enhance error messages, test=develop (#27423)

paddle/fluid/inference/capi/pd_predictor.cc

@@ -130,7 +130,8 @@ bool PD_PredictorZeroCopyRun(const PD_AnalysisConfig* config,
   VLOG(3) << "The inputs' size is " << input_names.size();
   PADDLE_ENFORCE_EQ(
       input_names.size(), in_size,
-      "The number of input and the number of model's input must match. ");
+      paddle::platform::errors::InvalidArgument(
+          "The number of input and the number of model's input must match."));
   for (int i = 0; i < in_size; ++i) {
     auto input_t = predictor->GetInputTensor(inputs[i].name);
     std::vector<int> tensor_shape;

paddle/fluid/operators/fused/fusion_seqpool_cvm_concat_op.cc

@@ -24,20 +24,27 @@ void FusionSeqPoolCVMConcatOp::InferShape(
     framework::InferShapeContext* ctx) const {
   PADDLE_ENFORCE_GE(
       ctx->Inputs("X").size(), 1UL,
-      "Inputs(X) of FusionSeqPoolCVMConcatOp should not be empty.");
-  PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                 "Output(Out) of FusionSeqPoolCVMConcatOp should not be null.");
+      paddle::platform::errors::InvalidArgument(
+          "Inputs(X) of FusionSeqPoolCVMConcatOp should not be empty."));
+  PADDLE_ENFORCE(
+      ctx->HasOutput("Out"),
+      paddle::platform::errors::InvalidArgument(
+          "Output(Out) of FusionSeqPoolCVMConcatOp should not be null."));
   int axis = ctx->Attrs().Get<int>("axis");
   PADDLE_ENFORCE_EQ(
-      axis, 1, "FusionSeqPoolCVMConcatOp only supports concat axis=1 yet.");
+      axis, 1,
+      paddle::platform::errors::InvalidArgument(
+          "FusionSeqPoolCVMConcatOp only supports concat axis=1 yet."));
   bool use_cvm = ctx->Attrs().Get<bool>("use_cvm");
   PADDLE_ENFORCE_EQ(
       use_cvm, true,
-      "FusionSeqPoolCVMConcatOp only supports use_cvm is true yet.");
+      paddle::platform::errors::InvalidArgument(
+          "FusionSeqPoolCVMConcatOp only supports use_cvm is true yet."));
 
   auto ins_dims = ctx->GetInputsDim("X");
   const size_t n = ins_dims.size();
-  PADDLE_ENFORCE_GT(n, 0UL, "Input tensors count should > 0.");
+  PADDLE_ENFORCE_GT(n, 0UL, paddle::platform::errors::InvalidArgument(
+                                "Input tensors count should > 0."));
   if (n == 1) {
     LOG(WARNING) << "Only have one input, may waste memory";
   }
@@ -45,7 +52,8 @@ void FusionSeqPoolCVMConcatOp::InferShape(
   // The output height should be confirmed in Compute,
   // since input lod is not accessible here.
   PADDLE_ENFORCE_EQ(ins_dims[0].size(), 2,
-                    "The dims size of first input should be 2.");
+                    paddle::platform::errors::InvalidArgument(
+                        "The dims size of first input should be 2."));
   ctx->SetOutputDim("Out", {-1, ins_dims[0][axis] * static_cast<int>(n)});
 }
 
@@ -99,7 +107,8 @@ class FusionSeqPoolCVMConcatKernel : public framework::OpKernel<T> {
 
     int w = ins[0]->numel() / x0_dims[0];
     PADDLE_ENFORCE_EQ(y_dims[1] % w, 0,
-                      "The output of dims[1] should be dividable of w");
+                      paddle::platform::errors::InvalidArgument(
+                          "The output of dims[1] should be dividable of w"));
     jit::seq_pool_attr_t attr(w, jit::SeqPoolType::kSum);
     if (pooltype == "AVERAGE") {
       attr.type = jit::SeqPoolType::kAvg;
@@ -117,9 +126,11 @@ class FusionSeqPoolCVMConcatKernel : public framework::OpKernel<T> {
       const T* src = ins[i]->data<T>();
       T* dst = y_data + i * w;
       PADDLE_ENFORCE_EQ(static_cast<int>(ins[i]->numel() / x_dims[0]), w,
-                        "Width of all inputs should be equal.");
+                        paddle::platform::errors::InvalidArgument(
+                            "Width of all inputs should be equal."));
       PADDLE_ENFORCE_EQ(x_lod.size(), bs + 1,
-                        "Batchsize of all inputs should be equal.");
+                        paddle::platform::errors::InvalidArgument(
+                            "Batchsize of all inputs should be equal."));
       for (size_t j = 0; j < bs; ++j) {
         attr.h = static_cast<int>(x_lod[j + 1] - x_lod[j]);
         seqpool(src, dst, &attr);