improve unique op (#26537)

* add unique_v2 op

* remove unique_v2 op

* update doc

paddle/fluid/operators/unique_op.cc

@@ -24,17 +24,63 @@ class UniqueOp : public framework::OperatorWithKernel {
   void InferShape(framework::InferShapeContext* ctx) const override {
     OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "unique");
     OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "unique");
-    OP_INOUT_CHECK(ctx->HasOutput("Index"), "Output", "Index", "unique");
-
     auto in_dims = ctx->GetInputDim("X");
-    PADDLE_ENFORCE_EQ(
-        in_dims.size(), 1,
-        platform::errors::InvalidArgument("The Input(X) should be 1-D Tensor, "
+    if (!ctx->Attrs().Get<bool>("is_sorted")) {
+      OP_INOUT_CHECK(ctx->HasOutput("Index"), "Output", "Index", "unique");
+      PADDLE_ENFORCE_EQ(in_dims.size(), 1,
+                        platform::errors::InvalidArgument(
+                            "The Input(X) should be 1-D Tensor, "
                             "But now the dims of Input(X) is %d.",
                             in_dims.size()));
 
       ctx->SetOutputDim("Out", {-1});
       ctx->SetOutputDim("Index", in_dims);
+      return;
+    }
+
+    bool return_index = ctx->Attrs().Get<bool>("return_index");
+    bool return_inverse = ctx->Attrs().Get<bool>("return_inverse");
+    bool return_counts = ctx->Attrs().Get<bool>("return_counts");
+    auto axis_vec = ctx->Attrs().Get<std::vector<int>>("axis");
+
+    if (return_index) {
+      OP_INOUT_CHECK(ctx->HasOutput("Indices"), "Output", "Indices", "unique");
+    }
+    if (return_inverse) {
+      OP_INOUT_CHECK(ctx->HasOutput("Index"), "Output", "Index", "unique");
+    }
+    if (return_counts) {
+      OP_INOUT_CHECK(ctx->HasOutput("Counts"), "Output", "Counts", "unique");
+    }
+
+    if (axis_vec.empty()) {
+      ctx->SetOutputDim("Out", {-1});
+      if (return_inverse) {
+        ctx->SetOutputDim("Index", {framework::product(in_dims)});
+      }
+    } else {
+      int axis = axis_vec[0];
+      if (axis < 0) {
+        axis += in_dims.size();
+      }
+      PADDLE_ENFORCE_LT(
+          axis, in_dims.size(),
+          platform::errors::InvalidArgument("The axis(%d) should be less than "
+                                            "the dimension size(%d) of x.",
+                                            axis, in_dims.size()));
+      auto out_dims = in_dims;
+      out_dims[axis] = -1;
+      ctx->SetOutputDim("Out", out_dims);
+      if (return_inverse) {
+        ctx->SetOutputDim("Index", {in_dims[axis]});
+      }
+    }
+    if (return_index) {
+      ctx->SetOutputDim("Indices", {-1});
+    }
+    if (return_counts) {
+      ctx->SetOutputDim("Counts", {-1});
+    }
   }
 
