refine concat_op

paddle/fluid/operators/CMakeLists.txt

@@ -184,6 +184,7 @@ op_library(save_op DEPS lod_tensor)
 op_library(load_op DEPS lod_tensor)
 op_library(save_combine_op DEPS lod_tensor)
 op_library(load_combine_op DEPS lod_tensor)
+op_library(concat_op DEPS concat_functor)
 
 list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
 foreach(src ${GENERAL_OPS})

paddle/fluid/operators/concat_op.cc

@@ -100,7 +100,8 @@ class ConcatOpGrad : public framework::OperatorWithKernel {
 namespace ops = paddle::operators;
 REGISTER_OP_EX(concat, ops::ConcatOp, ops::ConcatOpMaker, concat_grad,
                ops::ConcatOpGrad, false)
-REGISTER_OP_CPU_KERNEL(concat,
-                       ops::ConcatKernel<paddle::platform::CPUPlace, float>)
-REGISTER_OP_CPU_KERNEL(concat_grad,
-                       ops::ConcatGradKernel<paddle::platform::CPUPlace, float>)
+REGISTER_OP_CPU_KERNEL(
+    concat, ops::ConcatKernel<paddle::platform::CPUDeviceContext, float>)
+REGISTER_OP_CPU_KERNEL(
+    concat_grad,
+    ops::ConcatGradKernel<paddle::platform::CPUDeviceContext, float>)

paddle/fluid/operators/concat_op.h

@@ -17,6 +17,7 @@ limitations under the License. */
 #include <utility>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/math/concat.h"
 #include "paddle/fluid/operators/strided_memcpy.h"
 
 namespace paddle {
@@ -27,55 +28,17 @@ class ConcatKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto ins = ctx.MultiInput<framework::Tensor>("X");
-    auto* out = ctx.Output<framework::Tensor>("Out");
+    framework::Tensor* out = ctx.Output<framework::Tensor>("Out");
     int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
     auto place = ctx.GetPlace();
     out->mutable_data<T>(place);
-
-    auto out_stride = framework::stride_numel(out->dims());
-
-    size_t output_offset = 0;
-
-    // If axis >=1, copy to out immediately need to call many times
-    // of cuda memcpy. Copy the input to cpu and do the stride copy,
-    // then copy to gpu output.
-
-    if (platform::is_gpu_place(place) && axis >= 1) {
-      platform::CPUPlace copy_place;
-      auto& cpu_ctx = *platform::DeviceContextPool::Instance().Get(copy_place);
-      framework::Tensor cpu_out;
-      cpu_out.Resize(out->dims());
-      cpu_out.mutable_data<T>(copy_place);
-      auto& dev_ctx = ctx.device_context();
-      std::vector<std::unique_ptr<framework::Tensor>> cpu_ins;
-      for (auto* in : ins) {
-        std::unique_ptr<framework::Tensor> cpu_in(new framework::Tensor);
-        framework::TensorCopy(*in, copy_place, dev_ctx, cpu_in.get());
-        cpu_ins.emplace_back(std::move(cpu_in));
-      }
-      // TODO(dzhwinter): overlap copy and compute stream
-      // https://devblogs.nvidia.com/how-overlap-data-transfers-cuda-cc/
-      dev_ctx.Wait();
-
-      for (auto& in : cpu_ins) {
-        auto& cpu_in = *in.get();
-        auto in_stride = framework::stride_numel(cpu_in.dims());
-
-        StridedNumelCopyWithAxis<T>(
-            cpu_ctx, axis, cpu_out.data<T>() + output_offset, out_stride,
-            cpu_in.data<T>(), in_stride, in_stride[axis]);
-        output_offset += in_stride[axis];
-      }
-      framework::TensorCopy(cpu_out, place, dev_ctx, out);
-    } else {
-      for (auto* in : ins) {
-        auto in_stride = framework::stride_numel(in->dims());
-        StridedNumelCopyWithAxis<T>(ctx.device_context(), axis,
-                                    out->data<T>() + output_offset, out_stride,
-                                    in->data<T>(), in_stride, in_stride[axis]);
-        output_offset += in_stride[axis];
-      }
+    std::vector<framework::Tensor> inputs(ins.size());
+    for (size_t j = 0; j < ins.size(); ++j) {
+      inputs[j] = *ins[j];
     }
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    paddle::operators::math::ConcatFunctor<DeviceContext, T> concat_functor;
+    concat_functor(dev_ctx, inputs, static_cast<int>(axis), out);
   }
 };
 

