Refine error message for some ops (#24479)

test=release/1.8

l1_norm
norm
squared_l2_norm
squared_l2_distance
conv_shift
sample_logits

paddle/fluid/operators/conv_shift_op.cc

@@ -29,25 +29,49 @@ class ConvShiftOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
-    PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should be not null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should be not null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "ConvShiftOp");
+    OP_INOUT_CHECK(ctx->HasInput("Y"), "Input", "Y", "ConvShiftOp");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "ConvShiftOp");
 
     auto x_dims = ctx->GetInputDim("X");
     auto y_dims = ctx->GetInputDim("Y");
-    PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
-    PADDLE_ENFORCE_EQ(y_dims.size(), 2, "Input(Y)'s rank should be 2.");
+    PADDLE_ENFORCE_EQ(
+        x_dims.size(), 2,
+        platform::errors::InvalidArgument(
+            "Input(X)'s dimensions of ConvShiftOp should be 2. "
+            "But received X's shape = [%s] and the dimension is %d.",
+            x_dims, x_dims.size()));
+    PADDLE_ENFORCE_EQ(
+        y_dims.size(), 2,
+        platform::errors::InvalidArgument(
+            "Input(Y)'s dimensions of ConvShiftOp should be 2. "
+            "But received Y's shape = [%s] and the dimension is %d.",
+            y_dims, y_dims.size()));
     if (ctx->IsRuntime() || (x_dims[0] > 0 && y_dims[0] > 0))
-      PADDLE_ENFORCE_EQ(x_dims[0], y_dims[0],
-                        "The 1st dimension of Input(X) and Input(Y) should "
-                        "be equal.");
+      PADDLE_ENFORCE_EQ(
+          x_dims[0], y_dims[0],
+          platform::errors::InvalidArgument(
+              "The first dimension of Input(X) and Input(Y) of ConvShiftOp "
+              "should be equal. "
+              "But received X's shape = [%s], Y's shape = [%s], "
+              "and the first dimensions are %d and %d respectively.",
+              x_dims, y_dims, x_dims[0], y_dims[0]));
     if (ctx->IsRuntime() || y_dims[1] > 0)
-      PADDLE_ENFORCE_EQ(y_dims[1] % 2, 1,
-                        "The 2nd dimension of Input(Y) should be odd.");
+      PADDLE_ENFORCE_EQ(
+          y_dims[1] % 2, 1,
+          platform::errors::InvalidArgument(
+              "The second dimension of Input(Y) of ConvShiftOp should be odd."
+              "But received Y's shape = [%s] and the second dimension is %d.",
+              y_dims, y_dims[1]));
     if (ctx->IsRuntime() || (x_dims[1] > 0 && y_dims[1] > 0))
-      PADDLE_ENFORCE_LE(y_dims[1], x_dims[1],
-                        "The 2nd dimension of Input(Y) should be less than or "
-                        "equal to the 2nd dimension of Input(X).");
+      PADDLE_ENFORCE_LE(
+          y_dims[1], x_dims[1],
+          platform::errors::InvalidArgument(
+              "The second dimension of Input(Y) of ConvShiftOp should be less "
+              "than or equal to the 2nd dimension of Input(X)."
+              "But received X's shape = [%s], Y's shape = [%s], "
+              "and the second dimensions are %d and %d respectively.",
+              x_dims, y_dims, x_dims[1], y_dims[1]));
     ctx->ShareDim("X", /*->*/ "Out");
     ctx->ShareLoD("X", /*->*/ "Out");
   }
@@ -58,10 +82,10 @@ class ConvShiftGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
-    PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should be not null.");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
-                   "Input(Out@GRAD) should be not null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "ConvShiftGradOp");
+    OP_INOUT_CHECK(ctx->HasInput("Y"), "Input", "Y", "ConvShiftGradOp");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
+                   "Out@GRAD", "ConvShiftGradOp");
 
     auto x_grad_name = framework::GradVarName("X");
     if (ctx->HasOutput(x_grad_name)) {

paddle/fluid/operators/l1_norm_op.cc

@@ -25,8 +25,8 @@ class L1NormOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should be not null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "L1NormOp");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "L1NormOp");
 
     ctx->SetOutputDim("Out", {1});
   }
@@ -37,11 +37,11 @@ class L1NormGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
-                   "Input(Out@GRAD) should be not null.");
-    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
-                   "Output(X@GRAD) should be not null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "L1NormGradOp");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
+                   "Out@GRAD", "L1NormGradOp");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
+                   "X@GRAD", "L1NormGradOp");
 
     ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
   }

paddle/fluid/operators/l1_norm_op.h

@@ -45,7 +45,10 @@ class L1NormGradKernel : public framework::OpKernel<T> {
     const framework::Tensor *x = context.Input<framework::Tensor>("X");
     const framework::Tensor *d_out =
         context.Input<framework::Tensor>(framework::GradVarName("Out"));
-    PADDLE_ENFORCE(d_out->numel() == 1, "L1 Norm Gradient should be scalar");
+    PADDLE_ENFORCE_EQ(
+        d_out->numel(), 1,
+        platform::errors::InvalidArgument(
+            "Input(GRAD@Out) of L1NormGradOP should be a scalar."));
     framework::Tensor *dx =
         context.Output<framework::Tensor>(framework::GradVarName("X"));
     dx->mutable_data<T>(context.GetPlace());

paddle/fluid/operators/norm_op.cc

@@ -55,10 +55,8 @@ class NormOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of NormOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of NormOp should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "NormOp");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "NormOp");
     auto xdim = ctx->GetInputDim("X");
     ctx->SetOutputDim("Out", xdim);
     int axis = ctx->Attrs().Get<int>("axis");
@@ -72,9 +70,9 @@ class NormOpGrad : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
-                   "Input(X@GRAD) should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "NormOpGrad");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Input",
+                   "X@GRAD", "NormOpGrad");
     ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
   }
 };

paddle/fluid/operators/sample_logits_op.cc

@@ -112,32 +112,37 @@ class SampleLogitsOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Logits"),
-                   "Input(Logits) should be not null.");
-    PADDLE_ENFORCE(ctx->HasInput("Labels"),
-                   "Input(Labels) should be not null.");
-
-    PADDLE_ENFORCE(ctx->HasOutput("Samples"),
-                   "Output(Samples) should be not null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Probabilities"),
-                   "Output(Probabilities) should be not null.");
-    PADDLE_ENFORCE(ctx->HasOutput("SampledLogits"),
-                   "Output(SampledLogits) should be not null.");
-    PADDLE_ENFORCE(ctx->HasOutput("SampledLabels"),
-                   "Output(SampledLabels) should be not null.");
-    PADDLE_ENFORCE(ctx->HasOutput("LogitsDim"),
-                   "Output(LogitsDim) should be not null.");
-    PADDLE_ENFORCE(ctx->HasOutput("LabelsDim"),
-                   "Output(LabelsDim) should be not null.");
+    OP_INOUT_CHECK(ctx->HasInput("Labels"), "Input", "Logits",
+                   "SampleLogitsOp");
+    OP_INOUT_CHECK(ctx->HasInput("Labels"), "Input", "Logits",
+                   "SampleLogitsOp");
+
+    OP_INOUT_CHECK(ctx->HasOutput("Samples"), "Output", "Samples",
+                   "SampleLogitsOp");
+    OP_INOUT_CHECK(ctx->HasOutput("Probabilities"), "Output", "Probabilities",
+                   "SampleLogitsOp");
+    OP_INOUT_CHECK(ctx->HasOutput("SampledLogits"), "Output", "SampledLogits",
+                   "SampleLogitsOp");
+    OP_INOUT_CHECK(ctx->HasOutput("SampledLabels"), "Output", "SampledLabels",
+                   "SampleLogitsOp");
+    OP_INOUT_CHECK(ctx->HasOutput("LogitsDim"), "Output", "LogitsDim",
+                   "SampleLogitsOp");
+    OP_INOUT_CHECK(ctx->HasOutput("LabelsDim"), "Output", "LabelsDim",
+                   "SampleLogitsOp");
 
     auto logits_dims = ctx->GetInputDim("Logits");
     auto labels_dims = ctx->GetInputDim("Labels");
 