  protected:
@@ -49,14 +95,47 @@ class UniqueOp : public framework::OperatorWithKernel {
 class UniqueOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
-    AddInput("X", "Input tensor. It should be a 1-D tensor.");
+    AddInput("X",
+             "Input tensor. It should be a 1-D tensor when Attr(is_sorted)"
+             " is fasle or a N-D tensor when Attr(is_sorted) is true.");
     AddAttr<int>("dtype", "data type for output index");
     AddOutput("Out", "A unique subsequence for input tensor.");
     AddOutput("Index",
-              "An index tensor pointing to unique subsequence, which has "
-              "identical shape with input tensor and int64 dtype.");
+              "Equivalent to inverse in numpy.unique, "
+              "the indices for where elements in the original input ended up "
+              "in the returned unique tensor.");
+    AddOutput(
+        "Indices",
+        "The indices of the input tensor that result in the unique tensor.")
+        .AsDispensable();
+    AddOutput("Counts", "The counts for each unique element.").AsDispensable();
+    AddAttr<bool>("return_index",
+                  "If True, also return the indices of the input"
+                  " tensor that result in the unique Tensor.")
+        .SetDefault(false);
+    AddAttr<bool>(
+        "return_inverse",
+        "If True, also return the indices for where elements"
+        " in the original input ended up in the returned unique tensor.")
+        .SetDefault(false);
+    AddAttr<bool>("return_counts",
+                  "If True, also return the counts for each unique element.")
+        .SetDefault(false);
+    AddAttr<std::vector<int>>(
+        "axis",
+        "The axis to apply unique. If None, the input will be flattened.")
+        .SetDefault({});
+    AddAttr<bool>("is_sorted",
+                  "If True, the unique elements of X are in ascending order."
+                  "Otherwise, the unique elements are not sorted.")
+        .SetDefault(false);
     AddComment(R"DOC(
-    Return a unique subsequence for 1-D input tensor, and an index tensor pointing to this unique subsequence
+    1. Return a unique subsequence for 1-D input tensor, and an index tensor
+    pointing to this unique subsequence when Attr(is_sorted) is false. This 
+    means paddle.unique is called.
+    
+    2. Returns the unique elements of X in ascending order when Attr(is_sorted)
+    is true. This means fluid.layers.unique is called.
 )DOC");
   }
 };
@@ -65,6 +144,8 @@ class UniqueOpMaker : public framework::OpProtoAndCheckerMaker {
 
 namespace ops = paddle::operators;
 REGISTER_OP_WITHOUT_GRADIENT(unique, ops::UniqueOp, ops::UniqueOpMaker);
-REGISTER_OP_CPU_KERNEL(unique, ops::UniqueKernel<float>,
-                       ops::UniqueKernel<double>, ops::UniqueKernel<int32_t>,
-                       ops::UniqueKernel<int64_t>);
+REGISTER_OP_CPU_KERNEL(
+    unique, ops::UniqueKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::UniqueKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::UniqueKernel<paddle::platform::CPUDeviceContext, int32_t>,
+    ops::UniqueKernel<paddle::platform::CPUDeviceContext, int64_t>);

paddle/fluid/operators/unique_op.h

@@ -13,12 +13,17 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <algorithm>
 #include <cmath>
+#include <numeric>
+#include <set>
 #include <unordered_map>
 #include <utility>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/math/concat_and_split.h"
 #include "paddle/fluid/operators/math/math_function.h"
+#include "paddle/fluid/operators/transpose_op.h"
 
 namespace paddle {
 namespace operators {
@@ -104,17 +109,243 @@ struct UniqueOpFunctor {
   }
 };
 
