Add Top-k Python API. (#9973)

* Add topk Python API.

* Add unit test.

* Remove the repeated API.

doc/fluid/api/layers.rst

@@ -815,3 +815,8 @@ zeros
 ..  autofunction:: paddle.fluid.layers.zeros
     :noindex:
 
+topk
+----
+
+..  autofunction:: paddle.fluid.layers.topk
+    :noindex:

paddle/fluid/operators/top_k_op.h

@@ -24,7 +24,6 @@ namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
-using LoDTensor = framework::LoDTensor;
 
 template <typename T, int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>
@@ -36,9 +35,9 @@ class TopkKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     // Get the top k elements of each row of input tensor
     // FIXME: only deal with matrix(2d tensor).
-    auto* input = ctx.Input<LoDTensor>("X");
+    auto* input = ctx.Input<Tensor>("X");
-    auto* output = ctx.Output<LoDTensor>("Out");
+    auto* output = ctx.Output<Tensor>("Out");
-    auto* indices = ctx.Output<LoDTensor>("Indices");
+    auto* indices = ctx.Output<Tensor>("Indices");
     // k is determined by Attr
     const size_t k = static_cast<int>(ctx.Attr<int>("k"));
 

python/paddle/fluid/layers/control_flow.py

@@ -32,7 +32,6 @@ __all__ = [
     'Switch',
     'lod_rank_table',
     'max_sequence_len',
-    'topk',
     'lod_tensor_to_array',
     'array_to_lod_tensor',
     'increment',
@@ -751,43 +750,6 @@ def max_sequence_len(rank_table):
     return res
 
 
-def topk(input, k):
-    """
-    **topk**
-
-    This function performs the operation that selects the k entries in the input
-    vector and outputs their values and indices as vectors. Thus topk_out[j] is
-    the j-th largest entry in input, and its index is topk_indices[j]
-
-    Args:
-        input (Variable|list): The input tensor that has all the data.
-        k (int): The number of top elements that the function will pick.
-
-    Returns:
-        Variable: The variable of type array that contains the k largest entries
-                  from input.
-        Variable: The variable of type array that contains the indices of k
-                  largest entries from input.
-
-    Examples:
-        .. code-block:: python
-
-          x = fluid.layers.data(name='x', shape=[10])
-          k = 5
-          array = fluid.layers.topk(x, k)
-    """
-    helper = LayerHelper('topk', **locals())
-    topk_out = helper.create_tmp_variable(dtype=input.dtype)
-    topk_indices = helper.create_tmp_variable(dtype='int64')
-    helper.append_op(
-        type='top_k',
-        inputs={'X': [input]},
-        outputs={'Out': [topk_out],
-                 'Indices': [topk_indices]},
-        attrs={'k': k})
-    return topk_out, topk_indices
-
-
 def lod_tensor_to_array(x, table):
     """ Convert a LOD_TENSOR to an LOD_TENSOR_ARRAY.
 

python/paddle/fluid/layers/metric.py

@@ -20,6 +20,7 @@ from ..layer_helper import LayerHelper
 from ..initializer import Normal, Constant
 from ..framework import Variable
 from ..param_attr import ParamAttr
+import nn
 
