Error message opt, test=develop (#27467)

* Error message opt, test=develop

* solve comments, test=develop

* fix typo, test=develop

paddle/fluid/operators/center_loss_op.cu

@@ -30,8 +30,10 @@ __global__ void ComputeDifferent(T *centers_diff, const T *X, const T *centers,
 
   while (idy < K) {
     int64_t id = ids[idy];
-    PADDLE_ENFORCE(id >= 0, "received id:", id);
-    PADDLE_ENFORCE(id < N, "received id:", id);
+    PADDLE_ENFORCE(id >= 0, "Id should larger than 0 but received id: %d.", id);
+    PADDLE_ENFORCE(id < N, "Id should smaller than %d but received id: %d.", N,
+                   id);
+
     T *out = centers_diff + idy * D;
     const T *x = X + idy * D;
     const T *cent = centers + id * D;
@@ -52,8 +54,9 @@ __global__ void UpdateCenters(T *centers, T *centers_diff, const int64_t *ids,
   while (idy < K) {
     int count = 1;
     int64_t id = ids[idy];
-    PADDLE_ENFORCE(id >= 0, "received id:", id);
-    PADDLE_ENFORCE(id < N, "received id:", id);
+    PADDLE_ENFORCE(id >= 0, "Id should larger than 0 but received id: %d.", id);
+    PADDLE_ENFORCE(id < N, "Id should smaller than %d but received id: %d.", N,
+                   id);
 
     for (int i = 0; i < K; i++) {
       if (ids[i] == id) {

paddle/fluid/operators/ctc_align_op.cu

@@ -69,8 +69,10 @@ template <typename T>
 class CTCAlignOpCUDAKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
-                   "It must use CUDAPlace.");
+    PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx.GetPlace()), true,
+                      platform::errors::InvalidArgument(
+                          "CTCAlign operator CUDA kernel must use CUDAPlace "
+                          "rather than CPUPlace."));
     auto* input = ctx.Input<LoDTensor>("Input");
     auto* output = ctx.Output<LoDTensor>("Output");
     const int blank = ctx.Attr<int>("blank");

paddle/fluid/operators/ctc_align_op.h

@@ -72,8 +72,11 @@ class CTCAlignKernel : public framework::OpKernel<T> {
       // check input dims and lod
       PADDLE_ENFORCE_EQ(
           input_dims[0], static_cast<int64_t>(input_lod[level].back()),
-          "The first dimension of Input(Input) should be equal to "
-          "the sum of all sequences' lengths.");
+          platform::errors::InvalidArgument(
+              "The first dimension %d of CTCAlign operator Input(Input) should "
+              "be equal to "
+              "the sum of all sequences' lengths %d.",
+              input_dims[0], static_cast<int64_t>(input_lod[level].back())));
 
       const size_t num_sequences = input_lod[level].size() - 1;
 

paddle/fluid/operators/pool_cudnn_op.cu.cc

@@ -45,8 +45,10 @@ template <typename T>
 class PoolCUDNNOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext &ctx) const override {
-    PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx.GetPlace()), true,
-                      "It must use CUDAPlace.");
+    PADDLE_ENFORCE_EQ(
+        platform::is_gpu_place(ctx.GetPlace()), true,
+        platform::errors::InvalidArgument("Pool operator CUDA kernel must use "
+                                          "CUDAPlace rather than CPUPlace."));
 
     const Tensor *input = ctx.Input<Tensor>("X");
     Tensor *output = ctx.Output<Tensor>("Out");
@@ -175,8 +177,10 @@ template <typename T>
 class PoolCUDNNGradOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext &ctx) const override {
-    PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx.GetPlace()), true,
-                      "It must use CUDAPlace.");
+    PADDLE_ENFORCE_EQ(
+        platform::is_gpu_place(ctx.GetPlace()), true,
+        platform::errors::InvalidArgument("Pool operator CUDA kernel must use "
+                                          "CUDAPlace rather than CPUPlace."));
 
     const Tensor *input = ctx.Input<Tensor>("X");
     const Tensor *output = ctx.Input<Tensor>("Out");

paddle/fluid/operators/pool_op.cc

@@ -38,18 +38,22 @@ int PoolOutputSize(int input_size, int filter_size, int padding_1,
   }
   PADDLE_ENFORCE_GT(
       output_size, 0,
-      "ShapeError: the output size must be greater than 0. But received: "
-      "output_size = %d due to the settings of input_size(%d), padding(%d,%d), "
-      "k_size(%d) and stride(%d). Please check again!",
-      output_size, input_size, padding_1, padding_2, filter_size, stride);
+      platform::errors::InvalidArgument(
+          "the output size must be greater than 0. But received: "
+          "output_size = %d due to the settings of input_size(%d), "
+          "padding(%d,%d), "
+          "k_size(%d) and stride(%d). Please check again!",
+          output_size, input_size, padding_1, padding_2, filter_size, stride));
   return output_size;
 }
 