+static std::vector<framework::Tensor> Unbind(const framework::Tensor& in) {
+  int64_t size = in.dims()[0];
+  std::vector<framework::Tensor> tensors(size);
+  for (int64_t i = 0; i < size; ++i) {
+    tensors[i] = in.Slice(i, i + 1);
+  }
+  return tensors;
+}
+
+template <typename T>
+static bool Equal(const framework::Tensor& a, const framework::Tensor& b) {
+  if (a.numel() != b.numel()) {
+    return false;
+  }
+  for (int64_t i = 0; i < a.numel(); ++i) {
+    if (a.data<T>()[i] != b.data<T>()[i]) {
+      return false;
+    }
+  }
+  return true;
+}
+
 template <typename T>
+static void UniqueFlattendTensor(const framework::ExecutionContext& context,
+                                 const framework::Tensor& in,
+                                 framework::Tensor* out, bool return_index,
+                                 bool return_inverse, bool return_counts) {
+  const T* in_data = in.data<T>();
+  std::set<T> unique(in_data, in_data + in.numel());
+  out->Resize(framework::make_ddim({static_cast<int64_t>(unique.size())}));
+  auto out_data = out->mutable_data<T>(context.GetPlace());
+  std::copy(unique.begin(), unique.end(), out_data);
+
+  if (return_index) {
+    auto* indices = context.Output<framework::Tensor>("Indices");
+    indices->Resize(framework::make_ddim({out->numel()}));
+    auto indices_data = indices->mutable_data<int64_t>(context.GetPlace());
+    std::unordered_map<T, int64_t> indices_map;
+    indices_map.reserve(out->numel());
+    for (int64_t i = 0; i < in.numel(); ++i) {
+      if (indices_map.find(in_data[i]) != indices_map.end()) continue;
+      indices_map[in_data[i]] = i;
+    }
+    for (int64_t i = 0; i < out->numel(); ++i) {
+      indices_data[i] = indices_map[out_data[i]];
+    }
+  }
+
+  if (return_inverse) {
+    auto* inverse = context.Output<framework::Tensor>("Index");
+    inverse->Resize(framework::make_ddim({in.numel()}));
+    auto inverse_data = inverse->mutable_data<int64_t>(context.GetPlace());
+    std::unordered_map<T, int64_t> inverse_map;
+    inverse_map.reserve(out->numel());
+    for (int64_t i = 0; i < out->numel(); ++i) {
+      inverse_map[out_data[i]] = i;
+    }
+    for (int64_t i = 0; i < in.numel(); ++i) {
+      inverse_data[i] = inverse_map[in_data[i]];
+    }
+  }
+
+  if (return_counts) {
+    auto* count = context.Output<framework::Tensor>("Counts");
+    count->Resize(framework::make_ddim({out->numel()}));
+    auto count_data = count->mutable_data<int64_t>(context.GetPlace());
+    std::unordered_map<T, int64_t> counts_map;
+    counts_map.reserve(out->numel());
+    for (int64_t i = 0; i < out->numel(); ++i) {
+      counts_map[out_data[i]] = 0;
+    }
+    for (int64_t i = 0; i < in.numel(); i++) {
+      counts_map[in_data[i]] += 1;
+    }
+    for (int64_t i = 0; i < out->numel(); i++) {
+      count_data[i] = counts_map[out_data[i]];
+    }
+  }
+}
+
+template <class ForwardIt, typename T>
+static ForwardIt UniqueDimImpl(const framework::ExecutionContext& context,
+                               ForwardIt first, ForwardIt last,
+                               const std::vector<int64_t>& sorted_indices_vec,
+                               std::vector<int64_t>* inverse_vec,
+                               std::vector<int64_t>* counts_vec,
+                               std::vector<int64_t>* indices_vec) {
+  if (first == last) {
+    return last;
+  }
+
+  (*inverse_vec)[sorted_indices_vec[0]] = 0;
+  (*counts_vec)[0] = 1;
+  (*indices_vec)[0] = sorted_indices_vec[0];
+
+  ForwardIt begin = first;
+  ForwardIt result = first;
+
+  while (++first != last) {
+    int64_t idx_first = std::distance(begin, first);
+    int64_t idx_result = std::distance(begin, result);
+    if (!Equal<T>(*result, *first)) {
+      if (++result != first) {
+        *result = std::move(*first);
+      }
+      idx_result += 1;
+      (*indices_vec)[idx_result] = sorted_indices_vec[idx_first];
+    }
+    (*inverse_vec)[sorted_indices_vec[idx_first]] = idx_result;
+    (*counts_vec)[idx_result] += 1;
+  }
+  return ++result;
+}
+
+template <typename DeviceContext, typename T>
+static void UniqueDim(const framework::ExecutionContext& context,
+                      const framework::Tensor& in, framework::Tensor* out,
+                      bool return_index, bool return_inverse,
+                      bool return_counts, int axis) {
+  // transpose tensor: eg. axis=1, [dim0, dim1, dim2] -> [dim1, dim0, dim2]
