fix error info for transpose sequence_conv_pool max_sequence_len sequ… (#24437)

* fix error info for transpose sequence_conv_pool max_sequence_len sequence_erase, test=develop

* fix error info for transpose sequence_conv_pool max_sequence_len sequence_erase, test=develop

* update modify, test=develop

* update modify, test=develop

* fixed some modifications, test=develop

paddle/fluid/operators/max_sequence_len_op.cc

@@ -57,7 +57,8 @@ class MaxSeqenceLenOpProtoMaker : public framework::OpProtoAndCheckerMaker {
 class MaxSeqenceLenInferShape : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext *context) const override {
-    PADDLE_ENFORCE(context->HasInput("RankTable"));
+    OP_INOUT_CHECK(context->HasInput("RankTable"), "Input", "RankTable",
+                   "MaxSeqenceLen");
     context->SetOutputDim("Out", {1});
   }
 };

paddle/fluid/operators/sequence_ops/sequence_erase_op.cc

@@ -23,14 +23,15 @@ class SequenceEraseOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of SequenceErase operator should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of SequenceErase operator should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SequenceErase");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "SequenceErase");
     auto x_dims = ctx->GetInputDim("X");
     PADDLE_ENFORCE(x_dims.size() == 2 && x_dims[1] == 1,
-                   "Input(X) of SequenceEraseOp should be a 2-D LoDTensor "
-                   "with the 2nd dimension equal to 1.");
+                   platform::errors::InvalidArgument(
+                       "Input(X) of SequenceEraseOp should be a 2-D LoDTensor "
+                       "with the 2nd dimension equal to 1,"
+                       "but received size %d with the 2nd dimension %d.",
+                       x_dims.size(), x_dims[1]));
     ctx->SetOutputDim("Out", x_dims);
     // The output LoDTensor's lod_level should be input X's lod_level.
     // For compile-time, we call SetLoDLevel to set output's lod_level.

paddle/fluid/operators/sequence_ops/sequence_erase_op.cu

@@ -64,8 +64,10 @@ class SequenceEraseOpCUDAKernel : public framework::OpKernel<T> {
     auto* out = ctx.Output<LoDTensor>("Out");
 
     auto lod = in->lod();
-    PADDLE_ENFORCE_EQ(lod[lod.size() - 1].back(), (size_t)in->numel(),
-                      "The actual size mismatches with the LoD information.");
+    PADDLE_ENFORCE_EQ(
+        lod[lod.size() - 1].back(), (size_t)in->numel(),
+        platform::errors::InvalidArgument(
+            "The actual size mismatches with the LoD information."));
     auto tokens = ctx.Attr<std::vector<int>>("tokens");
     auto in_len = in->numel();
     auto in_dat = in->data<T>();

paddle/fluid/operators/sequence_ops/sequence_erase_op.h

@@ -30,9 +30,13 @@ class SequenceEraseKernel : public framework::OpKernel<T> {
     auto lod = in->lod();
     PADDLE_ENFORCE_EQ(
         lod.empty(), false,
-        "Input(X) Tensor of SequenceEraseOp does not contain LoD information.");
+        platform::errors::InvalidArgument("Input(X) Tensor of SequenceEraseOp "
+                                          "does not contain LoD information."));
     PADDLE_ENFORCE_EQ(lod[lod.size() - 1].back(), (size_t)in->numel(),
-                      "The actual size mismatches with the LoD information.");
+                      platform::errors::InvalidArgument(
+                          "The actual input size %d mismatches with the LoD "
+                          "information size %d.",
+                          lod[lod.size() - 1].back(), (size_t)in->numel()));
     auto tokens = ctx.Attr<std::vector<int>>("tokens");
     auto in_len = in->numel();
     auto in_dat = in->data<T>();

paddle/fluid/operators/transpose_op.cc

@@ -31,31 +31,33 @@ class TransposeOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "Transpose");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "Transpose");
     auto x_dims = ctx->GetInputDim("X");
     std::vector<int> axis = ctx->Attrs().Get<std::vector<int>>("axis");
     size_t x_rank = x_dims.size();
     size_t axis_size = axis.size();
 