-    PADDLE_ENFORCE_EQ(
-        logits_dims.size(), 2UL,
-        "The logits of softmax_with_cross_entropy should be a 2-D tensor.");
+    PADDLE_ENFORCE_EQ(logits_dims.size(), 2UL,
+                      platform::errors::InvalidArgument(
+                          "Input(Logits) of SampleLogitsOp should be 2D. "
+                          "But received shape = [%s] and dimension is %d.",
+                          logits_dims, logits_dims.size()));
     PADDLE_ENFORCE_EQ(labels_dims.size(), 2UL,
-                      "The labels should be a 2-D tensor.");
+                      platform::errors::InvalidArgument(
+                          "Input(Labels) of SampleLogitsOp should be 2D. "
+                          "But received shape = [%s] and dimension is %d.",
+                          labels_dims, labels_dims.size()));
 
     const int num_samples = ctx->Attrs().Get<int>("num_samples");
     int num_sampled_classes = labels_dims[1] + num_samples;
@@ -175,25 +180,33 @@ class SampleLogitsOpGrad : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("LogitsDim"),
-                   "Input(LogitsDim) should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("LabelsDim"),
-                   "Input(LabelsDim) should be not null.");
-    PADDLE_ENFORCE(ctx->HasInput("Samples"),
-                   "Input(Samples) should be not null.");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("SampledLogits")),
-                   "Input(SampledLogits@Grad) should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Logits")),
-                   "Output(Logits@Grad) should be not null.");
+    OP_INOUT_CHECK(ctx->HasInput("LogitsDim"), "Input", "LogitsDim",
+                   "SampleLogitsOpGrad");
+    OP_INOUT_CHECK(ctx->HasInput("LabelsDim"), "Input", "LabelsDim",
+                   "SampleLogitsOpGrad");
+    OP_INOUT_CHECK(ctx->HasInput("Samples"), "Input", "SamplesabelsDim",
+                   "SampleLogitsOpGrad");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("SampledLogits")),
+                   "Input", "SampledLogits@GRAD", "SampleLogitsOpGrad");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("Logits")), "Output",
+                   "Logits@GRAD", "SampleLogitsOpGrad");
 
     auto logits_dims = ctx->GetInputDim("LogitsDim");
     logits_dims = framework::DDim(logits_dims.Get(), logits_dims.size() - 1);
     auto labels_dims = ctx->GetInputDim("LabelsDim");
     labels_dims = framework::DDim(labels_dims.Get(), labels_dims.size() - 1);
-    PADDLE_ENFORCE_EQ(labels_dims.size(), 2UL,
-                      "The label should be a 2-D tensor.");
-    PADDLE_ENFORCE_EQ(logits_dims.size(), 2UL,
-                      "The logits should be a 2-D tensor.");
+    PADDLE_ENFORCE_EQ(
+        logits_dims.size(), 2UL,
+        platform::errors::InvalidArgument(
+            "Input(LogitsDim) of SampleLogitsOpGrad should be 2D. "
+            "But received shape = [%s] and dimension is %d.",
+            logits_dims, logits_dims.size()));
+    PADDLE_ENFORCE_EQ(
+        labels_dims.size(), 2UL,
+        platform::errors::InvalidArgument(
+            "Input(LabelsDim) of SampleLogitsOpGrad should be 2D. "
+            "But received shape = [%s] and dimension is %d.",
+            labels_dims, labels_dims.size()));
 
