API/OP(fill_constant) error message enhancement (#23584)

paddle/fluid/framework/parallel_executor.cc

@@ -789,7 +789,6 @@ void ParallelExecutor::BCastParamsToDevices(
 FetchResultType ParallelExecutor::Run(
     const std::vector<std::string> &fetch_tensors, bool return_merged) {
   VLOG(3) << "enter ParallelExecutor Run";
-  platform::RecordEvent parallel_executor_event("ParallelExecutor::Run");
 #ifdef WITH_GPERFTOOLS
   if (gProfileStarted) {
     ProfilerFlush();

paddle/fluid/operators/fill_constant_op.cc

@@ -22,8 +22,7 @@ class FillConstantOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
-                      "Output(Out) of FillConstantOp should not be null.");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "FillConstant");
 
     auto& shape = ctx->Attrs().Get<std::vector<int64_t>>("shape");
 

paddle/fluid/operators/fill_constant_op.h

@@ -49,11 +49,11 @@ inline framework::DDim GetShape(const framework::ExecutionContext &ctx) {
       auto tensor = shape_tensor_list[i];
       PADDLE_ENFORCE_EQ(
           tensor->dims(), framework::make_ddim({1}),
-          "ShapeError: If the element type of 'shape' in FillConstantOp is "
-          "Tensor, "
-          "the element's shape must be [1]. But received the element's shape "
-          "is [%s]",
-          tensor->dims());
+          platform::errors::InvalidArgument(
+              "If the element type of 'shape'(tensor_list type) in "
+              "FillConstantOp is Tensor, the shape of this Tensor element must "
+              "be [1]. But received the Tensor element's shape is [%s]",
+              tensor->dims()));
       if (platform::is_gpu_place(tensor->place())) {
         framework::Tensor temp;
         TensorCopySync(*tensor, platform::CPUPlace(), &temp);
@@ -124,9 +124,9 @@ class FillConstantKernel : public framework::OpKernel<T> {
       tensor = out_var->GetMutable<framework::SelectedRows>()->mutable_value();
       tensor->Resize(shape);
     } else {
-      PADDLE_THROW(
-          "fill constant op's output only"
-          "supports SelectedRows and LoDTensor");
+      PADDLE_THROW(platform::errors::Unimplemented(
+          "In fill constant Op, the output only supports SelectedRows and "
+          "LoDTensor."));
     }
 
     platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();

python/paddle/fluid/layers/tensor.py

@@ -628,11 +628,18 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
         out.stop_gradient = True
         return out
 
-    helper = LayerHelper("fill_constant", **locals())
-    check_dtype(dtype, 'create data type',
+    check_dtype(dtype, 'dtype',
                 ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
                 'fill_constant')
     check_type(shape, 'shape', (Variable, list, tuple), 'fill_constant')
+    if isinstance(shape, Variable):
+        check_variable_and_dtype(shape, 'shape', ['int32', 'int64'],
+                                 'fill_constant')
+    if out is not None:
+        check_variable_and_dtype(out, 'out', [convert_dtype(dtype)],
+                                 'fill_constant')
+
+    helper = LayerHelper("fill_constant", **locals())
     inputs = utils._get_shape_tensor_inputs(
         inputs=inputs,
         helper=helper,
@@ -642,12 +649,6 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
 
     if out is None:
         out = helper.create_variable_for_type_inference(dtype=dtype)
-    else:
-        check_dtype(
-            dtype, 'create data type',
-            convert_dtype(out.dtype), 'fill_constant',
-            '(The create data type in fill_constant must be the same with out data type.)'
-        )
     attrs['dtype'] = out.dtype
     helper.append_op(
         type='fill_constant',

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

@@ -23,6 +23,7 @@ import paddle.fluid.core as core
 from paddle.fluid.op import Operator
 import paddle.fluid as fluid
 from paddle.fluid import compiler, Program, program_guard
+import numpy as np
 
 
 # Situation 1: Attr(shape) is a list(without tensor)
@@ -263,12 +264,12 @@ class TestFillConstantOp2_ValueTensor(OpTest):
 # Test python API
 class TestFillConstantAPI(unittest.TestCase):
     def test_api(self):
-        positive_2_int32 = fluid.layers.fill_constant([1], "int32", 2)
 
+        positive_2_int32 = fluid.layers.fill_constant([1], "int32", 2)
         positive_2_int64 = fluid.layers.fill_constant([1], "int64", 2)
+
         shape_tensor_int32 = fluid.data(
             name="shape_tensor_int32", shape=[2], dtype="int32")
-
         shape_tensor_int64 = fluid.data(
             name="shape_tensor_int64", shape=[2], dtype="int64")
 
@@ -349,6 +350,15 @@ class TestFillConstantOpError(unittest.TestCase):
                 dtype='float64',
                 out=x2)
 
+            x3 = np.random.randn(100, 100).astype('int32')
+            self.assertRaises(
+                TypeError,
+                fluid.layers.fill_constant,
+                shape=[100, 100],
+                value=5,
+                dtype='float64',
+                out=x3)
+
             # The argument shape's type of fill_constant_op must be list, tuple or Variable.
             def test_shape_type():
                 fluid.layers.fill_constant(shape=1, dtype="float32", value=1)