cherry-pick Fix topk cannot handle 1D vector bug (#18466)

Add path to handle 1D vector

paddle/fluid/operators/top_k_op.h

@@ -29,6 +29,10 @@ template <typename T, int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>
 using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
 
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
+
 template <typename DeviceContext, typename T>
 class TopkKernel : public framework::OpKernel<T> {
  public:
@@ -57,17 +61,24 @@ class TopkKernel : public framework::OpKernel<T> {
         framework::slice_ddim(inputdims, 0, inputdims.size() - 1));
     const size_t col = inputdims[inputdims.size() - 1];
     Eigen::DSizes<int, 2> flat2dims(row, col);
-    // NOTE: eigen shape doesn't affect paddle tensor.
-    auto eg_input = EigenMatrix<T>::Reshape(*input, inputdims.size() - 1);
-
+// NOTE: eigen shape doesn't affect paddle tensor.
 #ifdef PADDLE_WITH_MKLML
 #pragma omp parallel for
 #endif
     for (size_t i = 0; i < row; i++) {
       std::vector<std::pair<T, size_t>> vec;
       vec.reserve(col);
-      for (size_t j = 0; j < col; j++) {
-        vec.push_back(std::pair<T, size_t>(eg_input(i, j), j));
+      // 1D vector
+      if (inputdims.size() == 1) {
+        auto eg_input = EigenVector<T>::Flatten(*input);
+        for (size_t j = 0; j < col; j++) {
+          vec.push_back(std::pair<T, size_t>(eg_input(j), j));
+        }
+      } else {
+        auto eg_input = EigenMatrix<T>::Reshape(*input, inputdims.size() - 1);
+        for (size_t j = 0; j < col; j++) {
+          vec.push_back(std::pair<T, size_t>(eg_input(i, j), j));
+        }
       }
 
       std::partial_sort(

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

@@ -87,6 +87,28 @@ class TestTopkOp3d(OpTest):
         self.check_output()
 
 
+class TestTopkOp1(OpTest):
+    def setUp(self):
+        self.op_type = "top_k"
+        k = 2
+        m = 2056
+        input = np.random.random(m).astype("float32")
+        output = np.ndarray(k)
+        indices = np.ndarray(k).astype("int64")
+
+        self.inputs = {'X': input}
+        self.attrs = {'k': k}
+
+        row = input
+        output = -np.sort(-row)[:k]
+        indices = (-row).argsort()[:k]
+
+        self.outputs = {'Out': output, 'Indices': indices}
+
+    def test_check_output(self):
+        self.check_output()
+
+
 class TestTopkOp2(OpTest):
     def setUp(self):
         self.op_type = "top_k"