!1967 check arg is tensor with vm backend

Merge pull request !1967 from zhangbuxue/check_arg_is_tensor_with_vm

mindspore/ccsrc/pipeline/pipeline.cc

@@ -615,12 +615,16 @@ void ProcessVmArgInner(const py::tuple &args, const ResourcePtr &res, VectorRef
     py::object arg = args[i];
     auto ms_context = MsContext::GetInstance();
     if (ms_context->backend_policy() == kMsConvert && py::isinstance<py::array>(arg)) {
-      MS_LOG(EXCEPTION) << "Args[" << i << "] is numpy array, not tensor";
+      MS_LOG(EXCEPTION) << "The " << i << "th arg is numpy array, not tensor.";
     }
     ValuePtr converted = nullptr;
     bool succ = parse::ConvertData(arg, &converted);
     if (!succ) {
-      MS_LOG(EXCEPTION) << "Args convert error";
+      MS_LOG(EXCEPTION) << "The " << i << "th arg convert failed.";
+    }
+    if (MsContext::GetInstance()->execution_mode() == 0 && !converted->isa<tensor::Tensor>()) {
+      MS_EXCEPTION(TypeError) << "For 'graph mode', the " << i << "th arg: " << converted->ToString()
+                              << " is not tensor.";
     }
     arg_list->push_back(converted);
   }

mindspore/ccsrc/pipeline/pipeline_ge.cc

@@ -460,12 +460,12 @@ void ProcessGeArg(const std::map<std::string, ExecutorInfoPtr> &info, const py::
       ValuePtr converted = nullptr;
       bool succ = parse::ConvertData(args[i], &converted);
       if (!succ) {
-        MS_LOG(EXCEPTION) << "Args convert error";
+        MS_LOG(EXCEPTION) << "The " << i << "th arg convert failed.";
       }
       if (converted->isa<tensor::Tensor>()) {
         inputs->push_back(converted->cast<tensor::TensorPtr>());
       } else {
-        MS_EXCEPTION(TypeError) << "Args " << converted->ToString() << " is not tensor";
+        MS_EXCEPTION(TypeError) << "The " << i << "th arg: " << converted->ToString() << " is not tensor.";
       }
     }
   }

mindspore/ops/operations/math_ops.py

@@ -775,7 +775,7 @@ class Sub(_MathBinaryOp):
     When the inputs are one tensor and one scalar,
     the scalar only could be a constant.
 
-        Inputs:
+    Inputs:
         - **input_x** (Union[Tensor, Number, bool]) - The first input is a number or
           a bool or a tensor whose data type is number or bool.
         - **input_y** (Union[Tensor, Number, bool]) - The second input is a number or
@@ -1843,7 +1843,7 @@ class LogicalAnd(_LogicBinaryOp):
     Inputs:
         - **input_x** (Union[Tensor, bool]) - The first input is a bool or a tensor whose data type is bool.
         - **input_y** (Union[Tensor, bool]) - The second input is a bool when the first input is a tensor or
-         a tensor whose data type is bool.
+          a tensor whose data type is bool.
 
     Outputs:
         Tensor, the shape is same as the shape after broadcasting, and the data type is bool.
@@ -1873,7 +1873,7 @@ class LogicalOr(_LogicBinaryOp):
     Inputs:
         - **input_x** (Union[Tensor, bool]) - The first input is a bool or a tensor whose data type is bool.
         - **input_y** (Union[Tensor, bool]) - The second input is a bool when the first input is a tensor or
-         a tensor whose data type is bool.
+          a tensor whose data type is bool.
 
     Outputs:
         Tensor, the shape is same as the shape after broadcasting,and the data type is bool.

tests/st/ops/gpu/test_cast_op.py

@@ -24,13 +24,15 @@ from mindspore.ops import operations as P
 
 
 class Net(Cell):
-    def __init__(self):
+    def __init__(self, type0, type1):
         super(Net, self).__init__()
         self.Cast = P.Cast()
+        self.type0 = type0
+        self.type1 = type1
 
-    def construct(self, x0, type0, x1, type1):
-        output = (self.Cast(x0, type0),
-                  self.Cast(x1, type1))
+    def construct(self, x0, x1):
+        output = (self.Cast(x0, self.type0),
+                  self.Cast(x1, self.type1))
         return output
 