paddle/fluid/operators/math/CMakeLists.txt

@@ -20,6 +20,7 @@ if(WITH_GPU)
     nv_library(unpooling SRCS unpooling.cc unpooling.cu DEPS device_context)
     nv_library(gru_compute SRCS gru_compute.cc gru_compute.cu DEPS device_context activation_functions math_function)
     nv_library(cos_sim_functor SRCS cos_sim_functor.cc cos_sim_functor.cu DEPS device_context)
+    nv_library(concat_functor SRCS concat.cc concat.cu DEPS device_context tensor)
 else()
     cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context framework_proto)
     cc_library(selected_rows_functor SRCS selected_rows_functor.cc DEPS selected_rows math_function)
@@ -37,6 +38,7 @@ else()
     cc_library(unpooling SRCS unpooling.cc DEPS device_context)
     cc_library(gru_compute SRCS gru_compute.cc DEPS device_context activation_functions math_function)
     cc_library(cos_sim_functor SRCS cos_sim_functor.cc DEPS device_context)
+    cc_library(concat_functor SRCS concat.cc DEPS device_context tensor)
 endif()
 
 cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
@@ -44,3 +46,4 @@ cc_test(selected_rows_functor_test SRCS selected_rows_functor_test.cc DEPS selec
 cc_test(im2col_test SRCS im2col_test.cc DEPS math_function tensor)
 cc_test(vol2col_test SRCS vol2col_test.cc DEPS vol2col tensor)
 cc_test(sequence_padding_test SRCS sequence_padding_test.cc DEPS sequence_padding)
+cc_test(concat_test SRCS concat_test.cc DEPS concat_functor tensor)

paddle/fluid/operators/math/concat.cc

+/* Copyright (c) 2018 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/math/concat.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+/*
+ * All tensors' dimension should be the same.
+ */
+template <typename T>
+class ConcatFunctor<platform::CPUDeviceContext, T> {
+ public:
+  void operator()(const platform::CPUDeviceContext& context,
+                  std::vector<framework::Tensor>& input, const int axis,
+                  framework::Tensor* output) {
+    // assume the the max size of input is less than 8 and see the performance
+    // save origin dim
+    int num = input.size();
+    std::vector<paddle::framework::DDim> origin_dim(num);
+    //    for (int j = 0; j < num; ++j) {
+    //      origin_dim[j] = input[j].dims();
+    //    }
+    auto out_dim = output->dims();
+
+    // get the matrix size
+    int rows = 1;
+    auto dim_0 = input[0].dims();
+    for (int i = 0; i < axis; ++i) {
+      rows *= dim_0[i];
+    }
+    int cols = input[0].numel() / rows;
+    int out_rows = rows, out_cols = 0;
+    bool sameShape = true;
+
+    // reshape to matrix
+    for (int i = 0; i < num; ++i) {
+      int t_cols = input[i].numel() / rows;
+      if (sameShape) {
+        if (t_cols != cols) sameShape = false;
+      }
+      out_cols += t_cols;
+      input[i].Resize({rows, t_cols});
+    }
+    output->Resize({out_rows, out_cols});
+    auto& cpu_place = boost::get<platform::CPUPlace>(context.GetPlace());
+    // computation
+    for (int k = 0; k < rows; ++k) {
+      // offset k * out_cols
+      T* dst_ptr = output->data<T>() + k * out_cols;
+      int col_idx = 0;
+      for (int j = 0; j < num; ++j) {
+        int col_len = input[j].dims()[1];
+        const T* src_prt = input[j].data<T>() + k * col_len;
+        memory::Copy(cpu_place, dst_ptr + col_idx, cpu_place, src_prt,
+                     sizeof(T) * col_len);
+        col_idx += col_len;
+      }
+    }
+
+    // recover origin dim
+    //    for (int j = 0; j < num; ++j) {
+    //      input[j]->Resize(origin_dim[j]);
+    //    }
+    output->Resize(out_dim);
+  }
+};
+
+template class ConcatFunctor<platform::CPUDeviceContext, int>;
+template class ConcatFunctor<platform::CPUDeviceContext, int64_t>;
+template class ConcatFunctor<platform::CPUDeviceContext, float>;
+template class ConcatFunctor<platform::CPUDeviceContext, double>;
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/math/concat.cu