 void PoolOp::InferShape(framework::InferShapeContext* ctx) const {
-  PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
-                    "X(Input) of Pooling should not be null.");
-  PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
-                    "Out(Output) of Pooling should not be null.");
+  PADDLE_ENFORCE_EQ(
+      ctx->HasInput("X"), true,
+      platform::errors::NotFound("Input(X) of Pool operator is not found."));
+  PADDLE_ENFORCE_EQ(
+      ctx->HasOutput("Out"), true,
+      platform::errors::NotFound("Output(Out) of Pool operator is not found."));
 
   std::string pooling_type = ctx->Attrs().Get<std::string>("pooling_type");
   std::vector<int> ksize = ctx->Attrs().Get<std::vector<int>>("ksize");
@@ -65,28 +69,32 @@ void PoolOp::InferShape(framework::InferShapeContext* ctx) const {
   auto in_x_dims = ctx->GetInputDim("X");
   PADDLE_ENFORCE_EQ(
       in_x_dims.size() == 4 || in_x_dims.size() == 5, true,
-      "ShapeError: the input of Op(pool) should be 4-D or 5-D Tensor. But "
-      "received: %u-D Tensor and it's shape is [%s].",
-      in_x_dims.size(), in_x_dims);
+      platform::errors::InvalidArgument(
+          "the input of Op(pool) should be 4-D or 5-D Tensor. But "
+          "received: %u-D Tensor and it's shape is [%s].",
+          in_x_dims.size(), in_x_dims));
 
   PADDLE_ENFORCE_EQ(
       in_x_dims.size() - ksize.size(), 2U,
-      "ShapeError: the dimension of input minus the size of "
-      "Attr(ksize) must be euqal to 2 in Op(pool). "
-      "But received: the dimension of input minus the size "
-      "of Attr(ksize) is %d, the "
-      "input's dimension is %d, the shape of input "
-      "is [%s], the Attr(ksize)'s size is %d, the Attr(ksize) is [%s].",
-      in_x_dims.size() - ksize.size(), in_x_dims.size(), in_x_dims,
-      ksize.size(), framework::make_ddim(ksize));
-
-  PADDLE_ENFORCE_EQ(ksize.size(), strides.size(),
-                    "ShapeError: the size of Attr(ksize) and Attr(strides) in "
-                    "Op(pool) must be equal. "
-                    "But received: Attr(ksize)'s size is %d, Attr(strides)'s "
-                    "size is %d, Attr(ksize) is [%s], Attr(strides)is [%s].",
-                    ksize.size(), strides.size(), framework::make_ddim(ksize),
-                    framework::make_ddim(strides));
+      platform::errors::InvalidArgument(
+          "the dimension of input minus the size of "
+          "Attr(ksize) must be euqal to 2 in Op(pool). "
+          "But received: the dimension of input minus the size "
+          "of Attr(ksize) is %d, the "
+          "input's dimension is %d, the shape of input "
+          "is [%s], the Attr(ksize)'s size is %d, the Attr(ksize) is [%s].",
+          in_x_dims.size() - ksize.size(), in_x_dims.size(), in_x_dims,
+          ksize.size(), framework::make_ddim(ksize)));
+
+  PADDLE_ENFORCE_EQ(
+      ksize.size(), strides.size(),
+      platform::errors::InvalidArgument(
+          "the size of Attr(ksize) and Attr(strides) in "
+          "Op(pool) must be equal. "
+          "But received: Attr(ksize)'s size is %d, Attr(strides)'s "
+          "size is %d, Attr(ksize) is [%s], Attr(strides)is [%s].",
+          ksize.size(), strides.size(), framework::make_ddim(ksize),
+          framework::make_ddim(strides)));
 
   // MKL-DNN Kernels are using NCHW order of dims description
   // so we ignore data_format consideration for MKL-DNN kernel
@@ -182,9 +190,12 @@ framework::OpKernelType PoolOp::GetKernelTypeForVar(
 }
 
 void PoolOpGrad::InferShape(framework::InferShapeContext* ctx) const {
-  PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true, "Input(X) must not be null.");
+  PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
+                    platform::errors::NotFound(
+                        "Input(X) of Pool Gradoperator is not found."));
   PADDLE_ENFORCE_EQ(ctx->HasOutput(framework::GradVarName("X")), true,
-                    "Input(X@GRAD) should not be null.");
+                    platform::errors::NotFound(
+                        "Input(X@GRAD) of Pool Gradoperator is not found."));
   ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
 }
 