+  std::vector<int> permute(in.dims().size());
+  std::iota(permute.begin(), permute.end(), 0);
+  permute[axis] = 0;
+  permute[0] = axis;
+  std::vector<int64_t> in_trans_dims_vec(framework::vectorize(in.dims()));
+  in_trans_dims_vec[axis] = in.dims()[0];
+  in_trans_dims_vec[0] = in.dims()[axis];
+  framework::Tensor in_trans;
+  framework::DDim in_trans_dims = framework::make_ddim(in_trans_dims_vec);
+  in_trans.Resize(in_trans_dims);
+  in_trans.mutable_data<T>(context.GetPlace());
+  auto& dev_ctx = context.template device_context<DeviceContext>();
+  TransCompute<DeviceContext, T>(in.dims().size(), dev_ctx, in, &in_trans,
+                                 permute);
+  // reshape tensor: eg. [dim1, dim0, dim2] -> [dim1, dim0*dim2]
+  framework::DDim in_trans_flat_dims =
+      framework::flatten_to_2d(in_trans_dims, 1);
+  in_trans.Resize(in_trans_flat_dims);
+
+  // sort indices
+  std::vector<int64_t> sorted_indices_vec(in_trans.dims()[0]);
+  std::iota(sorted_indices_vec.begin(), sorted_indices_vec.end(), 0);
+  int64_t col = in_trans.dims()[1];
+  const T* in_trans_data = in_trans.data<T>();
+  std::sort(sorted_indices_vec.begin(), sorted_indices_vec.end(),
+            [&](int64_t a, int64_t b) -> bool {
+              for (int64_t i = 0; i < col; ++i) {
+                T lhs = in_trans_data[i + a * col];
+                T rhs = in_trans_data[i + b * col];
+                if (lhs < rhs) {
+                  return true;
+                } else if (lhs > rhs) {
+                  return false;
+                }
+              }
+              return false;
+            });
+
+  // sort tensor according to indices
+  framework::Tensor input_sorted;
+  input_sorted.Resize(in_trans_dims);
+  input_sorted.mutable_data<T>(context.GetPlace());
+  T* input_sorted_data = input_sorted.data<T>();
+  for (size_t i = 0; i < sorted_indices_vec.size(); ++i) {
+    memcpy(input_sorted_data + i * col,
+           in_trans_data + sorted_indices_vec[i] * col, col * sizeof(T));
+  }
+
+  std::vector<framework::Tensor> input_unbind = Unbind(input_sorted);
+  std::vector<int64_t> inverse_vec(sorted_indices_vec.size(), 0);
+  std::vector<int64_t> counts_vec(sorted_indices_vec.size(), 0);
+  std::vector<int64_t> indices_vec(sorted_indices_vec.size(), 0);
+  auto last = UniqueDimImpl<std::vector<framework::Tensor>::iterator, T>(
+      context, input_unbind.begin(), input_unbind.end(), sorted_indices_vec,
+      &inverse_vec, &counts_vec, &indices_vec);
+  input_unbind.erase(last, input_unbind.end());
+  counts_vec.erase(counts_vec.begin() + input_unbind.size(), counts_vec.end());
+  indices_vec.erase(indices_vec.begin() + input_unbind.size(),
+                    indices_vec.end());
+
+  math::ConcatFunctor<DeviceContext, T> concat_functor;
+  framework::Tensor out_trans;
+  std::vector<int64_t> out_trans_dims_vec = in_trans_dims_vec;
+  out_trans_dims_vec[0] = input_unbind.size();
+  out_trans.Resize(framework::make_ddim(out_trans_dims_vec));
+  out_trans.mutable_data<T>(context.GetPlace());
+  std::swap(out_trans_dims_vec[0], out_trans_dims_vec[axis]);
+  out->Resize(framework::make_ddim(out_trans_dims_vec));
+  out->mutable_data<T>(context.GetPlace());
+  concat_functor(dev_ctx, input_unbind, 0, &out_trans);
+  TransCompute<DeviceContext, T>(out_trans.dims().size(), dev_ctx, out_trans,
+                                 out, permute);
+
+  if (return_inverse) {
+    auto* inverse = context.Output<framework::Tensor>("Index");
+    framework::TensorFromVector(inverse_vec, context.device_context(), inverse);
+  }
+
+  if (return_counts) {
+    auto* count = context.Output<framework::Tensor>("Counts");
+    framework::TensorFromVector(counts_vec, context.device_context(), count);
+  }
+
+  if (return_index) {
+    auto* indices = context.Output<framework::Tensor>("Indices");
+    framework::TensorFromVector(indices_vec, context.device_context(), indices);
+  }
+}
+
+template <typename DeviceContext, typename T>
 class UniqueKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto data_type = static_cast<framework::proto::VarType::Type>(
-        context.Attr<int>("dtype"));
     auto* x = context.Input<framework::Tensor>("X");
     auto* out = context.Output<framework::Tensor>("Out");
+    if (!context.Attr<bool>("is_sorted")) {
+      auto data_type = static_cast<framework::proto::VarType::Type>(
+          context.Attr<int>("dtype"));
       auto* index = context.Output<framework::Tensor>("Index");
 
