enhance shape error message of fc API (#20172)

* add api check in fc test=develop

* enforce shape error info of sum op test=develop

* fix spelling test=develop

* print x_dims info test=develop

* enhance shape error info test=develop

paddle/fluid/operators/sum_op.cc

@@ -33,9 +33,10 @@ class SumOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInputs("X"), "Inputs(X) should not be null");
+    PADDLE_ENFORCE_EQ(ctx->HasInputs("X"), true,
+                      "Inputs(X) should not be null");
 
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+    PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
                       "Output(Out) of SumOp should not be null.");
     if (ctx->IsRuntime() &&
         ctx->GetOutputsVarType("Out")[0] ==
@@ -46,10 +47,15 @@ class SumOp : public framework::OperatorWithKernel {
     auto x_var_types = ctx->GetInputsVarType("X");
     auto x_dims = ctx->GetInputsDim("X");
 
-    size_t N = x_dims.size();
-    PADDLE_ENFORCE_GT(N, 0, "Input tensors count should > 0.");
+    auto N = x_dims.size();
+    PADDLE_ENFORCE_GT(
+        N, 0,
+        "ShapeError: The input tensor X's dimensions of SumOp "
+        "should be larger than 0. But received X's dimensions %d, "
+        "X's shape = [%s].",
+        N, &x_dims);
     if (N == 1) {
-      VLOG(3) << "Warning: sum have only one input, may waste memory";
+      VLOG(3) << "Warning: SumOp have only one input, may waste memory";
     }
 
     framework::DDim in_dim({0});
@@ -67,18 +73,29 @@ class SumOp : public framework::OperatorWithKernel {
         in_dim = x_dim;
       } else {
         if (ctx->IsRuntime()) {
-          PADDLE_ENFORCE_EQ(in_dim, x_dim,
-                            "Input tensors must have same shape");
+          PADDLE_ENFORCE_EQ(
+              in_dim, x_dim,
+              "ShapeError: The input tensor X of SumOp must have same shape."
+              "But received X[0]'s shape = [%s], X[%d]'s shape = [%s].",
+              in_dim, i, x_dim);
         } else {
-          PADDLE_ENFORCE_EQ(in_dim.size(), x_dim.size(),
-                            "Input tensors must have same shape size");
+          PADDLE_ENFORCE_EQ(
+              in_dim.size(), x_dim.size(),
+              "ShapeError: The input tensor X of SumOp must have same "
+              "dimensions. But received X[0]'s dimensions = %d, X[0]'s shape = "
+              "[%s], X[%d]'s dimensions = %d, X[%d]'s shape = [%s].",
+              in_dim.size(), in_dim, i, x_dim.size(), i, x_dim);
           // if in_dim or x_dim has -1, not check equal
-          for (int i = 0; i < x_dim.size(); ++i) {
-            if (x_dim[i] == -1 || in_dim[i] == -1) {
+          for (int j = 0; j < x_dim.size(); ++j) {
+            if (x_dim[j] == -1 || in_dim[j] == -1) {
               continue;
             }
-            PADDLE_ENFORCE_EQ(in_dim[i], x_dim[i],
-                              "Input tensors must have same shape if not -1");
+            PADDLE_ENFORCE_EQ(
+                in_dim[j], x_dim[j],
+                "ShapeError: The input tensor X of SumOp must have same shape "
+                "if not -1."
+                "But received X[0]'s shape = [%s], X[%d]'s shape = [%s].",
+                in_dim, i, x_dim);
           }
         }
       }
@@ -107,8 +124,9 @@ class SumOp : public framework::OperatorWithKernel {
     if (x_vars[0]->IsType<framework::LoDTensor>()) {
       int dtype = -1;
       for (size_t idx = 0; idx < x_vars.size(); ++idx) {
-        PADDLE_ENFORCE(x_vars[idx] != nullptr,
-                       "Input var[%s] should not be nullptr", x_vars_name[idx]);
+        PADDLE_ENFORCE_NOT_NULL(x_vars[idx],
+                                "Input var[%s] should not be nullptr",
+                                x_vars_name[idx]);
         auto tensor =
             framework::GetLoDTensorOrSelectedRowsValueFromVar(*x_vars[idx]);
         if (tensor->numel() <= 0 || (!tensor->IsInitialized())) {
@@ -202,7 +220,7 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
         for (auto& each : inputs) {
           os << "    " << each << " type is " << ctx->GetType(each) << "\n";
         }
-        PADDLE_ENFORCE(all_inputs_are_tensor_array,
+        PADDLE_ENFORCE_EQ(all_inputs_are_tensor_array, true,
                           "Not all inputs are tensor array:\n%s", os.str());
       }
       var_type = framework::proto::VarType::LOD_TENSOR_ARRAY;

python/paddle/fluid/layers/nn.py

@@ -343,8 +343,22 @@ def fc(input,
           fc = fluid.layers.fc(input=[data_1, data_2], size=1000, act="tanh")
     """
     helper = LayerHelper("fc", **locals())
-
+    if isinstance(input, (list, tuple)):
+        for i, input_x in enumerate(input):
+            if not isinstance(input_x, Variable):
+                raise TypeError(
+                    "The type of input[%d] in fc must be Variable, but received %s"
+                    % (i, type(input_x)))
+    else:
+        if not isinstance(input, Variable):
+            raise TypeError(
+                "The type of 'input' in fc must be Variable, but received %s" %
+                (type(input)))
     dtype = helper.input_dtype()
+    if convert_dtype(dtype) not in ['float32', 'float64']:
+        raise TypeError(
+            "The data type of 'input' in fc must be float32 or float64, but received %s."
+            % (convert_dtype(dtype)))
 
     mul_results = []
     for input_var, param_attr in helper.iter_inputs_and_params():

python/paddle/fluid/tests/unittests/test_fc_op.py

@@ -15,6 +15,8 @@
 import unittest
 import numpy as np
 from op_test import OpTest
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
 
 
 def fc_refer(matrix, with_bias, with_relu=False):
@@ -122,5 +124,30 @@ class TestFCOpWithBias3(TestFCOp):
         self.matrix = MatrixGenerate(1, 64, 32, 3, 3, 1)
 
 
+class TestFCOpError(OpTest):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            input_data = np.random.random((2, 4)).astype("float32")
+
+            def test_Variable():
+                # the input type must be Variable
+                fluid.layers.fc(input=input_data, size=1)
+
+            self.assertRaises(TypeError, test_Variable)
+
+            def test_input_list():
+                # each of input(list) must be Variable
+                fluid.layers.fc(input=[input_data], size=1)
+
+            self.assertRaises(TypeError, test_input_list)
+
+            def test_type():
+                # dtype must be float32 or float64
+                x2 = fluid.layers.data(name='x2', shape=[4], dtype='int32')
+                fluid.layers.fc(input=x2, size=1)
+
+            self.assertRaises(TypeError, test_type)
+
+
 if __name__ == "__main__":
     unittest.main()