FLAG)) {
     std::shared_ptr<EnvInstance> env = obj.cast<std::shared_ptr<EnvInstance>>();
     converted = env;

mindspore/ccsrc/pybind_api/export_flags.cc

@@ -20,6 +20,7 @@ namespace mindspore {
 const char PYTHON_PRIMITIVE_FLAG[] = "__primitive_flag__";
 const char PYTHON_METAFUNCGRAPH_FLAG[] = "__metafuncgraph_flag__";
 const char PYTHON_TENSOR_FLAG[] = "__tensor_flag__";
+const char PYTHON_META_TENSOR_FLAG[] = "__meta_tensor_flag__";
 const char PYTHON_ENVINSTANCE_FLAG[] = "__envinstance_flag__";
 const char PYTHON_DTYPE_FLAG[] = "__dtype_flag__";
 const char PYTHON_CELL_AS_LIST[] = "__cell_as_list__";

mindspore/ccsrc/pybind_api/export_flags.h

@@ -22,6 +22,7 @@ namespace mindspore {
 extern const char PYTHON_PRIMITIVE_FLAG[];
 extern const char PYTHON_METAFUNCGRAPH_FLAG[];
 extern const char PYTHON_TENSOR_FLAG[];
+extern const char PYTHON_META_TENSOR_FLAG[];
 extern const char PYTHON_ENVINSTANCE_FLAG[];
 extern const char PYTHON_DTYPE_FLAG[];
 extern const char PYTHON_CELL_AS_LIST[];

mindspore/ccsrc/utils/convert_utils.cc

@@ -71,6 +71,11 @@ py::object ValuePtrToPyData(const ValuePtr &value) {
     py::tuple v(1);
     v[0] = value->cast<tensor::TensorPtr>();
     ret = v[0];
+  } else if (value->isa<tensor::MetaTensor>()) {
+    MS_LOG(DEBUG) << "MetaTensor";
+    py::tuple v(1);
+    v[0] = value->cast<tensor::MetaTensorPtr>();
+    ret = v[0];
   } else if (value->isa<RefKey>()) {
     MS_LOG(DEBUG) << "RefKey";
     py::tuple v(1);

mindspore/common/api.py

@@ -326,6 +326,12 @@ class _Executor:
             raise TypeError('Parameters need OrderedDict type, but got {}'.
                             format(type(params)))
 
+    def _params_init_data(self, obj, params):
+        if params is not None:
+            for _, param in params.items():
+                param.init_data()
+        obj.init_parameters_data()
+
     def compile(self, obj, *args, phase='predict', params=None, do_convert=True, auto_parallel_mode=False):
         """
         Compiles graph.
@@ -371,6 +377,7 @@ class _Executor:
         if not do_convert:
             return phase, True
 
+        self._params_init_data(obj, params)
         if not enable_debug_runtime or enable_ge:
             if auto_parallel_mode:
                 obj.parameter_layout_dict = self._executor.get_parameter_layout(phase)

mindspore/common/initializer.py

@@ -39,6 +39,8 @@ class Initializer:
     """
     def __init__(self, **kwargs):
         self._kwargs = kwargs
+        self.shape = None
+        self.dtype = None
 
     def _initialize(self, *kwargs):
         raise NotImplementedError('Must be overridden!')
@@ -46,6 +48,32 @@ class Initializer:
     def __call__(self, arr):
         return self._initialize(arr)
 
+    @property
+    def shape(self):
+        return self._shape
+
+    @shape.setter
+    def shape(self, shape):
+        self._shape = shape
+
+    @property
+    def dtype(self):
+        return self._dtype
+
+    @dtype.setter
+    def dtype(self, dtype):
+        self._dtype = dtype
+
+    def to_tensor(self):
+        arr = None
+        try:
+            arr = np.ndarray(self.shape)
+        except ValueError:
+            msg = "Error shape={}".format(self.shape)
+            logger.error(msg)
+            raise ValueError(msg)
+        self.__call__(arr)
+        return Tensor(arr, dtype=self.dtype)
 
 def _register(*aliases):
     """Return the alias register."""
@@ -279,13 +307,14 @@ def initializer(init, shape=None, dtype=mstype.float32):
         dtype (:class:`mindspore.dtype`): The type of data in initialized tensor. Default: mindspore.float32.
 
     Returns:
-        Tensor, initialized tensor.
+        Union[Tensor, Initialized], When `init` is Tensor, the return is Tensor object,
+            otherwise the return is Initialize object.
 
