support scalar input for cell

mindspore/_checkparam.py

@@ -453,7 +453,7 @@ class Validator:
         return padding
 
     @staticmethod
-    def check_subclass(arg_name, type_, template_types, prim_name):
+    def check_subclass(arg_name, type_, template_types, prim_name, addition_error_info=None):
         """Checks whether some type is subclass of another type"""
         if not isinstance(template_types, Iterable):
             template_types = (template_types,)
@@ -467,9 +467,12 @@ class Validator:
                 hit = True
                 break
         if not hit:
+            if addition_error_info is None:
+                addition_error_info = ''
             type_str = (type(type_).__name__ if isinstance(type_, (tuple, list)) else "") + str(type_)
             raise TypeError(f'For \'{prim_name}\', the type of `{arg_name}` should be subclass'
-                            f' of {", ".join((str(x) for x in template_types))}, but got {type_str}.')
+                            f' of {", ".join((str(x) for x in template_types))}, but got {type_str}.'
+                            f' {addition_error_info}')
 
     @staticmethod
     def check_const_input(arg_name, arg_value, prim_name):

mindspore/ccsrc/frontend/operator/composite/composite.cc

@@ -401,7 +401,9 @@ FuncGraphPtr Tail::GenerateSequeueFuncGraph(const abstract::AbstractSequeuePtr &
   }
 
   if (tail_type_ == kGradFirst) {
-    if (sequeue->size() > 1 && (*sequeue)[1] != nullptr && (*sequeue)[1]->isa<abstract::AbstractUndetermined>()) {
+    if (sequeue->size() > 1 && (*sequeue)[1] != nullptr &&
+        ((*sequeue)[1]->isa<abstract::AbstractUndetermined>() ||
+         ((*sequeue)[1]->BuildType() != nullptr && (*sequeue)[1]->BuildType()->isa<Number>()))) {
       ret->set_output(ret->NewCNode({NewValueNode(op), ptrTup, NewValueNode(SizeToLong(1))}));
     } else {
       ret->set_output(NewValueNode(std::make_shared<ValueTuple>(std::vector<ValuePtr>{})));
@@ -413,7 +415,8 @@ FuncGraphPtr Tail::GenerateSequeueFuncGraph(const abstract::AbstractSequeuePtr &
   for (size_t i = 1; i < sequeue->size(); ++i) {
     if (tail_type_ == kGradAll) {
       MS_EXCEPTION_IF_NULL((*sequeue)[i]);
-      if ((*sequeue)[i]->isa<abstract::AbstractUndetermined>()) {
+      if ((*sequeue)[i]->isa<abstract::AbstractUndetermined>() ||
+          ((*sequeue)[i]->BuildType() != nullptr && (*sequeue)[i]->BuildType()->isa<Number>())) {
         elems.push_back(ret->NewCNodeInOrder({NewValueNode(op), ptrTup, NewValueNode(SizeToLong(i))}));
       }
     } else {

mindspore/ccsrc/frontend/optimizer/cse.cc

@@ -224,7 +224,8 @@ void DeleteLoadUserUpdateState(const FuncGraphManagerPtr &manager, const AnfNode
 // b = Load(para1, u2)
 // u3 = UpdateState(u2, x)
 void DeleteLoadUserMakeTuple(const FuncGraphManagerPtr &manager, const CNodePtr &make_tuple, const AnfNodePtr &load) {
-  AnfNodePtr other_input = nullptr;
+  // Initialize the other_input with load in case of all the inputs of the make_tuple is the same load.
+  AnfNodePtr other_input = load;
   for (size_t i = 1; i < make_tuple->size(); i++) {
     if (make_tuple->input(i) != load) {
       other_input = make_tuple->input(i);

mindspore/ccsrc/pipeline/jit/pass.cc

@@ -489,7 +489,8 @@ void UpdateFuncGraphParameter(const FuncGraphPtr &func_graph) {
       continue;
     }
     AbstractBasePtr par_abs = param_node->abstract();
-    if (par_abs->isa<abstract::AbstractUndetermined>()) {
+    if (par_abs->isa<abstract::AbstractUndetermined>() ||
+        (par_abs->BuildType() != nullptr && par_abs->BuildType()->isa<Number>())) {
       new_paras.push_back(param_node);
     }
   }

mindspore/ccsrc/pipeline/jit/pipeline.cc

@@ -98,7 +98,7 @@ std::string GetBaseNameForIR(int64_t stage_idx, const std::string &action_name)
 
 AbstractBasePtr ArgsToAbstract(const ValuePtr &value) {
   MS_EXCEPTION_IF_NULL(value);
-  bool broaden = value->isa<MetaTensor>();
+  bool broaden = value->isa<MetaTensor>() || value->isa<Scalar>();
   return abstract::FromValue(value, broaden);
 }
 