       framework::VisitDataType(data_type, UniqueOpFunctor<T>(out, index, x));
+      return;
+    }
+
+    std::vector<int> axis_vec = context.Attr<std::vector<int>>("axis");
+    bool return_index = context.Attr<bool>("return_index");
+    bool return_inverse = context.Attr<bool>("return_inverse");
+    bool return_counts = context.Attr<bool>("return_counts");
+
+    if (axis_vec.empty()) {
+      UniqueFlattendTensor<T>(context, *x, out, return_index, return_inverse,
+                              return_counts);
+    } else {
+      int axis = axis_vec[0];
+      UniqueDim<DeviceContext, T>(context, *x, out, return_index,
+                                  return_inverse, return_counts, axis);
+    }
   }
 };
 

paddle/fluid/pybind/op_function_generator.cc

@@ -62,6 +62,7 @@ std::map<std::string, std::set<std::string>> op_outs_map = {
     {"sync_batch_norm",
      {"Y", "MeanOut", "VarianceOut", "SavedMean", "SavedVariance",
       "ReserveSpace"}},
+    {"unique", {"Out", "Index", "Indices", "Counts"}},
 };
 
 // NOTE(zhiqiu): Commonly, the outputs in auto-generated OP function are

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

@@ -17,6 +17,7 @@ from __future__ import print_function
 import unittest
 import numpy as np
 from op_test import OpTest
+import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.op import Operator
@@ -125,5 +126,164 @@ class TestRandomGPU(TestUniqueOp):
             self.check_output_with_place(place, atol=1e-5)
 