     Examples:
         >>> tensor = initializer('ones', [1, 2, 3], mindspore.float32)
     """
     if not isinstance(init, (Tensor, numbers.Number, str, Initializer)):
-        raise TypeError('Unsupported init type.')
+        raise TypeError("Unsupported init type '{}'.".format(type(init)))
 
     if isinstance(init, Tensor):
         init_shape = init.shape()
@@ -295,23 +324,32 @@ def initializer(init, shape=None, dtype=mstype.float32):
                              "the variable shape {}.".format(list(init.shape()), shape))
         return init
 
+    if isinstance(init, str):
+        init_obj = _INITIALIZER_ALIAS[init.lower()]()
+        if init_obj is None:
+            raise ValueError("The class corresponding to '{}' was not found.".format(init))
+        init = init_obj
+
+    if isinstance(shape, list):
+        shape = tuple(shape)
+    elif isinstance(shape, numbers.Number):
+        shape = (shape,)
     try:
-        arr = np.ndarray(shape)
+        np.ndarray(shape)
     except ValueError:
-        msg = "Error shape={}".format(shape)
-        logger.error(msg)
-        raise ValueError(msg)
+        raise ValueError("Error shape={}".format(shape))
+
+    if isinstance(init, Initializer):
+        init.shape = shape
+        init.dtype = dtype
+        return init
 
     if isinstance(init, numbers.Number):
         init_obj = Constant(init)
-    elif isinstance(init, str):
-        init_obj = _INITIALIZER_ALIAS[init.lower()]()
-    else:
-        init_obj = init
-
-    init_obj(arr)
-    return Tensor(arr, dtype=dtype)
-
+        init_obj.shape = shape
+        init_obj.dtype = dtype
+        return init_obj
+    raise TypeError("Unsupported init type '{}'.".format(type(init)))
 
 __all__ = [
     'Initializer',

mindspore/common/parameter.py

@@ -14,9 +14,10 @@
 # ============================================================================
 
 """Parameter for cell."""
+import numbers
 from copy import copy, deepcopy
-from .initializer import initializer
-from .tensor import Tensor
+from .initializer import initializer, Initializer
+from .tensor import Tensor, MetaTensor
 from .._checkparam import _check_str_by_regular
 from ..parallel._utils import _set_clone_info, _CloneInfo
 
@@ -41,7 +42,8 @@ class Parameter:
         Each parameter of Cell is represented by Parameter class.
 
     Args:
-        default_input (Tensor): A parameter tensor.
+        default_input (Union[Tensor, Initializer]): Parameter data, when `default_input` is` Initializer`,
+            the data stored by Parameter is `MetaTensor`, otherwise it is `Tensor`.
         name (str): Name of the child parameter.
         requires_grad (bool): True if the parameter requires gradient. Default: True.
         layerwise_parallel (bool): A kind of model parallel mode. When layerwise_parallel is true in paralle mode,
@@ -123,7 +125,11 @@ class Parameter:
         if init != 'same':
             shape = self.default_input.shape()
             dtype = self.default_input.dtype()
-            x.default_input = initializer(init, shape=shape, dtype=dtype)
+            if isinstance(init, (str, Initializer, numbers.Number)):
+                x.init_mode = initializer(init, shape=shape, dtype=dtype)
+                x.default_input = MetaTensor(dtype, shape)
+            else:
+                x.default_input = initializer(init, shape=shape, dtype=dtype)
 
         x.clone_info = copy(self.clone_info)
         _set_clone_info(self.clone_info, x.clone_info)
@@ -181,11 +187,21 @@ class Parameter:
         if isinstance(data, Tensor):
             # make a copy of Tensor to init the parameter
             data = Tensor(data.asnumpy().copy())
+        elif isinstance(data, Initializer):
+            self.init_mode = data
+            data = MetaTensor(self.init_mode.dtype, self.init_mode.shape)
         else:
             data = Tensor(data)
         self.default_input = data
 
 
+    def init_data(self):
+        if not isinstance(self.default_input, MetaTensor):
+            return
+        self.default_input = self.init_mode.to_tensor()
+        self.init_mode = None
+
+
 class ParameterTuple(tuple):
     """
     Class for storing tuple of parameters.

mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py

@@ -92,7 +92,7 @@ class GetMaskedLMOutput(nn.Cell):
                               config.hidden_size,
                               weight_init=weight_init,
                               activation=config.hidden_act).to_float(config.compute_type)
-        self.layernorm = nn.LayerNorm(config.hidden_size).to_float(config.compute_type)
+        self.layernorm = nn.LayerNorm((config.hidden_size,)).to_float(config.compute_type)
         self.output_bias = Parameter(
             initializer(
                 'zero',

mindspore/model_zoo/Bert_NEZHA/bert_model.py

@@ -190,7 +190,7 @@ class EmbeddingPostprocessor(nn.Cell):
         self.array_mul = P.MatMul()
         self.reshape = P.Reshape()
         self.shape = tuple(embedding_shape)
-        self.layernorm = nn.LayerNorm(embedding_size)
+        self.layernorm = nn.LayerNorm((embedding_size,))
         self.dropout = nn.Dropout(1 - dropout_prob)
         self.gather = P.GatherV2()
         self.use_relative_positions = use_relative_positions
@@ -246,7 +246,7 @@ class BertOutput(nn.Cell):
                               weight_init=TruncatedNormal(initializer_range)).to_float(compute_type)
         self.dropout = nn.Dropout(1 - dropout_prob)
         self.add = P.TensorAdd()
-        self.layernorm = nn.LayerNorm(out_channels).to_float(compute_type)
+        self.layernorm = nn.LayerNorm((out_channels,)).to_float(compute_type)
         self.cast = P.Cast()
 
     def construct(self, hidden_status, input_tensor):
@@ -802,13 +802,13 @@ class CreateAttentionMaskFromInputMask(nn.Cell):
 
         if not self.input_mask_from_dataset:
             self.input_mask = initializer(
-                "ones", [config.batch_size, config.seq_length], mstype.int32)
+                "ones", [config.batch_size, config.seq_length], mstype.int32).to_tensor()
 
         self.cast = P.Cast()
         self.reshape = P.Reshape()
         self.shape = (config.batch_size, 1, config.seq_length)
         self.broadcast_ones = initializer(
-            "ones", [config.batch_size, config.seq_length, 1], mstype.float32)
+            "ones", [config.batch_size, config.seq_length, 1], mstype.float32).to_tensor()
         self.batch_matmul = P.BatchMatMul()
 
     def construct(self, input_mask):
@@ -854,7 +854,7 @@ class BertModel(nn.Cell):
 
         if not self.token_type_ids_from_dataset:
             self.token_type_ids = initializer(
-                "zeros", [self.batch_size, self.seq_length], mstype.int32)
+                "zeros", [self.batch_size, self.seq_length], mstype.int32).to_tensor()
 
         self.bert_embedding_lookup = EmbeddingLookup(
             vocab_size=config.vocab_size,

mindspore/model_zoo/ssd.py

@@ -29,7 +29,7 @@ from .mobilenet import InvertedResidual, ConvBNReLU
 
 def _conv2d(in_channel, out_channel, kernel_size=3, stride=1, pad_mod='same'):
     weight_shape = (out_channel, in_channel, kernel_size, kernel_size)
-    weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32)
+    weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32).to_tensor()
     return nn.Conv2d(in_channel, out_channel, kernel_size=kernel_size, stride=stride,
                      padding=0, pad_mode=pad_mod, weight_init=weight)
 

mindspore/model_zoo/vgg.py

@@ -26,7 +26,7 @@ def _make_layer(base, batch_norm):
             layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
         else:
             weight_shape = (v, in_channels, 3, 3)
-            weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32)
+            weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32).to_tensor()
             conv2d = nn.Conv2d(in_channels=in_channels,
                                out_channels=v,
                                kernel_size=3,

mindspore/nn/cell.py

@@ -163,6 +163,7 @@ class Cell:
         if context.get_context("mode") == context.GRAPH_MODE:
             out = self.compile_and_run(*inputs)
             return out
+        self.init_parameters_data()
         output = self.construct(*inputs)
         if isinstance(output, Parameter):
             output = output.data
@@ -395,6 +396,10 @@ class Cell:
         """
         raise NotImplementedError
 
+    def init_parameters_data(self, recurse=True):
+        for param in self.get_parameters(expand=recurse):
+            param.init_data()
+
     def parameters_dict(self, recurse=True):
         """
         Gets parameters dictionary.