     ctx->SetOutputDim(framework::GradVarName("Logits"), logits_dims);
   }

paddle/fluid/operators/sample_logits_op.h

@@ -49,17 +49,37 @@ static void CPUTakeAlongD1(const platform::DeviceContext& ctx,
                            const framework::Tensor& array,
                            const framework::Tensor& index,
                            framework::Tensor* value) {
-  PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true);
+  PADDLE_ENFORCE_EQ(
+      platform::is_cpu_place(ctx.GetPlace()), true,
+      platform::errors::InvalidArgument("This kernel only runs on CPU."));
   // UNDERSTAND: check shape src(B, C), index(B, K), out should also be (B, K)
-  PADDLE_ENFORCE_EQ(index.dims().size(), 2);
-  PADDLE_ENFORCE_EQ(array.dims().size(), 2);
-  PADDLE_ENFORCE_EQ(index.dims()[0], array.dims()[0]);
-  PADDLE_ENFORCE_EQ(index.dims(), value->dims());
-
   const auto batch_size = index.dims()[0];
   const auto num_take = index.dims()[1];
   const auto array_dims = array.dims();
   const auto idx_dims = index.dims();
+  PADDLE_ENFORCE_EQ(idx_dims.size(), 2,
+                    platform::errors::InvalidArgument(
+                        "index of CPUTakeAlongD1 should be 2D. "
+                        "But received shape = [%s] and dimension is %d.",
+                        idx_dims, idx_dims.size()));
+  PADDLE_ENFORCE_EQ(array_dims.size(), 2,
+                    platform::errors::InvalidArgument(
+                        "array of CPUTakeAlongD1 should be 2D. "
+                        "But received shape = [%s] and dimension is %d.",
+                        array_dims, array_dims.size()));
+  PADDLE_ENFORCE_EQ(idx_dims[0], array_dims[0],
+                    platform::errors::InvalidArgument(
+                        "The first dimension of index and array of "
+                        "CPUTakeAlongD1 should be equal. "
+                        "But received index shape = [%s], array shape = [%s], "
+                        "and the first dimensions are %d and %d.",
+                        idx_dims, array_dims, idx_dims[0], array_dims[0]));
+  PADDLE_ENFORCE_EQ(
+      idx_dims, value->dims(),
+      platform::errors::InvalidArgument(
+          "index and array of CPUTakeAlongD1 should have the same shape. "
+          "But received index shape = [%s], array shape = [%s].",
+          idx_dims, value->dims()));
 
   // UNDERSTAND: no allocations here
   const T* p_array = array.data<T>();
@@ -89,16 +109,37 @@ static void CPUPutAlongD1(const platform::DeviceContext& ctx,
                           framework::Tensor* array,
                           const framework::Tensor& index,
                           const framework::Tensor& value) {
-  PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true);
+  PADDLE_ENFORCE_EQ(
+      platform::is_cpu_place(ctx.GetPlace()), true,
+      platform::errors::InvalidArgument("This kernel only runs on CPU."));
   // UNDERSTAND: check shape src(B, C), index(B, K), out should also be (B, K)
-  PADDLE_ENFORCE_EQ(index.dims().size(), 2);
-  PADDLE_ENFORCE_EQ(array->dims().size(), 2);
-  PADDLE_ENFORCE_EQ(index.dims()[0], array->dims()[0]);
-  PADDLE_ENFORCE_EQ(index.dims(), value.dims());
   const auto batch_size = index.dims()[0];
   const auto num_put = index.dims()[1];
   auto array_dims = array->dims();
   auto idx_dims = index.dims();
+  PADDLE_ENFORCE_EQ(idx_dims.size(), 2,
+                    platform::errors::InvalidArgument(
+                        "index of CPUPutAlongD1 should be 2D. "
+                        "But received shape = [%s] and dimension is %d.",
+                        idx_dims, idx_dims.size()));
+  PADDLE_ENFORCE_EQ(array_dims.size(), 2,
+                    platform::errors::InvalidArgument(
+                        "array of CPUPutAlongD1 should be 2D. "
+                        "But received shape = [%s] and dimension is %d.",
+                        array_dims, array_dims.size()));
+  PADDLE_ENFORCE_EQ(idx_dims[0], array_dims[0],
+                    platform::errors::InvalidArgument(
+                        "The first dimension of index and array of "
+                        "CPUPutAlongD1 should be equal. "
+                        "But received index shape = [%s], array shape = [%s], "
+                        "and the first dimensions are %d and %d.",
+                        idx_dims, array_dims, idx_dims[0], array_dims[0]));
+  PADDLE_ENFORCE_EQ(
+      idx_dims, value.dims(),
+      platform::errors::InvalidArgument(
+          "index and array of CPUPutAlongD1 should have the same shape. "
+          "But received index shape = [%s], array shape = [%s].",
+          idx_dims, value.dims()));
 