@@ -142,6 +142,21 @@ std::string GetCompileExceptionInfo() {
   return oss.str();
 }
 
+void SetGpuLoopSink(const ResourcePtr &resource_) {
+  auto func_graph = resource_->func_graph();
+  if (func_graph != nullptr && func_graph->manager() != nullptr) {
+    auto manager = func_graph->manager();
+    size_t graph_nums = manager->func_graphs().size();
+    int64_t sinksize = ConfigManager::GetInstance().iter_num();
+    if (graph_nums == 1) {
+      resource_->set_gpu_loopsink(true, sinksize);
+    } else {
+      resource_->set_gpu_loopsink(false, sinksize);
+    }
+    MS_LOG(INFO) << "Change gpu_loopsink_flag_ to " << resource_->gpu_loopsink_flag() << ", set loopsink size to "
+                 << sinksize;
+  }
+}
 }  // namespace
 
 py::tuple GenerateKey(const std::string &name, const std::unordered_map<std::string, py::object> &defaults) {
@@ -704,19 +719,7 @@ void Pipeline::Run() {
         MS_LOG(DEBUG) << "Action " << action.first << " end.";
       };
       if (action.first == "task_emit") {
-        auto func_graph = resource_->func_graph();
-        if (func_graph != nullptr && func_graph->manager() != nullptr) {
-          auto manager = func_graph->manager();
-          size_t graph_nums = manager->func_graphs().size();
-          int64_t sinksize = ConfigManager::GetInstance().iter_num();
-          if (graph_nums == 1) {
-            resource_->set_gpu_loopsink(true, sinksize);
-          } else {
-            resource_->set_gpu_loopsink(false, sinksize);
-          }
-          MS_LOG(INFO) << "Change gpu_loopsink_flag_ to " << resource_->gpu_loopsink_flag() << ", set loopsink size to "
-                       << sinksize;
-        }
+        SetGpuLoopSink(resource_);
       }
       if (!result) {
         MS_LOG(EXCEPTION) << "Pipeline running to end, failed in step:" << action.first;

mindspore/common/api.py

@@ -210,7 +210,7 @@ class _MindSporeFunction:
             return None
         new_inputs = []
         for i in args_list:
-            if isinstance(i, Tensor):
+            if isinstance(i, (Tensor, int, float)):
                 new_inputs.append(i)
         return self._executor(tuple(new_inputs), phase)
 

mindspore/core/abstract/abstract_value.cc

@@ -88,7 +88,7 @@ std::string AbstractBase::ToString() const {
   return buffer.str();
 }
 
-AbstractBasePtr AbstractScalar::Broaden(uint8_t config) const { return Clone(); }
+AbstractBasePtr AbstractScalar::Broaden(uint8_t config) const { return AbstractBase::Broaden(config); }
 
 AbstractBasePtr AbstractScalar::Join(const AbstractBasePtr &other) {
   MS_EXCEPTION_IF_NULL(other);

mindspore/core/abstract/prim_others.cc

@@ -171,10 +171,6 @@ AbstractBasePtr InferImplDepend(const AnalysisEnginePtr &, const PrimitivePtr &p
     return args_spec_list[0];
   }
   auto depends = args_spec_list[0]->Broaden();
-  // For scalar, need to set value to kAnyValue, because broaden scalar will not change the value.
-  if (depends->isa<AbstractScalar>()) {
-    depends->set_value(kAnyValue);
-  }
   return depends;
 }
 

mindspore/nn/cell.py

@@ -609,7 +609,7 @@ class Cell(Cell_):
 
         new_inputs = []
         for i in inputs:
-            if isinstance(i, Tensor):
+            if isinstance(i, (Tensor, int, float)):
                 new_inputs.append(i)
 
         if self._auto_parallel_mode:

mindspore/nn/wrap/cell_wrapper.py

@@ -199,10 +199,10 @@ class ForwardValueAndGrad(Cell):
             If the sensor_param is True, a sensitivity (gradient with respect to output) needs to be transferred through
             the location parameter or key-value pair parameter. If the value is transferred through the key-value pair
             parameter, the key must be sens.
+        sens (Number): The scaling number to be filled as the input of backpropagation. Default value is 1.0.
 
     Inputs:
         - **(\*inputs)** (Tuple(Tensor)) - Tuple of input tensors with shape :math:`(N, \ldots)`.
-        - sens (Number): The scaling number to be filled as the input of backpropagation. Default value is 1.0.
 
