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263 lines
6.6 KiB
263 lines
6.6 KiB
7 years ago
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/operators/math/concat.h"
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#include <gtest/gtest.h>
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#include <vector>
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#include "paddle/fluid/framework/tensor_util.h"
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using namespace paddle::framework;
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using namespace paddle::platform;
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template <typename DeviceContext, typename Place>
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void testConcat() {
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Tensor input_a_cpu;
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Tensor input_b_cpu;
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Tensor out_cpu;
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Tensor input_a;
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Tensor input_b;
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Tensor out;
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DeviceContext* context = new DeviceContext(Place());
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// DeviceContext context(Place());
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/**
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* cast1:
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* inputs:
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* t_a.shape: [2, 3, 4]
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* t_b.shape: [3, 3, 4]
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* output:
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* out.shape: [5, 3, 4]
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*/
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auto dim_a = make_ddim({2, 3, 4});
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auto dim_b = make_ddim({3, 3, 4});
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auto dim_out = make_ddim({5, 3, 4});
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input_a.mutable_data<int>(dim_a, Place());
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input_b.mutable_data<int>(dim_b, Place());
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out.mutable_data<int>(dim_out, Place());
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if (paddle::platform::is_gpu_place(Place())) {
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input_a_cpu.mutable_data<int>(dim_a, CPUPlace());
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input_b_cpu.mutable_data<int>(dim_b, CPUPlace());
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out_cpu.mutable_data<int>(dim_out, CPUPlace());
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}
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int* a_ptr;
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int* b_ptr;
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if (paddle::platform::is_gpu_place(Place())) {
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a_ptr = input_a_cpu.data<int>();
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b_ptr = input_b_cpu.data<int>();
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} else {
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a_ptr = input_a.data<int>();
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b_ptr = input_b.data<int>();
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}
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for (int i = 0; i < 2 * 3 * 4; ++i) {
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a_ptr[i] = i;
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}
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for (int i = 0; i < 3 * 3 * 4; ++i) {
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b_ptr[i] = i;
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}
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if (paddle::platform::is_gpu_place(Place())) {
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TensorCopy(input_a_cpu, Place(), *context, &input_a);
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TensorCopy(input_b_cpu, Place(), *context, &input_b);
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}
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std::vector<Tensor> input;
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input.push_back(input_a);
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input.push_back(input_b);
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paddle::operators::math::ConcatFunctor<DeviceContext, int> concat_functor;
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concat_functor(*context, input, 0, &out);
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// check the dim of input_a, input_b
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PADDLE_ENFORCE_EQ(input_a.dims(), dim_a);
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PADDLE_ENFORCE_EQ(input_b.dims(), dim_b);
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int* out_ptr;
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if (paddle::platform::is_gpu_place(Place())) {
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TensorCopy(out, CPUPlace(), *context, &out_cpu);
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out_ptr = out_cpu.data<int>();
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} else {
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out_ptr = out.data<int>();
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}
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int cols = 2 * 3 * 4;
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int idx_a = 0, idx_b = 0;
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for (int j = 0; j < 5 * 3 * 4; ++j) {
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if (j >= cols) {
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PADDLE_ENFORCE_EQ(out_ptr[j], b_ptr[idx_b]);
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++idx_b;
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} else {
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PADDLE_ENFORCE_EQ(out_ptr[j], a_ptr[idx_a]);
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++idx_a;
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}
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}
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//
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/**
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* cast2:
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* inputs:
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* t_a.shape: [2, 3, 4]
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* t_b.shape: [2, 4, 4]
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* output:
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* out.shape: [2, 7, 4]
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*/
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dim_a = make_ddim({2, 3, 4});
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dim_b = make_ddim({2, 4, 4});
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dim_out = make_ddim({2, 7, 4});
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input_a.Resize(dim_a);
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input_b.Resize(dim_b);
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out.Resize(dim_out);
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if (paddle::platform::is_gpu_place(Place())) {
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input_a_cpu.Resize(dim_a);
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input_b_cpu.Resize(dim_b);
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out_cpu.Resize(dim_out);
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}
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if (paddle::platform::is_gpu_place(Place())) {
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a_ptr = input_a_cpu.data<int>();
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b_ptr = input_b_cpu.data<int>();
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} else {
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a_ptr = input_a.data<int>();