 
@@ -44,8 +46,8 @@ def test_cast():
     t1 = mstype.float32
 
     context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
-    net = Net()
-    output = net(x0, t0, x1, t1)
+    net = Net(t0, t1)
+    output = net(x0, x1)
     type0 = output[0].asnumpy().dtype
     assert type0 == 'float16'
     type1 = output[1].asnumpy().dtype
@@ -62,8 +64,8 @@ def test_cast1():
     t1 = mstype.float32
 
     context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
-    net = Net()
-    output = net(x0, t0, x1, t1)
+    net = Net(t0, t1)
+    output = net(x0, x1)
     type0 = output[0].asnumpy().dtype
     assert type0 == 'float32'
     type1 = output[1].asnumpy().dtype

tests/st/ops/gpu/test_rmsprop.py

@@ -25,24 +25,29 @@ context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
 
 
 class NetCenteredRMSProp(nn.Cell):
-    def __init__(self):
+    def __init__(self, lr, decay, momentum, epsilon):
         super(NetCenteredRMSProp, self).__init__()
         self.rms_opt = P.ApplyCenteredRMSProp()
+        self.lr = lr
+        self.decay = decay
+        self.momentum = momentum
+        self.epsilon = epsilon
 
-    def construct(self, var, g, mg, rms, mom, lr, decay, momentum, epsilon):
-        return self.rms_opt(var, mg, rms, mom, g, lr, decay, momentum, epsilon)
+    def construct(self, var, g, mg, rms, mom):
+        return self.rms_opt(var, mg, rms, mom, g, self.lr, self.decay, self.momentum, self.epsilon)
 
 
 class NetRMSProp(nn.Cell):
-    def __init__(self, decay, momentum, epsilon):
+    def __init__(self, lr, decay, momentum, epsilon):
         super(NetRMSProp, self).__init__()
+        self.lr = lr
         self.decay = decay
         self.momentum = momentum
         self.epsilon = epsilon
         self.rms_opt = P.ApplyRMSProp()
 
-    def construct(self, var, g, mg, rms, mom, lr):
-        return self.rms_opt(var, rms, mom, lr, g, self.decay, self.momentum, self.epsilon)
+    def construct(self, var, g, mg, rms, mom):
+        return self.rms_opt(var, rms, mom, self.lr, g, self.decay, self.momentum, self.epsilon)
 
 
 def rmsprop_numpy(variable, gradients, mean_square, moment,
@@ -82,16 +87,14 @@ def test_rmsprop():
     if centered:
         rmspropcented_numpy(variable_np, gradients_np, mean_gradients_np, mean_square_np, moment_np,
                             learning_rate, decay, momentum, epsilon)
-        net = NetCenteredRMSProp()
-        _ = net(variable_ms, gradients_ms, mean_gradients_ms, mean_square_ms,
-                moment_ms, learning_rate, decay, momentum, epsilon)
+        net = NetCenteredRMSProp(learning_rate, decay, momentum, epsilon)
+        _ = net(variable_ms, gradients_ms, mean_gradients_ms, mean_square_ms, moment_ms)
 
     else:
         rmsprop_numpy(variable_np, gradients_np, mean_square_np, moment_np,
                       learning_rate, decay, momentum, epsilon)
-        net = NetRMSProp(decay, momentum, epsilon)
-        _ = net(variable_ms, gradients_ms, mean_gradients_ms, mean_square_ms,
-                moment_ms, learning_rate)
+        net = NetRMSProp(learning_rate, decay, momentum, epsilon)
+        _ = net(variable_ms, gradients_ms, mean_gradients_ms, mean_square_ms, moment_ms)
 
     error = np.ones(shape=variable_np.shape) * 10e-6
     diff = variable_ms.asnumpy() - variable_np
@@ -135,15 +138,13 @@ def test_rmspropcenter():
     if centered:
         rmspropcented_numpy(variable_np, gradients_np, mean_gradients_np, mean_square_np, moment_np,
                             learning_rate, decay, momentum, epsilon)
-        net = NetCenteredRMSProp()
-        _ = net(variable_ms, gradients_ms, mean_gradients_ms, mean_square_ms, moment_ms,
-                learning_rate, decay, momentum, epsilon)
+        net = NetCenteredRMSProp(learning_rate, decay, momentum, epsilon)
+        _ = net(variable_ms, gradients_ms, mean_gradients_ms, mean_square_ms, moment_ms)
     else:
         rmsprop_numpy(variable_np, gradients_np, mean_square_np, moment_np,
                       learning_rate, decay, momentum, epsilon)
-        net = NetRMSProp(decay, momentum, epsilon)
-        _ = net(variable_ms, gradients_ms, mean_gradients_ms, mean_square_ms, moment_ms,
-                learning_rate)
+        net = NetRMSProp(learning_rate, decay, momentum, epsilon)
+        _ = net(variable_ms, gradients_ms, mean_gradients_ms, mean_square_ms, moment_ms)
 
     error = np.ones(shape=variable_np.shape) * 10e-6
     diff = variable_ms.asnumpy() - variable_np