@@ -210,7 +221,8 @@ framework::OpKernelType PoolOpGrad::GetExpectedKernelType(
   auto input_data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
   if (input_data_type == framework::proto::VarType::FP16) {
     PADDLE_ENFORCE_EQ(library_, framework::LibraryType::kCUDNN,
-                      "float16 can only be used when CUDNN is used");
+                      platform::errors::InvalidArgument(
+                          "Float16 can only be used when CUDNN is used"));
   }
   return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout_,
                                  library_);

paddle/fluid/operators/pool_op.h

@@ -81,9 +81,11 @@ inline void UpdatePadding(std::vector<T>* paddings, const bool global_pooling,
       paddings->insert(paddings->begin() + 2 * i + 1, copy_pad);
     }
   } else {
-    PADDLE_ENFORCE_EQ(
-        data_dims.size() * 2, paddings->size(),
-        "Paddings size should be the same or twice as the pooling size.");
+    PADDLE_ENFORCE_EQ(data_dims.size() * 2, paddings->size(),
+                      platform::errors::InvalidArgument(
+                          "Paddings size %d should be the same or twice as the "
+                          "pooling size %d.",
+                          paddings->size(), data_dims.size() * 2));
   }
 
   // when padding_algorithm is "VALID" or "SAME"
@@ -200,7 +202,10 @@ class PoolKernel : public framework::OpKernel<T> {
                          pool_process, exclusive, adaptive, out);
         }
       } break;
-      default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
+      default: {
+        PADDLE_THROW(platform::errors::InvalidArgument(
+            "Pool op only supports 2D and 3D input."));
+      }
     }
   }
 };
@@ -287,7 +292,10 @@ class PoolGradKernel : public framework::OpKernel<T> {
                             adaptive, in_x_grad);
           }
         } break;
-        default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
+        default: {
+          PADDLE_THROW(platform::errors::InvalidArgument(
+              "Pool op only supports 2D and 3D input."));
+        }
       }
     }
   }

paddle/fluid/operators/pool_with_index_op.cc

@@ -46,8 +46,11 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
     std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
     bool adaptive = ctx->Attrs().Get<bool>("adaptive");
 
-    PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
-                   "Pooling intput should be 4-D or 5-D tensor.");
+    PADDLE_ENFORCE(
+        in_x_dims.size() == 4 || in_x_dims.size() == 5,
+        platform::errors::InvalidArgument("Pooling intput should be 4-D or 5-D "
+                                          "tensor but received %dD-Tensor",
+                                          in_x_dims.size()));
 
     if (ctx->Attrs().Get<bool>("global_pooling")) {
       ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
@@ -57,16 +60,21 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
       }
     }
 
-    PADDLE_ENFORCE_EQ(in_x_dims.size() - ksize.size(), 2U,
-                      platform::errors::InvalidArgument(
-                          "Input size and pooling size should be consistent."));
-    PADDLE_ENFORCE_EQ(ksize.size(), strides.size(),
-                      platform::errors::InvalidArgument(
-                          "Strides size and pooling size should be the same."));
+    PADDLE_ENFORCE_EQ(
+        in_x_dims.size() - ksize.size(), 2U,
+        platform::errors::InvalidArgument(
+            "The input size %d minus the kernel size %d should equal to 2.",
+            in_x_dims.size(), ksize.size()));
+    PADDLE_ENFORCE_EQ(
+        ksize.size(), strides.size(),
+        platform::errors::InvalidArgument(
+            "Strides size %d and pooling size %d should be the same.",
+            strides.size(), ksize.size()));
     PADDLE_ENFORCE_EQ(
         ksize.size(), paddings.size(),
         platform::errors::InvalidArgument(
-            "Paddings size and pooling size should be the same."));
+            "Paddings size %d and pooling size %d should be the same.",
+            paddings.size(), ksize.size()));
 
     std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
     if (adaptive) {

paddle/fluid/operators/pool_with_index_op.h

@@ -61,7 +61,10 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T1> {
         pool3d_forward(dev_ctx, *in_x, ksize, strides, paddings, adaptive, out,
                        mask);
       } break;
-      default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
+      default: {
+        PADDLE_THROW(platform::errors::InvalidArgument(
+            "Pool op only supports 2D and 3D input."));
+      }
     }
   }
 };
@@ -106,7 +109,10 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T1> {
           pool3d_backward(device_ctx, *out_grad, *mask, ksize, strides,
                           paddings, adaptive, in_x_grad);
         } break;
-        default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
+        default: {
+          PADDLE_THROW(platform::errors::InvalidArgument(
+              "Pool op only supports 2D and 3D input."));
+        }
       }
     }
   }

paddle/fluid/operators/psroi_pool_op.cu

@@ -176,22 +176,31 @@ class GPUPSROIPoolOpKernel : public framework::OpKernel<T> {
     int height = in_dims[2];
     int width = in_dims[3];
 