     Outputs:
         - **forward value** (a scalar Tensor with shape :math:`()`) - The result of network forward running.
@@ -242,7 +242,7 @@ class ForwardValueAndGrad(Cell):
         >>> loss, grads = forward_value_and_grad(inputs, labels, 1.0)
     """
 
-    def __init__(self, network, weights=None, get_all=False, get_by_list=False, sens_param=False):
+    def __init__(self, network, weights=None, get_all=False, get_by_list=False, sens_param=False, sens=1.0):
         super(ForwardValueAndGrad, self).__init__(auto_prefix=False)
         if not isinstance(network, (Cell, FunctionType, MethodType)):
             raise TypeError(f"The type of training network should be cell, function type or method type, "
@@ -259,19 +259,16 @@ class ForwardValueAndGrad(Cell):
         self.get_all = get_all
         self.get_by_list = get_by_list
         self.sens_param = sens_param
+        self.sens = sens
         self.grad = C.GradOperation(get_all=self.get_all, get_by_list=self.get_by_list, sens_param=self.sens_param)
 
     def construct(self, *inputs):
         weights = self.weights
-        if self.sens_param:
-            sens = inputs[-1]
-            inputs = inputs[:-1]
-        else:
-            sens = None
         loss = self.network(*inputs)
         if self.sens_param:
-            if not isinstance(sens, Tensor):
-                sens = P.Fill()(P.DType()(loss), P.Shape()(loss), sens)
+            sens = self.sens
+            if not isinstance(self.sens, Tensor):
+                sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
             grads = self.grad(self.network, weights)(*inputs, sens)
         else:
             grads = self.grad(self.network, weights)(*inputs)

mindspore/ops/operations/array_ops.py

@@ -223,7 +223,8 @@ class DType(PrimitiveWithInfer):
         """Initialize DType"""
 
     def __infer__(self, x):
-        validator.check_subclass("input_x", x['dtype'], mstype.tensor, self.name)
+        addition_error_info = 'Perhaps you are using a mixture of tensors and scalars to operate.'
+        validator.check_subclass("input_x", x['dtype'], mstype.tensor, self.name, addition_error_info)
         out = {'shape': (),
                'dtype': mstype.type_type,
                'value': x['dtype'].element_type()}

tests/st/networks/test_gpu_resnet.py

@@ -414,13 +414,14 @@ def test_trainTensor_with_new_interface(num_classes=10, epoch=8, batch_size=1):
     weights = ParameterTuple(filter(lambda x: x.requires_grad, net.get_parameters()))
     optimizer = Momentum(weights, 0.1, 0.9)
 
-    train_network = ForwardValueAndGrad(network=net_with_criterion, weights=weights, get_by_list=True, sens_param=True)
+    train_network = ForwardValueAndGrad(network=net_with_criterion, weights=weights, get_by_list=True, sens_param=True,
+                                        sens=1.0)
     losses = []
     for i in range(0, epoch):
         data = Tensor(np.ones([batch_size, 3, 224, 224]
                               ).astype(np.float32) * 0.01)
         label = Tensor(np.ones([batch_size]).astype(np.int32))
-        loss, grads = train_network(data, label, 1.0)
+        loss, grads = train_network(data, label)
         grads = F.identity(grads)
         optimizer(grads)
         losses.append(loss)
@@ -439,13 +440,14 @@ def test_big_batchSize_with_new_interface(num_classes=10, epoch=8, batch_size=33
     weights = ParameterTuple(filter(lambda x: x.requires_grad, net.get_parameters()))
     optimizer = Momentum(weights, 0.1, 0.9)
 
-    train_network = ForwardValueAndGrad(network=net_with_criterion, weights=weights, get_by_list=True, sens_param=True)
+    train_network = ForwardValueAndGrad(network=net_with_criterion, weights=weights, get_by_list=True, sens_param=True,
+                                        sens=1.0)
     losses = []
     for i in range(0, epoch):
         data = Tensor(np.ones([batch_size, 3, 224, 224]
                               ).astype(np.float32) * 0.01)
         label = Tensor(np.ones([batch_size]).astype(np.int32))
-        loss, grads = train_network(data, label, 1.0)
+        loss, grads = train_network(data, label)
         grads = F.identity(grads)
         optimizer(grads)
         losses.append(loss)

tests/st/ops/gpu/test_reduce_all_op.py

@@ -95,15 +95,23 @@ def test_ReduceAll():
     assert output[3].shape == expect3.shape
 
 
+x_1 = np.array([[True, True], [True, False], [False, False]])
+axis_1 = 0
+x_2 = np.array([[True, True], [True, True], [True, False], [False, False]])
+axis_2 = 0
+
+
 class ReduceAllDynamic(nn.Cell):
-    def __init__(self):
+    def __init__(self, x, axis):
         super(ReduceAllDynamic, self).__init__()
         self.reduceall = P.ReduceAll(False)
         self.test_dynamic = inner.GpuConvertToDynamicShape()
+        self.x = x
+        self.axis = axis
 