   // UNDERSTAND: no allocations here
   T* p_array = array->data<T>();
@@ -149,8 +190,9 @@ class SampleLogitsKernel : public framework::OpKernel<T> {
  public:
   using Tensor = framework::Tensor;
   void Compute(const framework::ExecutionContext& context) const override {
-    PADDLE_ENFORCE_EQ(platform::is_cpu_place(context.GetPlace()), true,
-                      "This kernel only runs on CPU.");
+    PADDLE_ENFORCE_EQ(
+        platform::is_cpu_place(context.GetPlace()), true,
+        platform::errors::InvalidArgument("this kernel only runs on cpu."));
     VLOG(3) << "Enter SampleLogitsKernel";
     // get necessary inputs
     const Tensor* logits = context.Input<Tensor>("Logits");

paddle/fluid/operators/squared_l2_distance_op.cc

@@ -26,44 +26,61 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of SquaredL2DistanceOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Y"),
-                   "Input(Y) of SquaredL2DistanceOp should not be null.");
-    PADDLE_ENFORCE(
-        ctx->HasOutput("sub_result"),
-        "Output(sub_result) of SquaredL2DistanceOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of SquaredL2DistanceOp should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SquaredL2DistanceOp");
+    OP_INOUT_CHECK(ctx->HasInput("Y"), "Input", "Y", "SquaredL2DistanceOp");
+    OP_INOUT_CHECK(ctx->HasOutput("sub_result"), "Output", "sub_result",
+                   "SquaredL2DistanceOp");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out",
+                   "SquaredL2DistanceOp");
 
     auto x_dims = ctx->GetInputDim("X");
     auto y_dims = ctx->GetInputDim("Y");
 
     PADDLE_ENFORCE_EQ(framework::arity(x_dims), framework::arity(y_dims),
-                      "Tensor rank of both SquaredL2DistanceOp's "
-                      "inputs must be same.");
+                      platform::errors::InvalidArgument(
+                          "Input(X) and Input(X) of SquaredL2DistanceOp should "
+                          "have same dimensions. "
+                          "But received X's shape = [%s] and Y's shape = [%s], "
+                          "the dimensions are %d and %d respectively",
+                          x_dims, y_dims, framework::arity(x_dims),
+                          framework::arity(y_dims)));
 