mindspore/nn/layer/normalization.py

@@ -471,6 +471,9 @@ class LayerNorm(Cell):
                  beta_init='zeros',
                  ):
         super(LayerNorm, self).__init__()
+        if not isinstance(normalized_shape, (tuple, list)):
+            raise TypeError("The type of 'normalized_shape' should be tuple[int] or list[int], but '{}' type is {}."
+                            .format(normalized_shape, type(normalized_shape)))
         self.normalized_shape = normalized_shape
         self.begin_norm_axis = begin_norm_axis
         self.begin_params_axis = begin_params_axis

mindspore/train/serialization.py

@@ -116,6 +116,8 @@ def save_checkpoint(parameter_list, ckpoint_file_name):
             param_value = checkpoint_list.value.add()
             param_value.tag = param["name"]
             param_tensor = param_value.tensor
+            if isinstance(param["data"], Parameter):
+                param["data"].init_data()
             param_data = param["data"].asnumpy().reshape(-1)
             param_tensor.tensor_content = param_data.tostring()
             param_tensor.tensor_type = str(param["data"].dtype())
@@ -238,6 +240,7 @@ def load_param_into_net(net, parameter_dict):
                 logger.error("Failed to combine the net and the parameters.")
                 msg = ("Argument parameter_dict element should be a Parameter, but got {}.".format(type(new_param)))
                 raise TypeError(msg)
+            param.init_data()
             _update_param(param, new_param)
         else:
             param_not_load.append(param.name)
@@ -311,6 +314,7 @@ def _exec_save_checkpoint(train_network, ckpoint_file_name, integrated_save=True
     param_list = []
     for (key, value) in param_dict.items():
         each_param = {"name": key}
+        value.init_data()
         if isinstance(value.data, Tensor):
             param_data = value.data
         else:
@@ -371,6 +375,8 @@ def _fill_param_into_net(net, parameter_list):
     parameter_dict = {}
     for each_param in parameter_list:
         param_name = each_param["name"]
+        if isinstance(each_param["data"], Parameter):
+            each_param["data"].init_data()
         np_val = each_param["data"].asnumpy()
         if np_val.shape == (1,):
             parameter_dict[param_name] = Parameter(np_val, name=param_name)

tests/mindspore_test_framework/utils/block_util.py

@@ -35,6 +35,7 @@ def get_uniform_with_shape(shape):
 def set_block_param_with_rand(net, rand_func=None):
     if not isinstance(net, nn.Cell) or rand_func is None:
         return
+    net.init_parameters_data()
     for param in net.trainable_params():
         param.default_input = Tensor(rand_func(param.default_input.asnumpy().shape))
 

tests/st/networks/models/bert/bert_tdt_lossscale.py

@@ -143,6 +143,7 @@ def test_bert_tdt():
     callback = ModelCallback()
     params = netwithloss.trainable_params()
     for param in params:
+        param.init_data()
         value = param.default_input
         name = param.name
         if isinstance(value, Tensor):

tests/ut/python/ir/test_tensor.py

@@ -223,6 +223,7 @@ def test_div():
 @non_graph_engine
 def test_parameter():
     x = Parameter(initializer(1, [1], ms.float32), name="beta1_power")
+    x.init_data()
     z = x / 2
     print(z)
 

tests/ut/python/nn/test_dense.py

@@ -34,7 +34,7 @@ def test_dense_str_activation():
     assert isinstance(dense.activation, nn.ReLU)
 
     input_data = Tensor(np.random.randint(0, 255, [1, 1]).astype(np.float32))
-    dense.construct(input_data)
+    dense(input_data)
 
 
 def test_dense_weight_error():

tests/ut/python/parallel/test_using_seed_for_initializer.py

@@ -40,8 +40,10 @@ class ParameterNet(nn.Cell):
 def test_using_same_seed_for_initializer():
     np.random.seed(0)
     net1 = ParameterNet()
+    net1.init_parameters_data()
     np.random.seed(0)
     net2 = ParameterNet()
+    net2.init_parameters_data()
     for key in net1.parameters_dict():
         if key not in net2.parameters_dict():
             assert False
@@ -52,8 +54,10 @@ def test_using_same_seed_for_initializer():
 def test_using_diffserent_seed_for_initializer():
     np.random.seed(0)
     net1 = ParameterNet()
+    net1.init_parameters_data()
     np.random.seed(1)
     net2 = ParameterNet()
+    net2.init_parameters_data()
     for key in net1.parameters_dict():
         if key not in net2.parameters_dict():
             assert False

tests/ut/python/pynative_mode/nn/test_batchnorm.py

@@ -59,7 +59,7 @@ def test_bn2d():
 