-    def construct(self, x, axis):
-        x = self.test_dynamic(x)
-        return self.reduceall(x, axis)
+    def construct(self):
+        dynamic_x = self.test_dynamic(self.x)
+        return self.reduceall(dynamic_x, self.axis)
 
 
 @pytest.mark.level0
@@ -111,18 +119,14 @@ class ReduceAllDynamic(nn.Cell):
 @pytest.mark.env_onecard
 def test_reduce_all_dynamic():
     context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-    net = ReduceAllDynamic()
+    net1 = ReduceAllDynamic(Tensor(x_1), axis_1)
+    net2 = ReduceAllDynamic(Tensor(x_2), axis_2)
 
-    x_1 = np.array([[True, True], [True, False], [False, False]])
-    axis_1 = 0
     expect_1 = np.all(x_1, axis=axis_1, keepdims=False)
-
-    x_2 = np.array([[True, True], [True, True], [True, False], [False, False]])
-    axis_2 = 0
     expect_2 = np.all(x_2, axis=axis_2, keepdims=False)
 
-    output_1 = net(Tensor(x_1), axis_1)
-    output_2 = net(Tensor(x_2), axis_2)
+    output1 = net1()
+    output2 = net2()
 
-    np.testing.assert_almost_equal(output_1.asnumpy(), expect_1)
-    np.testing.assert_almost_equal(output_2.asnumpy(), expect_2)
+    np.testing.assert_almost_equal(output1.asnumpy(), expect_1)
+    np.testing.assert_almost_equal(output2.asnumpy(), expect_2)

tests/st/ops/gpu/test_reduce_any_op.py

@@ -95,15 +95,23 @@ def test_ReduceAny():
     assert output[3].shape == expect3.shape
 
 
+x_1 = np.array([[True, True], [True, False], [False, False]])
+axis_1 = 0
+x_2 = np.array([[True, True], [True, True], [True, False], [False, False]])
+axis_2 = 0
+
+
 class ReduceAnyDynamic(nn.Cell):
-    def __init__(self):
+    def __init__(self, x, axis):
         super(ReduceAnyDynamic, self).__init__()
         self.reduceany = P.ReduceAny(False)
         self.test_dynamic = inner.GpuConvertToDynamicShape()
+        self.x = x
+        self.axis = axis
 
-    def construct(self, x, axis):
-        x = self.test_dynamic(x)
-        return self.reduceany(x, axis)
+    def construct(self):
+        dynamic_x = self.test_dynamic(self.x)
+        return self.reduceany(dynamic_x, self.axis)
 
 
 @pytest.mark.level0
@@ -111,18 +119,14 @@ class ReduceAnyDynamic(nn.Cell):
 @pytest.mark.env_onecard
 def test_reduce_any_dynamic():
     context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-    net = ReduceAnyDynamic()
+    net1 = ReduceAnyDynamic(Tensor(x_1), axis_1)
+    net2 = ReduceAnyDynamic(Tensor(x_2), axis_2)
 
-    x_1 = np.array([[True, True], [True, False], [False, False]])
-    axis_1 = 0
     expect_1 = np.any(x_1, axis=axis_1, keepdims=False)
-
-    x_2 = np.array([[True, True], [True, True], [True, False], [False, False]])
-    axis_2 = 0
     expect_2 = np.any(x_2, axis=axis_2, keepdims=False)
 
-    output_1 = net(Tensor(x_1), axis_1)
-    output_2 = net(Tensor(x_2), axis_2)
+    output1 = net1()
+    output2 = net2()
 
-    np.testing.assert_almost_equal(output_1.asnumpy(), expect_1)
-    np.testing.assert_almost_equal(output_2.asnumpy(), expect_2)
+    np.testing.assert_almost_equal(output1.asnumpy(), expect_1)
+    np.testing.assert_almost_equal(output2.asnumpy(), expect_2)

tests/st/ops/gpu/test_reduce_max_op.py

@@ -179,36 +179,41 @@ def test_ReduceMax():
     assert np.all(diff8 < error8)
 
 
+x_1 = x8
+axis_1 = 0
+x_2 = x1
+axis_2 = 0
+
+
 class ReduceMaxDynamic(nn.Cell):
-    def __init__(self):
+    def __init__(self, x, axis):
         super(ReduceMaxDynamic, self).__init__()
         self.reducemax = P.ReduceMax(False)
         self.test_dynamic = inner.GpuConvertToDynamicShape()
+        self.x = x
+        self.axis = axis
 
-    def construct(self, x, axis):
-        x = self.test_dynamic(x)
-        return self.reducemax(x, axis)
+    def construct(self):
+        dynamic_x = self.test_dynamic(self.x)
+        return self.reducemax(dynamic_x, self.axis)
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_reduce_max_dynamic():
     context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-    net = ReduceMaxDynamic()
+    net1 = ReduceMaxDynamic(Tensor(x_1), axis_1)
+    net2 = ReduceMaxDynamic(Tensor(x_2), axis_2)
 