+/* Copyright (c) 2018 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/math/concat.h"
+#include "paddle/fluid/platform/cuda_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+// TODO(zcd): This can be replaced by tensor,
+// if that, maybe we should add int8 to VarType::Type.
+// Or replaced by tensorArray.
+static constexpr int MaxSize = 32;
+template <typename T>
+struct CUDADeviceArray {
+  T data[MaxSize];
+  int size;
+};
+
+template <typename T>
+__device__ T upper_bound(const T* first, T count, T val) {
+  const T* orig = first;
+  const T* it = nullptr;
+  T step = 0;
+  while (count > 0) {
+    it = first;
+    step = count / 2;
+    it += step;
+    if (!(val < *it)) {
+      first = ++it;
+      count -= step + 1;
+    } else {
+      count = step;
+    }
+  }
+  return first - orig;
+}
+
+template <typename T>
+__global__ void KernelConcat(const CUDADeviceArray<const T*> inputs,
+                             const CUDADeviceArray<int> input_cols,
+                             const int output_rows, const int output_cols,
+                             T* output) {
+  int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
+  int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
+  int segment = upper_bound<int>(input_cols.data, input_cols.size, tid_x) - 1;
+
+  int curr_offset = input_cols.data[segment];
+  int curr_segment = segment;
+  for (; tid_x < output_cols; tid_x += blockDim.x * gridDim.x) {
+    T curr_col_offset;
+    while ((curr_col_offset = input_cols.data[curr_segment + 1]) <= tid_x) {
+      curr_offset = curr_col_offset;
+      ++curr_segment;
+    }
+
+    int local_col = tid_x - curr_offset;
+    int segment_width = curr_col_offset - curr_offset;
+    const T* input_ptr = inputs.data[curr_segment];
+
+    for (; tid_y < output_rows; tid_y += blockDim.y * gridDim.y)
+      output[tid_y * output_cols + tid_x] =
+          input_ptr[tid_y * segment_width + local_col];
+  }
+}
+
+/*
+ * All tensors' dimension should be the same.
+ */
+template <typename T>
+class ConcatFunctor<platform::CUDADeviceContext, T> {
+ public:
+  void operator()(const platform::CUDADeviceContext& context,
+                  std::vector<framework::Tensor>& input, const int axis,
+                  framework::Tensor* output) {
+    // assume the the max size of input is less than 8 and see the performance
+    // save origin dim
+    int num = input.size();
+    // std::vector<paddle::framework::DDim> origin_dim(num);
+    // for (int j = 0; j < num; ++j) {
+    //   origin_dim[j] = input[j].dims();
+    // }
+    auto out_dim = output->dims();
+
+    // get the matrix size
+    int rows = 1;
+    auto dim_0 = input[0].dims();
+    for (int i = 0; i < axis; ++i) {
+      rows *= dim_0[i];
+    }
+    int cols = input[0].numel() / rows;
+    int out_rows = rows, out_cols = 0;
+    bool sameShape = true;
+
+    CUDADeviceArray<const T*> inputs_data;
+    CUDADeviceArray<int> inputs_cols;
+    inputs_data.size = num;
+    inputs_cols.size = num + 1;
+    inputs_cols.data[0] = 0;
+    // reshape to matrix
+    // check input shape is valid
+    for (int i = 0; i < num; ++i) {
+      int t_cols = input[i].numel() / rows;
+      if (sameShape) {
+        if (t_cols != cols) sameShape = false;
+      }
+      out_cols += t_cols;
+      input[i].Resize({rows, t_cols});
+      inputs_cols.data[i + 1] = out_cols;
+      inputs_data.data[i] = input[i].data<T>();
+    }
+    output->Resize({out_rows, out_cols});
+
+    // computation
+    const int kThreadsPerBlock = 256;
+    int block_cols = std::min(out_cols, kThreadsPerBlock);
+    int block_rows = std::max(kThreadsPerBlock / block_cols, 1);
+    dim3 block_size = dim3(block_cols, block_rows, 1);
+
+    int grid_cols = (out_cols + block_cols - 1) / block_cols;
+    int grid_rows = (out_rows + block_rows - 1) / block_rows;
+    dim3 grid_size = dim3(grid_cols, grid_rows, 1);
+
+    KernelConcat<<<grid_size, block_size, 0, context.stream()>>>(
+        inputs_data, inputs_cols, out_rows, out_cols, output->data<T>());
+
+    // recover origin dim
+    // for (int j = 0; j < num; ++j) {
+    //  input[j].Resize(origin_dim[j]);
+    // }
+    output->Resize(out_dim);
+  }
+};
+
+template class ConcatFunctor<platform::CUDADeviceContext, int>;
+template class ConcatFunctor<platform::CUDADeviceContext, int64_t>;
+template class ConcatFunctor<platform::CUDADeviceContext, float>;
+template class ConcatFunctor<platform::CUDADeviceContext, double>;
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/math/concat.h