 __all__ = ['accuracy', 'auc']
 
@@ -27,17 +28,10 @@ __all__ = ['accuracy', 'auc']
 def accuracy(input, label, k=1, correct=None, total=None):
     """
     This function computes the accuracy using the input and label.
-    The output is the top_k inputs and their indices.
+    The output is the top k inputs and their indices.
     """
     helper = LayerHelper("accuracy", **locals())
-    topk_out = helper.create_tmp_variable(dtype=input.dtype)
+    topk_out, topk_indices = nn.topk(input, k=k)
-    topk_indices = helper.create_tmp_variable(dtype="int64")
-    helper.append_op(
-        type="top_k",
-        inputs={"X": [input]},
-        outputs={"Out": [topk_out],
-                 "Indices": [topk_indices]},
-        attrs={"k": k})
     acc_out = helper.create_tmp_variable(dtype="float32")
     if correct is None:
         correct = helper.create_tmp_variable(dtype="int64")
@@ -68,12 +62,7 @@ def auc(input, label, curve='ROC', num_thresholds=200):
     helper = LayerHelper("auc", **locals())
     topk_out = helper.create_tmp_variable(dtype=input.dtype)
     topk_indices = helper.create_tmp_variable(dtype="int64")
-    helper.append_op(
+    topk_out, topk_indices = nn.topk(input, k=k)
-        type="top_k",
-        inputs={"X": [input]},
-        outputs={"Out": [topk_out],
-                 "Indices": [topk_indices]},
-        attrs={"k": k})
     auc_out = helper.create_tmp_variable(dtype="float32")
     if correct is None:
         correct = helper.create_tmp_variable(dtype="int64")

python/paddle/fluid/layers/nn.py

@@ -60,6 +60,7 @@ __all__ = [
     'edit_distance',
     'l2_normalize',
     'matmul',
+    'topk',
     'warpctc',
     'sequence_reshape',
     'transpose',
@@ -2576,6 +2577,53 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
     return out
 
 
+def topk(input, k):
+    """
+    This operator is used to find values and indices of the k largest entries
+    for the last dimension.
+
+    If the input is a vector (rank=1), finds the k largest entries in the vector
+    and outputs their values and indices as vectors. Thus values[j] is the j-th
+    largest entry in input, and its index is indices[j].
+
+    If the input is a Tensor with higher rank, this operator computes the top k
+    entries along the last dimension.
+
+    Args:
+        input(Variable): The input variable which can be a vector or Tensor with
+            higher rank.
+        k(int): An integer value to specify the top k largest elements.
+
+    Returns:
+        values(Variable): The k largest elements along each last dimensional
+            slice.
+        indices(Variable): The indices of values within the last dimension of
+            input.
+
+    Examples:
+        .. code-block:: python
+
+            top5_values, top5_indices = layers.topk(input, k=5)
+    """
+    shape = input.shape
+    if k < 1 and k >= shape[-1]:
+        raise ValueError("k must be greater than 0 and less than %d." %
+                         (shape[-1]))
+
+    helper = LayerHelper("top_k", **locals())
+    values = helper.create_tmp_variable(dtype=input.dtype)
+    indices = helper.create_tmp_variable(dtype="int64")
+    helper.append_op(
+        type="top_k",
+        inputs={"X": [input]},
+        outputs={"Out": [values],
+                 "Indices": [indices]},
+        attrs={"k": k})
+    values.stop_gradient = True
+    indices.stop_gradient = True
+    return values, indices
+
+
 def edit_distance(input, label, normalized=True, ignored_tokens=None,
                   name=None):
     """
@@ -2717,15 +2765,7 @@ def ctc_greedy_decoder(input, blank, name=None):
             cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0)
     """
     helper = LayerHelper("ctc_greedy_decoder", **locals())
-    # top 1 op
+    _, topk_indices = topk(input, k=1)
-    topk_out = helper.create_tmp_variable(dtype=input.dtype)
-    topk_indices = helper.create_tmp_variable(dtype="int64")
-    helper.append_op(
-        type="top_k",
-        inputs={"X": [input]},
-        outputs={"Out": [topk_out],
-                 "Indices": [topk_indices]},
-        attrs={"k": 1})
 
     # ctc align op
     ctc_out = helper.create_tmp_variable(dtype="int64")

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

@@ -350,6 +350,15 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(smooth_label)
         print(str(program))
 
+    def test_topk(self):
+        program = Program()
+        with program_guard(program):
+            data = layers.data(name="label", shape=[200], dtype="float32")
+            values, indices = layers.topk(data, k=5)
+            self.assertIsNotNone(values)
+            self.assertIsNotNone(indices)
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()