-    x_1 = x8
-    axis_1 = 0
     expect_1 = np.max(x_1, axis=0, keepdims=False)
-
-    x_2 = x1
-    axis_2 = 0
     expect_2 = np.max(x_2, axis=0, keepdims=False)
 
-    output_1 = net(Tensor(x_1), axis_1)
-    output_2 = net(Tensor(x_2), axis_2)
+    output1 = net1()
+    output2 = net2()
+
+    np.testing.assert_almost_equal(output1.asnumpy(), expect_1)
+    np.testing.assert_almost_equal(output2.asnumpy(), expect_2)
 
-    np.testing.assert_almost_equal(output_1.asnumpy(), expect_1)
-    np.testing.assert_almost_equal(output_2.asnumpy(), expect_2)
 
 class ReduceMaxTypeNet(nn.Cell):
     def __init__(self, nptype):

tests/st/ops/gpu/test_reduce_mean_op.py

@@ -268,14 +268,16 @@ def test_ReduceMean():
     assert output[14].shape == expect14.shape
 
 class ReduceMeanDynamic(nn.Cell):
-    def __init__(self, keepdims=False):
+    def __init__(self, x, axis, keepdims=False):
         super(ReduceMeanDynamic, self).__init__()
         self.test_dynamic = inner.GpuConvertToDynamicShape()
         self.reducemean = P.ReduceMean(keep_dims=keepdims)
+        self.x = x
+        self.axis = axis
 
-    def construct(self, input_x, axis):
-        input_x = self.test_dynamic(input_x)
-        output = self.reducemean(input_x, axis)
+    def construct(self):
+        dynamic_x = self.test_dynamic(self.x)
+        output = self.reducemean(dynamic_x, self.axis)
         return output
 
 @pytest.mark.level0
@@ -283,32 +285,30 @@ class ReduceMeanDynamic(nn.Cell):
 @pytest.mark.env_onecard
 def test_dynamic_reduce_mean_keepdims_true():
     context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-    net = ReduceMeanDynamic(keepdims=True)
-    x_tensor_1 = Tensor(x14)
-    output_1 = net(x_tensor_1, axis14)
-    x_tensor_2 = Tensor(x0)
-    output_2 = net(x_tensor_2, axis0)
+    net1 = ReduceMeanDynamic(Tensor(x14), axis14, keepdims=True)
+    net2 = ReduceMeanDynamic(Tensor(x0), axis0, keepdims=True)
+    output1 = net1()
+    output2 = net2()
 
     expect_1 = np.mean(x14, axis=np_axis14, keepdims=True)
-    diff_1 = abs(output_1.asnumpy() - expect_1)
+    diff_1 = abs(output1.asnumpy() - expect_1)
     error_1 = np.ones(shape=expect_1.shape) * 1.0e-5
     assert np.all(diff_1 < error_1)
-    assert output_1.shape == expect_1.shape
+    assert output1.shape == expect_1.shape
 
     expect_2 = np.mean(x0, axis=axis0, keepdims=True)
-    diff_2 = abs(output_2.asnumpy() - expect_2)
+    diff_2 = abs(output2.asnumpy() - expect_2)
     error_2 = np.ones(shape=expect_2.shape) * 1.0e-5
     assert np.all(diff_2 < error_2)
-    assert output_2.shape == expect_2.shape
+    assert output2.shape == expect_2.shape
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_dynamic_reduce_mean_keepdims_false():
     context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-    net = ReduceMeanDynamic(keepdims=False)
-    x_tensor = Tensor(x12)
-    output = net(x_tensor, axis12)
+    net = ReduceMeanDynamic(Tensor(x12), axis12, keepdims=False)
+    output = net()
 
     expect = np.mean(x12, axis=axis12, keepdims=False)
     diff = abs(output.asnumpy() - expect)

tests/st/ops/gpu/test_reduce_min_op.py

@@ -179,33 +179,37 @@ def test_ReduceMin():
     assert np.all(diff8 < error8)
 
 
+x_1 = x8
+axis_1 = 0
+x_2 = x1
+axis_2 = 0
+
+
 class ReduceMinDynamic(nn.Cell):
-    def __init__(self):
+    def __init__(self, x, axis):
         super(ReduceMinDynamic, self).__init__()
         self.reducemin = P.ReduceMin(False)
         self.test_dynamic = inner.GpuConvertToDynamicShape()
+        self.x = x
+        self.axis = axis
 
