fix reshape error: (Repeated layer name: reshape (layers must have distinct names)) (#54072)

paddle/fluid/inference/tensorrt/convert/cumsum_op.cc

@@ -115,7 +115,10 @@ class CumsumOpConverter : public OpConverter {
                             [axis](int x) { return x == axis; });
     subscripts.resize(p - subscripts.begin());
     auto newDims = Gather(Shape(inputSliced_output), subscripts);
-    inputSliced_output = Reshape(inputSliced_output, newDims);
+    inputSliced_output =
+        Reshape(inputSliced_output,
+                newDims,
+                ("cumsum: reshape: (Output(" + output_name + ")").c_str());
 
     // creat ZeroTensor
     std::vector<float> zero_vec{0.f};
@@ -127,7 +130,11 @@ class CumsumOpConverter : public OpConverter {
                engine_,
                ElementWise,
                *inputSliced_output,
-               *BroadcastTensors(cast->getOutput(0), inputSliced_output),
+               *BroadcastTensors(cast->getOutput(0),
+                                 inputSliced_output,
+                                 ("cumsum: reshape_for_broadcast: (Output(" +
+                                  output_name + ")")
+                                     .c_str()),
                nvinfer1::ElementWiseOperation::kPROD)
                ->getOutput(0);
 

paddle/fluid/inference/tensorrt/convert/op_converter.h

@@ -403,15 +403,18 @@ class OpConverter {
 
   nvinfer1::ITensor* Reshape(nvinfer1::ITensor* input,
                              nvinfer1::ITensor* newShape,
-                             const std::string& name = "reshape") {
+                             const std::string& name = "") {
     auto* shuffle = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input);
     shuffle->setInput(1, *newShape);
-    shuffle->setName(name.c_str());
+    if (name != "") {
+      shuffle->setName(name.c_str());
+    }
     return shuffle->getOutput(0);
   }
 
   nvinfer1::ITensor* BroadcastTensor(nvinfer1::ITensor* input,
-                                     const int nbDims) {
+                                     const int nbDims,
+                                     const std::string& name = "") {
     auto oldShape = Shape(input);
     auto oldShapeDims = oldShape->getDimensions();
     const int rank = oldShapeDims.nbDims;
@@ -427,22 +430,23 @@ class OpConverter {
       itensors.push_back(one_rank_tensor);
       itensors.push_back(oldShape);
       concat_shape_tensor = Concat(itensors);
-      input = Reshape(input, concat_shape_tensor);
+      input = Reshape(input, concat_shape_tensor, name);
     }
     return input;
   }
 
   nvinfer1::ITensor* BroadcastTensors(nvinfer1::ITensor* a,
-                                      nvinfer1::ITensor* b) {
+                                      nvinfer1::ITensor* b,
+                                      const std::string& name = "") {
     const int aDims = a->getDimensions().nbDims;
     const int bDims = b->getDimensions().nbDims;
     if (aDims == bDims) {
       VLOG(3) << "Broadcast two equal rank tensors";
     }
     if (aDims > bDims) {
-      return BroadcastTensor(b, aDims);
+      return BroadcastTensor(b, aDims, name);
     }
-    return BroadcastTensor(a, bDims);
+    return BroadcastTensor(a, bDims, name);
   }
 
   // Concat not make rank changed