     int rank = framework::arity(x_dims);
-    PADDLE_ENFORCE_GE(rank, 2, "Tensor rank should be at least equal to 2.");
+    PADDLE_ENFORCE_GE(
+        rank, 2,
+        platform::errors::InvalidArgument(
+            "Input dimensions of SquaredL2DistanceOp should be at least 2."
+            "But received shape = [%s] and dimension is %d.",
+            x_dims, rank));
     bool check = true;
     if ((!ctx->IsRuntime()) &&
         (framework::product(x_dims) <= 0 || framework::product(y_dims) <= 0)) {
       check = false;
     }
     if (check) {
-      PADDLE_ENFORCE_EQ(product(x_dims) / x_dims[0],
-                        product(y_dims) / y_dims[0],
-                        "Product of dimensions expcet the first dimension of "
-                        "input and target must be equal.");
+      PADDLE_ENFORCE_EQ(
+          product(x_dims) / x_dims[0], product(y_dims) / y_dims[0],
+          platform::errors::InvalidArgument(
+              "Input(X) and Input(Y) of SquaredL2DistanceOp should "
+              "have same dimensions."
+              "But received X's shape = [%s] and Y's shape = [%s]"
+              ", the products are %d and %d respectively",
+              x_dims, y_dims, product(x_dims) / x_dims[0],
+              product(y_dims) / y_dims[0]));
     }
     check = true;
     if ((!ctx->IsRuntime()) && (y_dims[0] <= 0 || x_dims[0] <= 0)) {
       check = false;
     }
     if (check) {
-      PADDLE_ENFORCE(y_dims[0] == 1 || y_dims[0] == x_dims[0],
-                     "First dimension of target must be equal to input "
-                     "or to 1.");
+      PADDLE_ENFORCE_EQ(
+          y_dims[0] == 1 || y_dims[0] == x_dims[0], true,
+          platform::errors::InvalidArgument(
+              "First dimension of Input(Y) of SquaredL2DistanceOp "
+              "must be equal to 1 or to first dimension of Input(X)."
+              "But received X's shape = [%s] and Y's shape = [%s],"
+              "the first dimensions are %d and %d respectively",
+              x_dims, y_dims, x_dims[0], y_dims[0]));
     }
     ctx->SetOutputDim("sub_result", {x_dims[0], product(x_dims) / x_dims[0]});
     ctx->SetOutputDim("Out", {x_dims[0], 1});
@@ -126,19 +143,29 @@ class SquaredL2DistanceGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
-                   "Gradient of Out should not be null");
-    PADDLE_ENFORCE(ctx->HasInput("sub_result"), "SubResult should not be null");
+    OP_INOUT_CHECK(ctx->HasInput("sub_result"), "Input", "sub_result",
+                   "SquaredL2DistanceGradOp");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
+                   "Out@GRAD", "SquaredL2DistanceGradOp");
     auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
     auto x_dims = ctx->GetInputDim("X");
     auto y_dims = ctx->GetInputDim("Y");
     if (ctx->IsRuntime()) {
-      PADDLE_ENFORCE_EQ(out_dims[0], x_dims[0],
-                        "First dimension of output gradient and "
-                        "input value must be equal.");
+      PADDLE_ENFORCE_EQ(
+          out_dims[0], x_dims[0],
+          platform::errors::InvalidArgument(
+              "First dimension of output gradient and Input(X) "
+              "of SquaredL2DistanceGradOp must be equal "
+              "But received X's shape = [%s] and grad's shape = [%s], "
+              "the first dimensions are %d and %d respectively",
+              x_dims, out_dims, x_dims[0], out_dims[0]));
       PADDLE_ENFORCE_EQ(out_dims[1], 1,
-                        "Second dimension of output gradient "
-                        "must be 1.");
+                        platform::errors::InvalidArgument(
+                            "Second dimension of output gradient of "
+                            "SquaredL2DistanceGradOp must be 1. "
+                            "But received grad's shape = [%s], "
+                            "with second dimension %d",
+                            out_dims, out_dims[1]));
     }
     auto x_grad_name = framework::GradVarName("X");
     auto y_grad_name = framework::GradVarName("Y");

paddle/fluid/operators/squared_l2_norm_op.cc

@@ -26,8 +26,8 @@ class SquaredL2NormOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should be not null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SquaredL2NormOp");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "SquaredL2NormOp");
 
     ctx->SetOutputDim("Out", {1});
   }
@@ -56,11 +56,11 @@ class SquaredL2NormGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
-                   "Input(Out@GRAD) should be not null.");
-    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
-                   "Output(X@GRAD) should be not null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SquaredL2NormGradOp");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
+                   "Out@GRAD", "SquaredL2NormGradOp");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
+                   "X@GRAD", "SquaredL2NormGradOp");
 
     ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
   }

paddle/fluid/operators/squared_l2_norm_op.h

@@ -45,8 +45,10 @@ class SquaredL2NormGradKernel : public framework::OpKernel<T> {
     const framework::Tensor *X = context.Input<framework::Tensor>("X");
     const framework::Tensor *dOut =
         context.Input<framework::Tensor>(framework::GradVarName("Out"));
-    PADDLE_ENFORCE(dOut->numel() == 1,
-                   "Squared L2 Norm Gradient should be scalar");
+    PADDLE_ENFORCE_EQ(
+        dOut->numel(), 1,
+        platform::errors::InvalidArgument(
+            "Input(GRAD@Out) of SquaredL2NormGradOP should be a scalar."));
     framework::Tensor *dX =
         context.Output<framework::Tensor>(framework::GradVarName("X"));
     dX->mutable_data<T>(context.GetPlace());