-    def construct(self, x, axis):
-        x = self.test_dynamic(x)
-        return self.reducemin(x, axis)
+    def construct(self):
+        dynamic_x = self.test_dynamic(self.x)
+        return self.reducemin(dynamic_x, self.axis)
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_reduce_min_dynamic():
     context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-    net = ReduceMinDynamic()
+    net1 = ReduceMinDynamic(Tensor(x_1), axis_1)
+    net2 = ReduceMinDynamic(Tensor(x_2), axis_2)
 
-    x_1 = x8
-    axis_1 = 0
     expect_1 = np.min(x_1, axis=0, keepdims=False)
-
-    x_2 = x1
-    axis_2 = 0
     expect_2 = np.min(x_2, axis=0, keepdims=False)
 
-    output_1 = net(Tensor(x_1), axis_1)
-    output_2 = net(Tensor(x_2), axis_2)
+    output1 = net1()
+    output2 = net2()
 
-    np.testing.assert_almost_equal(output_1.asnumpy(), expect_1)
-    np.testing.assert_almost_equal(output_2.asnumpy(), expect_2)
+    np.testing.assert_almost_equal(output1.asnumpy(), expect_1)
+    np.testing.assert_almost_equal(output2.asnumpy(), expect_2)

tests/st/ops/gpu/test_reduce_sum_op.py

@@ -270,15 +270,23 @@ def test_ReduceSum():
     assert output[14].shape == expect14.shape
 
 
+x_1 = x8
+axis_1 = 0
+x_2 = x1
+axis_2 = 0
+
+
 class ReduceSumDynamic(nn.Cell):
-    def __init__(self):
+    def __init__(self, x, axis):
         super(ReduceSumDynamic, self).__init__()
         self.reducesum = P.ReduceSum(True)
         self.test_dynamic = inner.GpuConvertToDynamicShape()
+        self.x = x
+        self.axis = axis
 
-    def construct(self, x, axis):
-        x = self.test_dynamic(x)
-        return self.reducesum(x, axis)
+    def construct(self):
+        dynamic_x = self.test_dynamic(self.x)
+        return self.reducesum(dynamic_x, self.axis)
 
 
 @pytest.mark.level0
@@ -286,21 +294,18 @@ class ReduceSumDynamic(nn.Cell):
 @pytest.mark.env_onecard
 def test_reduce_sum_dynamic():
     context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-    net = ReduceSumDynamic()
+    net1 = ReduceSumDynamic(Tensor(x_1), axis_1)
+    net2 = ReduceSumDynamic(Tensor(x_2), axis_2)
 
-    x_1 = x8
-    axis_1 = 0
     expect_1 = np.sum(x_1, axis=axis_1, keepdims=True)
-
-    x_2 = x1
-    axis_2 = 0
     expect_2 = np.sum(x_2, axis=axis_2, keepdims=True)
 
-    output_1 = net(Tensor(x_1), axis_1)
-    output_2 = net(Tensor(x_2), axis_2)
+    output1 = net1()
+    output2 = net2()
+
+    np.testing.assert_almost_equal(output1.asnumpy(), expect_1)
+    np.testing.assert_almost_equal(output2.asnumpy(), expect_2)
 
-    np.testing.assert_almost_equal(output_1.asnumpy(), expect_1)
-    np.testing.assert_almost_equal(output_2.asnumpy(), expect_2)
 
 class ReduceSumTypeNet(nn.Cell):
     def __init__(self, nptype):

tests/ut/cpp/abstract/utils_test.cc

@@ -32,18 +32,26 @@ TEST_F(TestUtils, test_join) {
   AbstractBasePtr abs_s1 = FromValue(static_cast<int64_t>(1), false);
   AbstractBasePtr abs_s2 = FromValue(static_cast<int64_t>(2), false);
   AbstractBasePtr abs_s_anything = FromValue(static_cast<int64_t>(2), true);
-  abs_s_anything->set_value(kAnyValue);
 
   AbstractBasePtr res_s1 = abs_s1->Join(abs_s2);
   ASSERT_EQ(*res_s1, *abs_s_anything);
 
+  // AbstractTuple join;
+  std::vector<int64_t> list1 = {1, 2, 3, 4, 5};
+  std::vector<int64_t> list2 = {5, 4, 3, 2, 1};
+  AbstractBasePtr abs_t1 = FromValue(list1, true);
+  AbstractBasePtr abs_t2 = FromValue(list2, true);
+
+  AbstractBasePtr res_t1 = abs_t1->Join(abs_t2);
+  ASSERT_EQ(res_t1, abs_t1);
+
   abs_s1 = FromValue(static_cast<int64_t>(1), false);
 