+/* Copyright (c) 2018 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. */
+
+#pragma once
+#include "paddle/fluid/framework/tensor.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+/*
+ * the tensor's shape of input will be changed,
+ * so the second parameter is not const.
+ *
+ */
+template <typename DeviceContext, typename T>
+class ConcatFunctor {
+ public:
+  void operator()(const DeviceContext& context,
+                  std::vector<framework::Tensor>& input, const int axis,
+                  framework::Tensor* output);
+};
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/math/concat_test.cc

+/* Copyright (c) 2018 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/math/concat.h"
+#include <gtest/gtest.h>
+#include <vector>
+#include "paddle/fluid/framework/tensor_util.h"
+
+using namespace paddle::framework;
+using namespace paddle::platform;
+
+template <typename DeviceContext, typename Place>
+void testConcat() {
+  Tensor input_a_cpu;
+  Tensor input_b_cpu;
+  Tensor out_cpu;
+  Tensor input_a;
+  Tensor input_b;
+  Tensor out;
+
+  DeviceContext* context = new DeviceContext(Place());
+  //  DeviceContext context(Place());
+
+  /**
+   * cast1:
+   *    inputs:
+   *        t_a.shape: [2, 3, 4]
+   *        t_b.shape: [3, 3, 4]
+   *    output:
+   *        out.shape: [5, 3, 4]
+   */
+  auto dim_a = make_ddim({2, 3, 4});
+  auto dim_b = make_ddim({3, 3, 4});
+  auto dim_out = make_ddim({5, 3, 4});
+
+  input_a.mutable_data<int>(dim_a, Place());
+  input_b.mutable_data<int>(dim_b, Place());
+  out.mutable_data<int>(dim_out, Place());
+
+  if (paddle::platform::is_gpu_place(Place())) {
+    input_a_cpu.mutable_data<int>(dim_a, CPUPlace());
+    input_b_cpu.mutable_data<int>(dim_b, CPUPlace());
+    out_cpu.mutable_data<int>(dim_out, CPUPlace());
+  }
+
+  int* a_ptr;
+  int* b_ptr;
+  if (paddle::platform::is_gpu_place(Place())) {
+    a_ptr = input_a_cpu.data<int>();
+    b_ptr = input_b_cpu.data<int>();
+  } else {
+    a_ptr = input_a.data<int>();
+    b_ptr = input_b.data<int>();
+  }
+
+  for (int i = 0; i < 2 * 3 * 4; ++i) {
+    a_ptr[i] = i;
+  }
+  for (int i = 0; i < 3 * 3 * 4; ++i) {
+    b_ptr[i] = i;
+  }
+
+  if (paddle::platform::is_gpu_place(Place())) {
+    TensorCopy(input_a_cpu, Place(), *context, &input_a);
+    TensorCopy(input_b_cpu, Place(), *context, &input_b);