 
+class TestSortedUniqueOp(TestUniqueOp):
+    def init_config(self):
+        self.inputs = {'X': np.array([2, 3, 3, 1, 5, 3], dtype='int64')}
+        unique, indices, inverse, count = np.unique(
+            self.inputs['X'],
+            return_index=True,
+            return_inverse=True,
+            return_counts=True,
+            axis=None)
+        self.attrs = {
+            'dtype': int(core.VarDesc.VarType.INT32),
+            "return_index": True,
+            "return_inverse": True,
+            "return_counts": True,
+            "axis": None,
+            "is_sorted": True
+        }
+        self.outputs = {
+            'Out': unique,
+            'Indices': indices,
+            "Index": inverse,
+            "Counts": count,
+        }
+
+
+class TestUniqueOpAxisNone(TestUniqueOp):
+    def init_config(self):
+        self.inputs = {'X': np.random.random((4, 7, 10)).astype('float64')}
+        unique, indices, inverse, counts = np.unique(
+            self.inputs['X'],
+            return_index=True,
+            return_inverse=True,
+            return_counts=True,
+            axis=None)
+        self.attrs = {
+            'dtype': int(core.VarDesc.VarType.INT32),
+            "return_index": True,
+            "return_inverse": True,
+            "return_counts": True,
+            "axis": None,
+            "is_sorted": True
+        }
+        self.outputs = {
+            'Out': unique,
+            'Indices': indices,
+            "Index": inverse,
+            "Counts": counts,
+        }
+
+
+class TestUniqueOpAxis1(TestUniqueOp):
+    def init_config(self):
+        self.inputs = {'X': np.random.random((3, 8, 8)).astype('float64')}
+        unique, indices, inverse, counts = np.unique(
+            self.inputs['X'],
+            return_index=True,
+            return_inverse=True,
+            return_counts=True,
+            axis=1)
+        self.attrs = {
+            'dtype': int(core.VarDesc.VarType.INT32),
+            "return_index": True,
+            "return_inverse": True,
+            "return_counts": True,
+            "axis": [1],
+            "is_sorted": True
+        }
+        self.outputs = {
+            'Out': unique,
+            'Indices': indices,
+            "Index": inverse,
+            "Counts": counts,
+        }
+
+
+class TestUniqueAPI(unittest.TestCase):
+    def test_dygraph_api_out(self):
+        paddle.disable_static()
+        x_data = x_data = np.random.randint(0, 10, (120))
+        x = paddle.to_tensor(x_data)
+        out = paddle.unique(x)
+        expected_out = np.unique(x_data)
+        self.assertTrue((out.numpy() == expected_out).all(), True)
+        paddle.enable_static()
+
+    def test_dygraph_api_attr(self):
+        paddle.disable_static()
+        x_data = np.random.random((3, 5, 5)).astype("float32")
+        x = paddle.to_tensor(x_data)
+        out, index, inverse, counts = paddle.unique(
+            x,
+            return_index=True,
+            return_inverse=True,
+            return_counts=True,
+            axis=0)
+        np_out, np_index, np_inverse, np_counts = np.unique(
+            x_data,
+            return_index=True,
+            return_inverse=True,
+            return_counts=True,
+            axis=0)
+        self.assertTrue((out.numpy() == np_out).all(), True)
+        self.assertTrue((index.numpy() == np_index).all(), True)
+        self.assertTrue((inverse.numpy() == np_inverse).all(), True)
+        self.assertTrue((counts.numpy() == np_counts).all(), True)
+        paddle.enable_static()
+
+    def test_static_graph(self):
+        with paddle.static.program_guard(paddle.static.Program(),
+                                         paddle.static.Program()):
+            x = paddle.data(name='x', shape=[3, 2], dtype='float64')
+            unique, inverse, counts = paddle.unique(
+                x, return_inverse=True, return_counts=True, axis=0)
+            place = paddle.CPUPlace()
+            exe = paddle.static.Executor(place)
+            x_np = np.array([[1, 2], [3, 4], [1, 2]]).astype('float64')
+            result = exe.run(feed={"x": x_np},
+                             fetch_list=[unique, inverse, counts])
+        np_unique, np_inverse, np_counts = np.unique(
+            x_np, return_inverse=True, return_counts=True, axis=0)
+        self.assertTrue(np.allclose(result[0], np_unique))
+        self.assertTrue(np.allclose(result[1], np_inverse))
+        self.assertTrue(np.allclose(result[2], np_counts))
+
+
+class TestUniqueError(unittest.TestCase):
+    def test_input_dtype(self):
+        def test_x_dtype():
+            with paddle.static.program_guard(paddle.static.Program(),
+                                             paddle.static.Program()):
+                x = paddle.data(name='x', shape=[10, 10], dtype='float16')
+                result = paddle.unique(x)
+
+            self.assertRaises(TypeError, test_x_dtype)
+
+    def test_attr(self):
+        x = paddle.data(name='x', shape=[10, 10], dtype='float64')
+
+        def test_return_index():
+            result = paddle.unique(x, return_index=0)
+
+        self.assertRaises(TypeError, test_return_index)
+
+        def test_return_inverse():
+            result = paddle.unique(x, return_inverse='s')
+
+        self.assertRaises(TypeError, test_return_inverse)
+
+        def test_return_counts():
+            result = paddle.unique(x, return_counts=3)
+
+        self.assertRaises(TypeError, test_return_counts)
+
+        def test_axis():
+            result = paddle.unique(x, axis='12')
+
+        self.assertRaises(TypeError, test_axis)
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/tensor/manipulation.py

@@ -27,7 +27,6 @@ from ..fluid.layers import expand_as  #DEFINE_ALIAS
 from ..fluid.layers import slice  #DEFINE_ALIAS
 from ..fluid.layers import strided_slice  #DEFINE_ALIAS
 from ..fluid.layers import transpose  #DEFINE_ALIAS
-from ..fluid.layers import unique  #DEFINE_ALIAS
 from ..fluid.layers import unstack  #DEFINE_ALIAS
 
 from ..fluid.layers import scatter_nd_add  #DEFINE_ALIAS
@@ -608,6 +607,126 @@ def squeeze(x, axis=None, name=None):
     return layers.squeeze(x, axis, name)
 