   AbstractBasePtr t1 = std::make_shared<AbstractTuple>(AbstractBasePtrList({abs_s1, abs_s_anything}));
   AbstractBasePtr t2 = std::make_shared<AbstractTuple>(AbstractBasePtrList({abs_s1, abs_s_anything}));
   AbstractBasePtr t3 = std::make_shared<AbstractTuple>(AbstractBasePtrList({abs_s_anything, abs_s_anything}));
 
-  AbstractBasePtr res_t1 = t1->Join(t2);
+  res_t1 = t1->Join(t2);
   ASSERT_EQ(res_t1, t1);
 
   res_t1 = t1->Join(t3);

tests/ut/cpp/optimizer/lib_test.cc

@@ -111,11 +111,8 @@ TEST_F(TestOptLib, test_inline) {
   // add infer and renormalize
   std::shared_ptr<mindspore::pipeline::Resource> res = std::make_shared<mindspore::pipeline::Resource>();
   AbstractBasePtrList args_spec_list;
-  tensor::TensorPtr x_tensor = std::make_shared<tensor::Tensor>(kFloat32->type_id(), std::vector<int64_t>{2, 3});
-  tensor::TensorPtr y_tensor = std::make_shared<tensor::Tensor>(kFloat32->type_id(), std::vector<int64_t>{2, 3});
-
-  AbstractBasePtr abstract_v1 = abstract::FromValue(x_tensor, true);
-  AbstractBasePtr abstract_v2 = abstract::FromValue(y_tensor, true);
+  AbstractBasePtr abstract_v1 = abstract::FromValue(static_cast<int64_t>(1), true);
+  AbstractBasePtr abstract_v2 = abstract::FromValue(static_cast<int64_t>(2), true);
   args_spec_list.push_back(abstract_v1);
   args_spec_list.push_back(abstract_v2);
   AnalysisResult result = pipeline::AbstractAnalyze(res, before1, args_spec_list);

tests/ut/cpp/pipeline/static_analysis/data_test.cc

@@ -184,7 +184,7 @@ TEST_F(TestData, test_broaden) {
   AbstractBasePtr s2 = s1->Broaden();
   ASSERT_TRUE(*s1->GetTypeTrack() == *s2->GetTypeTrack());
   ASSERT_TRUE(*s1->GetValueTrack() == *MakeValue(int1));
-  ASSERT_TRUE(s2->GetValueTrack()->isa<Int64Imm>());
+  ASSERT_TRUE(s2->GetValueTrack()->isa<AnyValue>());
 
   AbstractFunctionPtr f1 = std::make_shared<FuncGraphAbstractClosure>(std::make_shared<FuncGraph>(),
                                                                       AnalysisContext::DummyContext());
@@ -196,7 +196,7 @@ TEST_F(TestData, test_broaden) {
   AbstractList* l2_cast = dynamic_cast<AbstractList*>(l2.get());
   ASSERT_TRUE(l2_cast != nullptr);
   AbstractBasePtr csr = AbstractJoin(l2_cast->elements());
-  ASSERT_TRUE(csr->GetValueTrack()->isa<Int64Imm>());
+  ASSERT_TRUE(csr->GetValueTrack()->isa<AnyValue>());
 }
 
 }  // namespace abstract

tests/ut/python/ops/test_tensor_slice.py

@@ -20,7 +20,6 @@ from mindspore import Tensor, Parameter
 from mindspore import context
 from mindspore import dtype as mstype
 from mindspore.nn import Cell
-from mindspore.ops import operations as P
 from ....mindspore_test_framework.mindspore_test import mindspore_test
 from ....mindspore_test_framework.pipeline.forward.compile_forward \
     import pipeline_for_compile_forward_ge_graph_for_case_by_case_config, \
@@ -684,27 +683,6 @@ def test_tensor_assign_bool_index():
         net4(Ta, Tensor(u_scalar, mstype.int32))
 
 
-def test_trivial_call_function_twice_with_diff_key_value_para():
-    class Net(Cell):
-        def __init__(self):
-            super(Net, self).__init__()
-            self.arange = Tensor(np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))
-            self.concat = P.Concat(axis=0)
-
-        def compute(self, x, is_decoder):
-            if is_decoder:
-                return self.arange[:x]
-            return self.arange[1:x + 1]
-
-        def construct(self):
-            result1 = self.compute(7, is_decoder=True)
-            result2 = self.compute(6, is_decoder=False)
-            return self.concat((result1, result2))
-
-    net = Net()
-    net()
-
-
 test_cases = [
     ('TensorAssignWithTupleEllipsis2', {
         'block': TensorAssignWithTupleEllipsis2(),

tests/ut/python/pipeline/parse/test_ms_function_pass_non_tensor_inputs.py

@@ -19,7 +19,7 @@ from mindspore import Tensor, ms_function
 from mindspore import context
 from mindspore.ops import operations as P
 
-context.set_context(mode=context.PYNATIVE_MODE, save_graphs=True)
+context.set_context(mode=context.PYNATIVE_MODE)
 