+  }
+
+  std::vector<Tensor> input;
+  input.push_back(input_a);
+  input.push_back(input_b);
+
+  paddle::operators::math::ConcatFunctor<DeviceContext, int> concat_functor;
+  concat_functor(*context, input, 0, &out);
+
+  // check the dim of input_a, input_b
+  PADDLE_ENFORCE_EQ(input_a.dims(), dim_a);
+  PADDLE_ENFORCE_EQ(input_b.dims(), dim_b);
+
+  int* out_ptr;
+  if (paddle::platform::is_gpu_place(Place())) {
+    TensorCopy(out, CPUPlace(), *context, &out_cpu);
+    out_ptr = out_cpu.data<int>();
+  } else {
+    out_ptr = out.data<int>();
+  }
+
+  int cols = 2 * 3 * 4;
+  int idx_a = 0, idx_b = 0;
+  for (int j = 0; j < 5 * 3 * 4; ++j) {
+    if (j >= cols) {
+      PADDLE_ENFORCE_EQ(out_ptr[j], b_ptr[idx_b]);
+      ++idx_b;
+    } else {
+      PADDLE_ENFORCE_EQ(out_ptr[j], a_ptr[idx_a]);
+      ++idx_a;
+    }
+  }
+  //
+  /**
+    * cast2:
+    *    inputs:
+    *        t_a.shape: [2, 3, 4]
+    *        t_b.shape: [2, 4, 4]
+    *    output:
+    *        out.shape: [2, 7, 4]
+    */
+  dim_a = make_ddim({2, 3, 4});
+  dim_b = make_ddim({2, 4, 4});
+  dim_out = make_ddim({2, 7, 4});
+
+  input_a.Resize(dim_a);
+  input_b.Resize(dim_b);
+  out.Resize(dim_out);
+  if (paddle::platform::is_gpu_place(Place())) {
+    input_a_cpu.Resize(dim_a);
+    input_b_cpu.Resize(dim_b);
+    out_cpu.Resize(dim_out);
+  }
+
+  if (paddle::platform::is_gpu_place(Place())) {
+    a_ptr = input_a_cpu.data<int>();
+    b_ptr = input_b_cpu.data<int>();
+  } else {
+    a_ptr = input_a.data<int>();
+    b_ptr = input_b.data<int>();
+  }
+
+  for (int i = 0; i < 2 * 3 * 4; ++i) {
+    a_ptr[i] = i;
+  }
+  for (int i = 0; i < 2 * 4 * 4; ++i) {
+    b_ptr[i] = i;
+  }
+
+  if (paddle::platform::is_gpu_place(Place())) {
+    TensorCopy(input_a_cpu, Place(), *context, &input_a);
+    TensorCopy(input_b_cpu, Place(), *context, &input_b);
+  }
+
+  input.clear();
+  input.push_back(input_a);
+  input.push_back(input_b);
+
+  concat_functor(*context, input, 1, &out);
+
+  // check the dim of input_a, input_b
+  PADDLE_ENFORCE_EQ(input_a.dims(), dim_a);
+  PADDLE_ENFORCE_EQ(input_b.dims(), dim_b);
+
+  if (paddle::platform::is_gpu_place(Place())) {
+    TensorCopy(out, CPUPlace(), *context, &out_cpu);
+    out_ptr = out_cpu.data<int>();
+  } else {
+    out_ptr = out.data<int>();
+  }
+
+  cols = 3 * 4;