     #3-channel RGB
     input_data = Tensor(np.random.randint(0, 1, [1, 3, 224, 224]).astype(np.float32))
-    output = bn.construct(input_data)
+    output = bn(input_data)
     output_np = output.asnumpy()
     assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
 
@@ -68,7 +68,7 @@ def test_bn1d():
     """ut of nn.BatchNorm1d"""
     bn = nn.BatchNorm1d(3)
     input_data = Tensor(np.random.randint(0, 1, [1, 3, 100, 100]).astype(np.float32))
-    output = bn.construct(input_data)
+    output = bn(input_data)
     output_np = output.asnumpy()
     assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
 

tests/ut/python/pynative_mode/nn/test_conv.py

@@ -27,7 +27,7 @@ kernel_size = 3
 
 def test_check_conv2d_1():
     m = nn.Conv2d(3, 64, 3, bias_init='zeros')
-    output = m.construct(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
+    output = m(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
     output_np = output.asnumpy()
     assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
 
@@ -35,7 +35,7 @@ def test_check_conv2d_1():
 def test_check_conv2d_2():
     Tensor(np.ones([2, 2]))
     m = nn.Conv2d(3, 64, 4, has_bias=False, weight_init='normal')
-    output = m.construct(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
+    output = m(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
     output_np = output.asnumpy()
     assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
 
@@ -43,7 +43,7 @@ def test_check_conv2d_2():
 def test_check_conv2d_3():
     Tensor(np.ones([2, 2]))
     m = nn.Conv2d(3, 64, (3, 3))
-    output = m.construct(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
+    output = m(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
     output_np = output.asnumpy()
     assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
 
@@ -51,13 +51,13 @@ def test_check_conv2d_3():
 def test_check_conv2d_4():
     Tensor(np.ones([2, 2]))
     m = nn.Conv2d(3, 64, (3, 3), stride=2, pad_mode='pad', padding=4)
-    output = m.construct(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
+    output = m(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
     output_np = output.asnumpy()
     assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
 
 
 def test_check_conv2d_bias():
     m = nn.Conv2d(3, 64, 3, bias_init='zeros')
-    output = m.construct(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
+    output = m(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
     output_np = output.asnumpy()
     assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))

tests/ut/python/pynative_mode/nn/test_dense.py

@@ -27,7 +27,7 @@ def test_dense_defaultbias_noactivation():
     assert dense.activation is None
 
     input_data = Tensor(np.random.randint(0, 255, [1, 3]).astype(np.float32))
-    output = dense.construct(input_data)
+    output = dense(input_data)
     output_np = output.asnumpy()
     assert isinstance(output_np[0][0], (np.float32, np.float64))
 
@@ -37,7 +37,7 @@ def test_dense_defaultweight():
     dense = nn.Dense(3, 2, bias_init=bias)
     #batch_size 1 && 3-channel RGB
     input_data = Tensor(np.random.randint(0, 255, [1, 3]).astype(np.float32))
-    output = dense.construct(input_data)
+    output = dense(input_data)
     output_np = output.asnumpy()
     assert isinstance(output_np[0][0], (np.float32, np.float64))
 
@@ -48,7 +48,7 @@ def test_dense_bias():
     dense = nn.Dense(3, 2, weight, bias)
 
     input_data = Tensor(np.random.randint(0, 255, [2, 3]).astype(np.float32))
-    output = dense.construct(input_data)
+    output = dense(input_data)
     output_np = output.asnumpy()
     assert isinstance(output_np[0][0], (np.float32, np.float64))
 
@@ -58,7 +58,7 @@ def test_dense_nobias():
     dense = nn.Dense(3, 2, weight, has_bias=False)
 
     input_data = Tensor(np.random.randint(0, 255, [2, 3]).astype(np.float32))
-    output = dense.construct(input_data)
+    output = dense(input_data)
     output_np = output.asnumpy()
     assert isinstance(output_np[0][0], (np.float32, np.float64))
 
@@ -73,7 +73,7 @@ def test_dense_str_activation():
     assert isinstance(dense.activation, nn.ReLU)
 
     input_data = Tensor(np.random.randint(0, 255, [1, 1]).astype(np.float32))
-    output = dense.construct(input_data)
+    output = dense(input_data)
     output_np = output.asnumpy()
     assert isinstance(output_np[0][0], np.float32)