 
 @ms_function
@@ -33,8 +33,7 @@ def test_scalar_compute():
     p = (3, 4)
     q = [5, 6]
     w = {"x": 7, "y": 8}
-    ret = compute(int_x, int_y, p, q, w)
-    assert ret == -1
+    compute(int_x, int_y, p, q, w)
 
 
 def test_tensor_compute():

tests/ut/python/pipeline/parse/test_outermost_net_pass_non_tensor_inputs.py

@@ -45,6 +45,17 @@ class GradNet(nn.Cell):
         return self.grad_all(self.forward_net)(tuple_a, tensor_x, list_b, tensor_y, scalar, dict_c, flag)
 
 
+class GradNet1(nn.Cell):
+    def __init__(self, net, get_all):
+        super(GradNet1, self).__init__()
+        self.forward_net = net
+        self.sens = Tensor(np.ones((2, 2), np.float32) * 5)
+        self.grad_all = C.GradOperation(get_all=get_all)
+
+    def construct(self, tuple_a, tensor_x, list_b, tensor_y, tensor_z, dict_c):
+        return self.grad_all(self.forward_net)(tuple_a, tensor_x, list_b, tensor_y, tensor_z, dict_c)
+
+
 x = Tensor(np.ones((2, 2), np.float32))
 y = Tensor(np.ones((2, 2), np.float32) * 2)
 z = Tensor(np.ones((2, 2), np.float32) * 3)
@@ -68,33 +79,18 @@ forward_net = FirstInputTupleNet()
 grad_all_inputs_net = GradNet(forward_net, get_all=True)
 
 
-def test_outermost_net_inputs_including_non_tensor():
-    forward_net(arg_t0, z, arg_l0, w, sl, args_d0, flag_0)
-    forward_net(arg_t1, z, arg_l1, x, sl, args_d1, flag_1)
-
-
-def test_grad_net_inputs_including_non_tensor():
-    assert len(grad_all_inputs_net(arg_t0, z, arg_l0, w, sl, args_d0, flag_0)) == 2
-    assert len(grad_all_inputs_net(arg_t1, z, arg_l1, x, sl, args_d1, flag_1)) == 2
-
-
 def test_grad_first_input_net():
     class FirstInputTensorNet(nn.Cell):
         def __init__(self):
             super(FirstInputTensorNet, self).__init__()
 
-        def construct(self, tensor_x, tuple_a, list_b, tensor_y, scalar, dict_c, flag):
-            if flag:
-                return tensor_x - tuple_a[2] + list_b[1][1]["x"] - tensor_y + scalar - dict_c["x"]
-            return tensor_x + tuple_a[2] - list_b[1][1]["y"] + tensor_y - scalar + dict_c["y"]
+        def construct(self, tensor_x, tuple_a, list_b, tensor_y, tensor_z, dict_c):
+            return tensor_x + tuple_a[2] - list_b[1][1]["y"] + tensor_y - tensor_z + dict_c["y"]
 
-    grad_fist_input_tensor_net = GradNet(FirstInputTensorNet(), get_all=False)
-    ret = grad_fist_input_tensor_net(z, arg_t0, arg_l0, w, sl, args_d0, flag_0)
+    grad_fist_input_tensor_net = GradNet1(FirstInputTensorNet(), get_all=False)
+    ret = grad_fist_input_tensor_net(z, arg_t0, arg_l0, w, y, args_d0)
     assert np.allclose(ret.asnumpy(), np.ones((2, 2), np.float32))
 
-    grad_fist_input_tuple_net = GradNet(forward_net, get_all=False)
-    assert not grad_fist_input_tuple_net(arg_t0, z, arg_l0, w, sl, args_d0, flag_0)
-
 
 def test_net_inputs_including_str():
     with pytest.raises(TypeError) as err:

tests/ut/python/pynative_mode/test_framstruct.py

@@ -14,7 +14,7 @@
 # ============================================================================
 """ test_framstruct """
 import numpy as np
-
+import pytest
 import mindspore as ms
 import mindspore.nn as nn
 from mindspore import context
@@ -76,11 +76,13 @@ def dynamic_make_tuple(x, lower, upper):
 
 
 def test_dynamic_make_tuple():
-    assert dynamic_make_tuple(2, 1, 5) == (2, 2, 2, 2)
+    # Dynamically recursively creating static type is invalid in mindspore, as mindspore is a static language.
+    with pytest.raises(RuntimeError):
+        dynamic_make_tuple(2, 1, 5)
 
 
 def test_make_tuple():
-    # Staticly recursively creating static type is valid in mindspore.
+    # Statically recursively creating static type is valid in mindspore.
     @ms_function
     def make_tuple(x):
         out = ()