PaddlePaddle
diff --git a/‎paddle/fluid/operators/unique_consecutive_op.cc‎
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+/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/operators/unique_consecutive_op.h"
+#include "paddle/fluid/framework/op_version_registry.h"
+
+namespace paddle {
+namespace operators {
+
+class UniqueConsecutiveOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "unique_consecutive");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out",
+                   "unique_consecutive");
+
+    auto in_dims = ctx->GetInputDim("X");
+    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_inverse) {
+      OP_INOUT_CHECK(ctx->HasOutput("Index"), "Output", "Index",
+                     "unique_consecutive");
+    }
+    if (return_counts) {
+      OP_INOUT_CHECK(ctx->HasOutput("Counts"), "Output", "Counts",
+                     "unique_consecutive");
+    }
+
+    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_counts) {
+      ctx->SetOutputDim("Counts", {-1});
+    }
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace());
+  }
+};
+
+class UniqueConsecutiveOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "The input tensor of unique_consecutive op.");
+    AddAttr<int>("dtype",
+                 "(int, default 5(FP32)) "
+                 "data type for output index")
+        .SetDefault(framework::proto::VarType::FP32);
+
+    AddOutput("Out", "A unique consecutive subsequence for input tensor.");
+    AddOutput("Index",
+              "The indices for where elements in the original input ended up "
+              "in the returned unique tensor.")
+        .AsDispensable();
+    AddOutput("Counts", "The counts for each unique element.").AsDispensable();
+    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({});
+    AddComment(R"DOC(
+    This function is different from paddle.unique() in the sense that this
+    function only eliminates consecutive duplicate values.
+)DOC");
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(unique_consecutive, ops::UniqueConsecutiveOp,
+                             ops::UniqueConsecutiveOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    unique_consecutive,
+    ops::UniqueConsecutiveKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::UniqueConsecutiveKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::UniqueConsecutiveKernel<paddle::platform::CPUDeviceContext, int32_t>,
+    ops::UniqueConsecutiveKernel<paddle::platform::CPUDeviceContext, int64_t>);
+REGISTER_OP_VERSION(unique_consecutive)
+    .AddCheckpoint(
+        R"ROC(
+        Upgrade unique_consecutive, add 2 outputs [Indices, Counts] and 3 attribute
+        [return_inverse, return_counts, axis].
+      )ROC",
+        paddle::framework::compatible::OpVersionDesc()
+            .NewOutput("Counts", "The counts for each unique element.")
+            .NewAttr("return_inverse",
+                     "If True, also return the indices for where elements"
+                     " in the original input ended up in the returned unique "
+                     "tensor.",
+                     false)
+            .NewAttr("return_counts",
+                     "If True, also return the counts for each unique element.",
+                     false)
+            .NewAttr("axis",
+                     "The axis to apply unique. If None, the input will be "
+                     "flattened.",
+                     std::vector<int>{}));