+  idx_a = 0, idx_b = 0;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 28; ++j) {
+      if (j >= cols) {
+        PADDLE_ENFORCE_EQ(out_ptr[i * 28 + j], b_ptr[idx_b]);
+        ++idx_b;
+      } else {
+        PADDLE_ENFORCE_EQ(out_ptr[i * 28 + j], a_ptr[idx_a]);
+        ++idx_a;
+      }
+    }
+  }
+
+  /**
+    * cast3:
+    *    inputs:
+    *        t_a.shape: [2, 3, 5]
+    *        t_b.shape: [2, 3, 4]
+    *    output:
+    *        out.shape: [2, 3, 9]
+    */
+  dim_a = make_ddim({2, 3, 4});
+  dim_b = make_ddim({2, 3, 5});
+  dim_out = make_ddim({2, 3, 9});
+
+  input_a.Resize(dim_a);
+  input_b.Resize(dim_b);
+  out.Resize(dim_out);
+  if (paddle::platform::is_gpu_place(Place())) {
+    input_a_cpu.Resize(dim_a);
+    input_b_cpu.Resize(dim_b);
+    out_cpu.Resize(dim_out);
+  }
+
+  if (paddle::platform::is_gpu_place(Place())) {
+    a_ptr = input_a_cpu.data<int>();
+    b_ptr = input_b_cpu.data<int>();
+  } else {
+    a_ptr = input_a.data<int>();
+    b_ptr = input_b.data<int>();
+  }
+
+  for (int i = 0; i < 2 * 3 * 4; ++i) {
+    a_ptr[i] = i;
+  }
+  for (int i = 0; i < 2 * 3 * 5; ++i) {
+    b_ptr[i] = i;
+  }
+
+  if (paddle::platform::is_gpu_place(Place())) {
+    TensorCopy(input_a_cpu, Place(), *context, &input_a);
+    TensorCopy(input_b_cpu, Place(), *context, &input_b);
+  }
+
+  input.clear();
+  input.push_back(input_a);
+  input.push_back(input_b);
+
+  concat_functor(*context, input, 2, &out);
+
+  // check the dim of input_a, input_b
+  PADDLE_ENFORCE_EQ(input_a.dims(), dim_a);
+  PADDLE_ENFORCE_EQ(input_b.dims(), dim_b);
+
+  if (paddle::platform::is_gpu_place(Place())) {
+    TensorCopy(out, CPUPlace(), *context, &out_cpu);
+    out_ptr = out_cpu.data<int>();
+  } else {
+    out_ptr = out.data<int>();
+  }
+
+  // check the data
+  cols = 4;
+  idx_a = 0, idx_b = 0;
+  for (int i = 0; i < 6; ++i) {
+    for (int j = 0; j < 9; ++j) {
+      if (j >= cols) {
+        PADDLE_ENFORCE_EQ(out_ptr[i * 9 + j], b_ptr[idx_b]);
+        ++idx_b;
+      } else {
+        PADDLE_ENFORCE_EQ(out_ptr[i * 9 + j], a_ptr[idx_a]);
+        ++idx_a;
+      }
+    }
+  }
+}
+
+TEST(math, concat) {
+  testConcat<paddle::platform::CPUDeviceContext, paddle::platform::CPUPlace>();
+#ifdef PADDLE_WITH_CUDA
+  testConcat<paddle::platform::CUDADeviceContext,
+             paddle::platform::CUDAPlace>();
+#endif
+}