-    PADDLE_ENFORCE_EQ(input_channels,
-                      output_channels * pooled_height * pooled_width,
-                      "the channels of input X should equal the product of "
-                      "output_channels x pooled_height x pooled_width");
+    PADDLE_ENFORCE_EQ(
+        input_channels, output_channels * pooled_height * pooled_width,
+        platform::errors::InvalidArgument(
+            "The channels %d of input X should equal the product of "
+            "output_channels %d x pooled_height %d x pooled_width %d.",
+            input_channels, output_channels, pooled_height, pooled_width));
 
     int rois_num = rois->dims()[0];
     if (rois_num == 0) return;
 
     auto rois_lod = rois->lod().back();
     int rois_batch_size = rois_lod.size() - 1;
-    PADDLE_ENFORCE_EQ(
-        rois_batch_size, batch_size,
-        "The rois_batch_size and input(X) batch_size must be the same.");
+    PADDLE_ENFORCE_EQ(rois_batch_size, batch_size,
+                      platform::errors::InvalidArgument(
+                          "The batch size of input(ROIs) and input(X) must be "
+                          "the same but received batch size of input(ROIs) and "
+                          "input(X) is %d and %d respectively.",
+                          rois_batch_size, batch_size));
     int rois_num_with_lod = rois_lod[rois_batch_size];
     PADDLE_ENFORCE_EQ(rois_num, rois_num_with_lod,
-                      "The rois_num from input and lod must be the same.");
+                      platform::errors::InvalidArgument(
+                          "The number of rois from input(ROIs) and its LOD "
+                          "must be the same. Received rois %d of input(ROIs) "
+                          "but the number of rois %d from its LOD is %d",
+                          rois_num, rois_num_with_lod));
 
     // set rois batch id
     framework::Tensor rois_batch_id_list;

paddle/fluid/operators/roi_pool_op.cu

@@ -160,9 +160,14 @@ class GPUROIPoolOpKernel : public framework::OpKernel<T> {
     if (ctx.HasInput("RoisNum")) {
       auto* rois_num_t = ctx.Input<Tensor>("RoisNum");
       int rois_batch_size = rois_num_t->numel();
+
       PADDLE_ENFORCE_EQ(
           rois_batch_size, batch_size,
-          "The rois_batch_size and imgs batch_size must be the same.");
+          platform::errors::InvalidArgument(
+              "The batch size of input(ROIs) and input(X) must be the same but "
+              "received batch size of input(ROIs) and input(X) is %d and %d "
+              "respectively.",
+              rois_batch_size, batch_size));
       std::vector<int> rois_num_list(rois_batch_size);
       memory::Copy(cplace, rois_num_list.data(), gplace,
                    rois_num_t->data<int>(), sizeof(int) * rois_batch_size, 0);
@@ -178,10 +183,19 @@ class GPUROIPoolOpKernel : public framework::OpKernel<T> {
       int rois_batch_size = rois_lod.size() - 1;
       PADDLE_ENFORCE_EQ(
           rois_batch_size, batch_size,
-          "The rois_batch_size and imgs batch_size must be the same.");
+          platform::errors::InvalidArgument(
+              "The batch size of input(ROIs) and input(X) must be the same but "
+              "received batch size of input(ROIs) and input(X) is %d and %d "
+              "respectively.",
+              rois_batch_size, batch_size));
+
       int rois_num_with_lod = rois_lod[rois_batch_size];
       PADDLE_ENFORCE_EQ(rois_num, rois_num_with_lod,
-                        "The rois_num from input and lod must be the same.");
+                        platform::errors::InvalidArgument(
+                            "The number of rois from input(ROIs) and its LOD "
+                            "must be the same. Received rois %d of input(ROIs) "
+                            "but the number of rois %d from its LOD is %d",
+                            rois_num, rois_num_with_lod));
       for (int n = 0; n < rois_batch_size; ++n) {
         for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
           roi_batch_id_data[i] = n;