 
+def unique(x,
+           return_index=False,
+           return_inverse=False,
+           return_counts=False,
+           axis=None,
+           name=None):
+    """
+    Returns the unique elements of `x` in ascending order.
+
+    Args:
+        x(Tensor): The input tensor, it's data type should be float32, float64, int32, int64.
+        return_index(bool, optional): If True, also return the indices of the input tensor that
+            result in the unique Tensor.
+        return_inverse(bool, optional): If True, also return the indices for where elements in
+            the original input ended up in the returned unique tensor.
+        return_counts(bool, optional): If True, also return the counts for each unique element.
+        axis(int, optional): The axis to apply unique. If None, the input will be flattened.
+            Default: None.
+        name(str, optional): Name for the operation. For more information, please refer to
+            :ref:`api_guide_Name`. Default: None.
+
+    Returns: 
+        tuple: (out, indices, inverse, counts). `out` is the unique tensor for `x`. `indices` is \
+            provided only if `return_index` is True. `inverse` is provided only if `return_inverse` \
+            is True. `counts` is provided only if `return_counts` is True.
+
+    Examples:
+        .. code-block:: python
+
+            import numpy as np
+            import paddle
+
+            paddle.disable_static()
+            x_data = np.array([2, 3, 3, 1, 5, 3])
+            x = paddle.to_tensor(x_data)
+            unique = paddle.unique(x)
+            np_unique = unique.numpy() # [1 2 3 5]
+            _, indices, inverse, counts = paddle.unique(x, return_index=True, return_inverse=True, return_counts=True)
+            np_indices = indices.numpy() # [3 0 1 4]
+            np_inverse = inverse.numpy() # [1 2 2 0 3 2]
+            np_counts = counts.numpy() # [1 1 3 1]
+
+            x_data = np.array([[2, 1, 3], [3, 0, 1], [2, 1, 3]])
+            unique = paddle.unique(x)
+            np_unique = unique.numpy() # [0 1 2 3]
+
+            unique = paddle.unique(x, axis=0)
+            np_unique = unique.numpy() 
+            # [[2 1 3]
+            #  [3 0 1]]
+    """
+    if axis is None:
+        axis = []
+    else:
+        axis = [axis]
+
+    if in_dygraph_mode():
+        out, inverse, indices, counts = core.ops.unique(
+            x, 'dtype',
+            convert_np_dtype_to_dtype_('int32'), 'return_index', return_index,
+            'return_inverse', return_inverse, 'return_counts', return_counts,
+            'axis', axis, "is_sorted", True)
+        outs = [out]
+        if return_index:
+            outs.append(indices)
+        if return_inverse:
+            outs.append(inverse)
+        if return_counts:
+            outs.append(counts)
+
+        if len(outs) == 1:
+            return outs[0]
+
+        return tuple(outs)
+
+    check_variable_and_dtype(x, "input",
+                             ['float32', 'float64', 'int32', 'int64'], 'unique')
+    check_type(return_index, 'return_index', bool, 'unique')
+    check_type(return_inverse, 'return_inverse', bool, 'unique')
+    check_type(return_counts, 'return_counts', bool, 'unique')
+    if len(axis) != 0:
+        check_type(axis[0], 'axis', int, 'unique')
+
+    helper = LayerHelper('unique', **locals())
+    attrs = {
+        'dtype': int(core.VarDesc.VarType.INT32),
+        "return_index": return_index,
+        "return_inverse": return_inverse,
+        "return_counts": return_counts,
+        "axis": axis,
+        "is_sorted": True
+    }
+    out = helper.create_variable_for_type_inference(
+        dtype=x.dtype, stop_gradient=True)
+    inverse = helper.create_variable_for_type_inference(
+        dtype=core.VarDesc.VarType.INT64, stop_gradient=True)
+    outputs = {"Out": out, "Index": inverse}
+    outs = [out]
+    if return_index:
+        indices = helper.create_variable_for_type_inference(
+            dtype=core.VarDesc.VarType.INT64, stop_gradient=True)
+        outputs["Indices"] = indices
+        outs.append(indices)
+    if return_inverse:
+        outs.append(inverse)
+    if return_counts:
+        counts = helper.create_variable_for_type_inference(
+            dtype=core.VarDesc.VarType.INT64, stop_gradient=True)
+        outputs["Counts"] = counts
+        outs.append(counts)
+
+    helper.append_op(
+        type="unique", inputs={"X": x}, attrs=attrs, outputs=outputs)
+
+    if len(outs) == 1:
+        return outs[0]
+
+    return tuple(outs)
+
+
 def unsqueeze(x, axis, name=None):
     """
 	:alias_main: paddle.unsqueeze