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Michalis Spyrou7930db42018-11-22 17:36:28 +00001/*
Michele Di Giorgiod9eaf612020-07-08 11:12:57 +01002 * Copyright (c) 2018-2020 Arm Limited.
Michalis Spyrou7930db42018-11-22 17:36:28 +00003 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
24
25#include "arm_compute/runtime/CL/functions/CLArgMinMaxLayer.h"
26
Michalis Spyrou7930db42018-11-22 17:36:28 +000027#include "arm_compute/core/Error.h"
28#include "arm_compute/core/TensorInfo.h"
29#include "arm_compute/core/Types.h"
30#include "arm_compute/core/Validate.h"
Manuel Bottini7b9998d2019-10-21 17:59:07 +010031#include "arm_compute/core/utils/misc/ShapeCalculator.h"
Sang-Hoon Park68dd25f2020-10-19 16:00:11 +010032#include "src/core/CL/CLValidate.h"
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +010033#include "src/core/CL/kernels/CLArgMinMaxLayerKernel.h"
Sang-Hoon Park68dd25f2020-10-19 16:00:11 +010034#include "src/core/helpers/AutoConfiguration.h"
35#include "src/runtime/Utils.h"
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +010036#include "support/MemorySupport.h"
Michalis Spyrou7930db42018-11-22 17:36:28 +000037
38namespace arm_compute
39{
Sang-Hoon Park2697fd82019-10-15 16:49:24 +010040CLArgMinMaxLayer::CLArgMinMaxLayer(std::shared_ptr<IMemoryManager> memory_manager)
Michalis Spyrou2aad21a2020-07-02 12:43:53 +010041 : _memory_group(std::move(memory_manager)), _results_vector(), _not_reshaped_output(), _reduction_kernels_vector(), _reshape(), _num_of_stages(), _reduction_axis()
Michalis Spyrou7930db42018-11-22 17:36:28 +000042{
Sang-Hoon Park2697fd82019-10-15 16:49:24 +010043}
44
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +010045CLArgMinMaxLayer::~CLArgMinMaxLayer() = default;
46
Michalis Spyrou7930db42018-11-22 17:36:28 +000047Status CLArgMinMaxLayer::validate(const ITensorInfo *input, int axis, const ITensorInfo *output, const ReductionOperation &op)
48{
Manuel Bottini7b9998d2019-10-21 17:59:07 +010049 ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
Sheri Zhangc5b6d882020-06-26 14:46:59 +010050 ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
51 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S32, DataType::F16, DataType::F32);
Manuel Bottini7b9998d2019-10-21 17:59:07 +010052 ARM_COMPUTE_RETURN_ERROR_ON_MSG(op != ReductionOperation::ARG_IDX_MAX && op != ReductionOperation::ARG_IDX_MIN, "Invalid reduction operation");
53 ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= static_cast<int>(TensorShape::num_max_dimensions), "Reduction axis greater than max number of dimensions");
54 ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis");
Sang-Hoon Park68dd25f2020-10-19 16:00:11 +010055 const unsigned int num_of_stages = utils::calculate_number_of_stages_only_x_axis(input->dimension(0), axis);
Manuel Bottini7b9998d2019-10-21 17:59:07 +010056
57 DataType output_data_type = DataType::S32;
58 TensorInfo not_reshaped_output;
59 const auto input_num_channles = input->num_channels();
60 const auto input_qinfo = input->quantization_info();
61
62 if(output->total_size() != 0)
63 {
64 output_data_type = output->data_type();
65 const TensorInfo expected_output_shape = output->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_reduced_shape(input->tensor_shape(), axis, false));
66 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output_shape, output);
67 }
68
69 auto shape_before_reshape = input->tensor_shape();
70 shape_before_reshape.set(axis, 1);
71 auto initialize_tensorinfo = [](TensorInfo & ti, TensorShape shape, DataType data_type, int num_channels, QuantizationInfo qinfo)
72 {
73 ti.set_data_type(data_type).set_tensor_shape(shape).set_num_channels(num_channels).set_quantization_info(qinfo);
74 };
75
76 initialize_tensorinfo(not_reshaped_output, shape_before_reshape, output_data_type, input_num_channles, input_qinfo);
77
78 if(num_of_stages == 1)
79 {
80 ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, nullptr, &not_reshaped_output, axis, op));
81 }
82 else
83 {
84 // Create temporary tensor infos
85 std::vector<TensorInfo> sums_vector(num_of_stages - 1);
86
87 // Create intermediate tensor info
88 TensorShape shape{ input->tensor_shape() };
89
90 for(unsigned int i = 0; i < num_of_stages - 1; i++)
91 {
92 shape.set(0, ceil(shape.x() / 128.f));
93 sums_vector[i].set_data_type(input->data_type());
94 sums_vector[i].set_tensor_shape(shape);
95 sums_vector[i].set_num_channels(input->num_channels());
96 }
97
98 // Validate ReductionOperation only on first kernel
99 ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, nullptr, &sums_vector[0], axis, op));
100
101 // Validate ReductionOperation on intermediate stages
102 for(unsigned int i = 1; i < num_of_stages - 1; ++i)
103 {