     PADDLE_ENFORCE_EQ(x_rank, axis_size,
-                      "ShapeError: The input tensor's dimension "
-                      "should be equal to the axis's size. "
-                      "But received input tensor's dimension is %d, "
-                      "axis's size is %d",
-                      x_rank, axis_size);
+                      platform::errors::InvalidArgument(
+                          "The input tensor's dimension "
+                          "should be equal to the axis's size. "
+                          "But received input tensor's dimension is %d, "
+                          "axis's size is %d",
+                          x_rank, axis_size));
 
     std::vector<int> count(axis_size, 0);
     for (size_t i = 0; i < axis_size; i++) {
-      PADDLE_ENFORCE(
-          axis[i] < static_cast<int>(axis_size) && ++count[axis[i]] == 1,
-          "ValueError: Each element of Attribute axis should "
-          "be a unique value range from 0 to (dims - 1), "
-          "where the dims is the axis's size, "
-          "unique value means this axis value can appear only once. "
-          "But received axis[%d] is %d, axis_size is %d, "
-          "count[axis[%d]] is %d",
-          i, axis[i], axis_size, i, count[axis[i]]);
+      PADDLE_ENFORCE_EQ(
+          axis[i] < static_cast<int>(axis_size) && ++count[axis[i]] == 1, true,
+          platform::errors::InvalidArgument(
+              "Each element of Attribute axis should "
+              "be a unique value range from 0 to (dims - 1), "
+              "where the dims is the axis's size, "
+              "unique value means this axis value can appear only once. "
+              "But received axis[%d] is %d, axis_size is %d, "
+              "count[axis[%d]] is %d",
+              i, axis[i], axis_size, i, count[axis[i]]));
     }
 
     framework::DDim out_dims(x_dims);
@@ -149,9 +151,9 @@ class TransposeOpGrad : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
-                   "Input(Out@GRAD) should not be null");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "TransposeOpGrad");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
+                   framework::GradVarName("Out"), "TransposeOpGrad");
     auto x_dims = ctx->GetInputDim("X");
     ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
     if (ctx->HasOutput(framework::GradVarName("X"))) {
@@ -193,8 +195,7 @@ class Transpose2Op : public TransposeOp {
 
   void InferShape(framework::InferShapeContext *ctx) const override {
     TransposeOp::InferShape(ctx);
-    PADDLE_ENFORCE(ctx->HasOutput("XShape"),
-                   "Output(XShape) should not be null");
+    OP_INOUT_CHECK(ctx->HasOutput("XShape"), "Output", "XShape", "Transpose2");
     const auto &in_dims = ctx->GetInputDim("X");
     std::vector<int64_t> x_shape_dim(in_dims.size() + 1);
     x_shape_dim[0] = 0;
@@ -259,9 +260,10 @@ class Transpose2OpGrad : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("XShape"), "Input(XShape) should not be null");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
-                   "Input(Out@GRAD) should not be null");
+    OP_INOUT_CHECK(ctx->HasInput("XShape"), "Input", "XShape",
+                   "Transpose2OpGrad");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
+                   framework::GradVarName("Out"), "Transpose2OpGrad");
     if (ctx->HasOutput(framework::GradVarName("X"))) {
       auto xshape_dim = ctx->GetInputDim("XShape");
       auto x_shape_dim =

paddle/fluid/operators/transpose_op.h

@@ -53,7 +53,10 @@ inline void TransCompute(const int dim, const DeviceContext& dev_ctx,
       trans6(dev_ctx, in, out, axis);
       break;
     default:
-      PADDLE_THROW("Tensors with rank at most 6 are supported");
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Tensors with rank at most 6 are supported"
+          ", but received input tensor's rank is %d,",
+          dim));
   }
 }
 

python/paddle/fluid/nets.py

@@ -305,6 +305,8 @@ def sequence_conv_pool(input,
                                                      act="tanh",
                                                      pool_type="sqrt")
     """
+
+    check_variable_and_dtype(input, 'input', ['float32', 'float64'], 'input')
     conv_out = layers.sequence_conv(
         input=input,
         num_filters=num_filters,