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b_ptr = input_b.data<int>();
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}
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for (int i = 0; i < 2 * 3 * 4; ++i) {
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a_ptr[i] = i;
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}
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for (int i = 0; i < 2 * 4 * 4; ++i) {
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b_ptr[i] = i;
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}
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if (paddle::platform::is_gpu_place(Place())) {
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TensorCopy(input_a_cpu, Place(), *context, &input_a);
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TensorCopy(input_b_cpu, Place(), *context, &input_b);
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}
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input.clear();
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input.push_back(input_a);
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input.push_back(input_b);
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concat_functor(*context, input, 1, &out);
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// check the dim of input_a, input_b
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PADDLE_ENFORCE_EQ(input_a.dims(), dim_a);
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PADDLE_ENFORCE_EQ(input_b.dims(), dim_b);
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if (paddle::platform::is_gpu_place(Place())) {
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TensorCopy(out, CPUPlace(), *context, &out_cpu);
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out_ptr = out_cpu.data<int>();
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} else {
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out_ptr = out.data<int>();
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}
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cols = 3 * 4;
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idx_a = 0, idx_b = 0;
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 28; ++j) {
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if (j >= cols) {
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PADDLE_ENFORCE_EQ(out_ptr[i * 28 + j], b_ptr[idx_b]);
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++idx_b;
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} else {
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PADDLE_ENFORCE_EQ(out_ptr[i * 28 + j], a_ptr[idx_a]);
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++idx_a;
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}
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}
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}
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/**
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* cast3:
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* inputs:
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* t_a.shape: [2, 3, 5]
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* t_b.shape: [2, 3, 4]
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* output:
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* out.shape: [2, 3, 9]
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*/
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dim_a = make_ddim({2, 3, 4});
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dim_b = make_ddim({2, 3, 5});
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dim_out = make_ddim({2, 3, 9});
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input_a.Resize(dim_a);
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input_b.Resize(dim_b);
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out.Resize(dim_out);
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if (paddle::platform::is_gpu_place(Place())) {
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input_a_cpu.Resize(dim_a);
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input_b_cpu.Resize(dim_b);
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out_cpu.Resize(dim_out);
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}
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if (paddle::platform::is_gpu_place(Place())) {
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a_ptr = input_a_cpu.data<int>();
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b_ptr = input_b_cpu.data<int>();
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} else {
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a_ptr = input_a.data<int>();
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b_ptr = input_b.data<int>();
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}
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for (int i = 0; i < 2 * 3 * 4; ++i) {
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a_ptr[i] = i;
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}
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for (int i = 0; i < 2 * 3 * 5; ++i) {
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b_ptr[i] = i;
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}
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if (paddle::platform::is_gpu_place(Place())) {
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TensorCopy(input_a_cpu, Place(), *context, &input_a);
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TensorCopy(input_b_cpu, Place(), *context, &input_b);
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}
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input.clear();
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input.push_back(input_a);
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input.push_back(input_b);
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concat_functor(*context, input, 2, &out);
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// check the dim of input_a, input_b
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PADDLE_ENFORCE_EQ(input_a.dims(), dim_a);
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PADDLE_ENFORCE_EQ(input_b.dims(), dim_b);
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if (paddle::platform::is_gpu_place(Place())) {
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TensorCopy(out, CPUPlace(), *context, &out_cpu);
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out_ptr = out_cpu.data<int>();
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} else {
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out_ptr = out.data<int>();
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}
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// check the data
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cols = 4;
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idx_a = 0, idx_b = 0;
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for (int i = 0; i < 6; ++i) {
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for (int j = 0; j < 9; ++j) {
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if (j >= cols) {
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PADDLE_ENFORCE_EQ(out_ptr[i * 9 + j], b_ptr[idx_b]);
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++idx_b;
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} else {
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PADDLE_ENFORCE_EQ(out_ptr[i * 9 + j], a_ptr[idx_a]);
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++idx_a;
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}
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}
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}
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}
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TEST(math, concat) {
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testConcat<paddle::platform::CPUDeviceContext, paddle::platform::CPUPlace>();
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#ifdef PADDLE_WITH_CUDA
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testConcat<paddle::platform::CUDADeviceContext,
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paddle::platform::CUDAPlace>();
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#endif
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}
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