104 ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, &sums_vector[i - 1], &sums_vector[i], axis, op));
105 }
106
107 // Validate ReductionOperation on the last stage
108 const unsigned int last_stage = num_of_stages - 1;
109 ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, &sums_vector[last_stage - 1], &not_reshaped_output, axis, op));
110 }
Michalis Spyrou2aad21a2020-07-02 12:43:53 +0100111 ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayer::validate(&not_reshaped_output, output));
Manuel Bottini7b9998d2019-10-21 17:59:07 +0100112 return Status{};
113}
114
115void CLArgMinMaxLayer::configure(const ICLTensor *input, int axis, ICLTensor *output, const ReductionOperation &op)
116{
Manuel Bottini2b84be52020-04-08 10:15:51 +0100117 configure(CLKernelLibrary::get().get_compile_context(), input, axis, output, op);
118}
119
120void CLArgMinMaxLayer::configure(const CLCompileContext &compile_context, const ICLTensor *input, int axis, ICLTensor *output, const ReductionOperation &op)
121{
Manuel Bottini7b9998d2019-10-21 17:59:07 +0100122 ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
Sang-Hoon Park68dd25f2020-10-19 16:00:11 +0100123 _num_of_stages = utils::calculate_number_of_stages_only_x_axis(input->info()->dimension(0), axis);
Manuel Bottini7b9998d2019-10-21 17:59:07 +0100124 _reduction_axis = axis;
125
126 const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->info()->tensor_shape(), axis, false);
127 DataType output_data_type = (output->info()->data_type() == DataType::UNKNOWN) ? DataType::S32 : output->info()->data_type();
128 auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true));
129
130 // Configure reduction operation kernels
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +0100131 _reduction_kernels_vector.reserve(_num_of_stages);
132
133 auto add_reduction_kernel = [this, &compile_context, axis, op](const ICLTensor * input, const ICLTensor * prev_output, ICLTensor * output)
134 {
135 _reduction_kernels_vector.emplace_back(support::cpp14::make_unique<CLArgMinMaxLayerKernel>());
136 _reduction_kernels_vector.back()->configure(compile_context, input, prev_output, output, axis, op);
137 };
Manuel Bottini7b9998d2019-10-21 17:59:07 +0100138
139 _memory_group.manage(&_not_reshaped_output);
140 // Create temporary tensors
141 if(_num_of_stages == 1)
142 {
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +0100143 add_reduction_kernel(input, nullptr, &_not_reshaped_output);
Manuel Bottini7b9998d2019-10-21 17:59:07 +0100144 }
145 else
146 {
147 _results_vector.resize(_num_of_stages - 1);
148 TensorShape shape{ input->info()->tensor_shape() };
149 for(unsigned int i = 0; i < _num_of_stages - 1; i++)
150 {
151 shape.set(0, ceil(shape.x() / 128.f));
152 _results_vector[i].allocator()->init(input->info()->clone()->set_tensor_shape(shape).set_data_type(output_data_type));
153 }
154
155 // Apply ReductionOperation only on first kernel
156 _memory_group.manage(&_results_vector[0]);
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +0100157 add_reduction_kernel(input, nullptr, &_results_vector[0]);
Manuel Bottini7b9998d2019-10-21 17:59:07 +0100158
159 // Apply ReductionOperation on intermediate stages
160 for(unsigned int i = 1; i < _num_of_stages - 1; ++i)
161 {
162 _memory_group.manage(&_results_vector[i]);
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +0100163 add_reduction_kernel(input, &_results_vector[i - 1], &_results_vector[i]);
Manuel Bottini7b9998d2019-10-21 17:59:07 +0100164 _results_vector[i - 1].allocator()->allocate();
165 }
166
167 // Apply ReductionOperation on the last stage
168 const unsigned int last_stage = _num_of_stages - 1;
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +0100169 add_reduction_kernel(input, &_results_vector[last_stage - 1], &_not_reshaped_output);
Manuel Bottini7b9998d2019-10-21 17:59:07 +0100170 _results_vector[last_stage - 1].allocator()->allocate();
171 }
Michalis Spyrou2aad21a2020-07-02 12:43:53 +0100172 _reshape.configure(compile_context, &_not_reshaped_output, output);
Manuel Bottini7b9998d2019-10-21 17:59:07 +0100173 _not_reshaped_output.allocator()->allocate();
Sang-Hoon Park2697fd82019-10-15 16:49:24 +0100174}
175
176void CLArgMinMaxLayer::run()
177{
Manuel Bottini7b9998d2019-10-21 17:59:07 +0100178 MemoryGroupResourceScope scope_mg(_memory_group);
179
180 for(unsigned int i = 0; i < _num_of_stages; ++i)
181 {
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +0100182 CLScheduler::get().enqueue(*_reduction_kernels_vector[i], false);
Manuel Bottini7b9998d2019-10-21 17:59:07 +0100183 }
Michalis Spyrou2aad21a2020-07-02 12:43:53 +0100184 _reshape.run();
Michalis Spyrou7930db42018-11-22 17:36:28 +0000185}
Sang-Hoon Park68dd25f2020-10-19 16:00:11 +0100186} // namespace arm_compute