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

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import collections
+import numpy as np
+from op_test import OpTest
+
+
+class TestSampleLogitsOp(OpTest):
+    def setUp(self):
+        self.op_type = "sample_logits"
+        self.dtype = np.float64
+        self.use_mkldnn = False
+        bs = 2
+        K = 20
+        NT = 10
+        S = 5
+
+        Samples = np.random.random([bs, NT + S]).astype('int64')
+        Probabilities = np.random.random([bs, NT + S]).astype('float64')
+        LogitsDim = np.array([bs, K], dtype=np.int64)
+        LabelsDim = np.array([bs, NT], dtype=np.int64)
+        SampledLogits = np.random.random([bs, NT + S]).astype('float64')
+        SampledLabels = np.random.random([bs, NT]).astype('int64')
+
+        self.bs = bs
+        self.K = K
+        self.NT = NT
+        self.S = S
+        Labels = np.array(list(range(self.NT)) * self.bs).astype('int64')
+        self.Labels = Labels.reshape(self.bs, -1)
+        self.Logits = np.random.random([self.bs, self.K]).astype('float64')
+
+        self.inputs = {"Logits": self.Logits, "Labels": self.Labels}
+        self.fetch_list = [
+            'Samples', 'Probabilities', 'SampledLogits', 'SampledLabels'
+        ]
+        self.outputs = collections.OrderedDict(
+            (('Samples', Samples), ('Probabilities', Probabilities),
+             ('LogitsDim', LogitsDim), ('LabelsDim', LabelsDim),
+             ('SampledLogits', SampledLogits), ('SampledLabels',
+                                                SampledLabels)))
+
+        self.attrs = {'num_samples': self.S}
+
+    def test_check_output(self):
+        places = self._get_places()
+        for p in places:
+            (Samples, Probabilities, SampledLogits,
+             SampledLabels) = [np.array(o) for o in self.calc_output(p)]
+
+            assert Samples.dtype == np.int64, \
+                "Samples dtype is {}, not int64".format(Samples.dtype)
+            assert Probabilities.dtype == np.float64, \
+                "Probabilities dtype is {}, not float64".format(
+                    Probabilities.dtype)
+            assert SampledLogits.dtype == np.float64, \
+                "SampledLogits dtype is {}, not float64".format(
+                    SampledLogits.dtype)
+            assert SampledLabels.dtype == np.int64, \
+                "SampledLabels dtype is {}, not int64".format(
+                    SampledLabels.dtype)
+
+            assert Samples.shape == (self.bs, self.NT + self.S)
+            assert Probabilities.shape == (self.bs, self.NT + self.S)
+            assert SampledLogits.shape == (self.bs, self.NT + self.S)
+            assert SampledLabels.shape == (self.bs, self.NT)
+
+            assert (SampledLabels == self.Labels).all()
+            sampled_logits = self.Logits[:, Samples[0][:self.NT]]
+            sampled_logits -= np.log(Probabilities[:, :self.NT])
+            np.testing.assert_almost_equal(sampled_logits,
+                                           SampledLogits[:, :self.NT])
+
+    def test_check_grad(self):
+        self._check_grad_helper()
+        for p in self._get_places():
+            grads = self._get_gradient(['Logits'], p, ['SampledLogits'], [])
+            np.testing.assert_almost_equal(grads[0].sum(), np.array([1.]))
+
+
+class TestSampleLogitsOpNoUniq(TestSampleLogitsOp):
+    def setUp(self):
+        super(TestSampleLogitsOpNoUniq, self).setUp()
+        self.attrs = {'num_samples': self.S, 'uniq': False}
+
+
+class TestSampleLogitsOpWithAccidentalHits(TestSampleLogitsOp):
+    def setUp(self):
+        super(TestSampleLogitsOpWithAccidentalHits, self).setUp()
+        self.attrs = {'num_samples': self.S, 'remove_accidental_hits': False}
+
+
+if __name__ == "__main__":
+    unittest.main()