1 /*
2 * Copyright (c) 2017-2020 Arm Limited.
3 *
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 #include "arm_compute/runtime/NEON/functions/NELocallyConnectedLayer.h"
25
26 #include "arm_compute/core/PixelValue.h"
27 #include "arm_compute/core/Utils.h"
28 #include "arm_compute/core/Validate.h"
29 #include "arm_compute/runtime/NEON/NEScheduler.h"
30 #include "src/core/NEON/kernels/NEIm2ColKernel.h"
31 #include "src/core/NEON/kernels/NELocallyConnectedMatrixMultiplyKernel.h"
32 #include "src/core/NEON/kernels/NEWeightsReshapeKernel.h"
33 #include "support/MemorySupport.h"
34
35 #include <cmath>
36 #include <tuple>
37
38 namespace arm_compute
39 {
40 namespace
41 {
calculate_shapes(const ITensorInfo * input,const ITensorInfo * weights,const ITensorInfo * biases,const ITensorInfo * output,const PadStrideInfo & conv_info,TensorShape & shape_wr,TensorShape & shape_im2col,TensorShape & shape_gemm)42 void calculate_shapes(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
43 TensorShape &shape_wr, TensorShape &shape_im2col, TensorShape &shape_gemm)
44 {
45 ARM_COMPUTE_UNUSED(output);
46
47 const unsigned int kernel_width = weights->dimension(0);
48 const unsigned int kernel_height = weights->dimension(1);
49
50 bool has_bias = (biases != nullptr);
51
52 // Get convolved dimensions
53 unsigned int conv_w = 0;
54 unsigned int conv_h = 0;
55 std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
56 conv_info);
57
58 const size_t mat_weights_cols = weights->dimension(3);
59 const size_t mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + ((has_bias) ? 1 : 0);
60 const size_t mat_weights_num = weights->dimension(4);
61
62 shape_wr = TensorShape(mat_weights_cols, mat_weights_rows, mat_weights_num);
63
64 const size_t mat_input_cols = mat_weights_rows;
65 const size_t mat_input_rows = conv_w * conv_h;
66
67 shape_im2col = input->tensor_shape();
68 shape_im2col.set(0, mat_input_cols);
69 shape_im2col.set(1, mat_input_rows);
70 shape_im2col.set(2, 1);
71
72 shape_gemm = shape_im2col;
73 shape_gemm.set(0, mat_weights_cols);
74 shape_gemm.set(1, mat_input_rows);
75 }
76 } // namespace
77 NELocallyConnectedLayer::~NELocallyConnectedLayer() = default;
78
NELocallyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)79 NELocallyConnectedLayer::NELocallyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)
80 : _memory_group(std::move(memory_manager)), _input_im2col(), _weights_reshape_kernel(), _mm_kernel(), _output_col2im(), _input_im2col_reshaped(), _weights_reshaped(), _gemm_output(),
81 _is_prepared(false), _original_weights(nullptr)
82 {
83 }
84
validate(const ITensorInfo * input,const ITensorInfo * weights,const ITensorInfo * biases,const ITensorInfo * output,const PadStrideInfo & conv_info)85 Status NELocallyConnectedLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
86 {
87 ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
88 ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != input->dimension(2));
89 ARM_COMPUTE_RETURN_ERROR_ON(!conv_info.padding_is_symmetric());
90
91 bool has_bias = (biases != nullptr);
92
93 if(has_bias)
94 {
95 ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
96 ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 2);
97 }
98
99 const unsigned int kernel_width = weights->dimension(0);
100 const unsigned int kernel_height = weights->dimension(1);
101
102 // Get convolved dimensions
103 unsigned int conv_w = 0;
104 unsigned int conv_h = 0;
105 std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
106 conv_info);
107
108 ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != conv_w) || (output->dimension(1) != conv_h), "Output shape does not match the expected one");
109 ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(4) != (conv_w * conv_h), "Weights shape does not match the expected one");
110
111 // Calculate intermediate buffer shapes
112 TensorShape shape_wr;
113 TensorShape shape_im2col;
114 TensorShape shape_gemm;
115 calculate_shapes(input, weights, biases, output, conv_info, shape_wr, shape_im2col, shape_gemm);
116
117 TensorInfo weights_reshaped_info(shape_wr, 1, weights->data_type());
118 TensorInfo input_im2col_reshaped_info(shape_im2col, 1, input->data_type());
119 TensorInfo gemm_output_info(shape_gemm, 1, input->data_type());
120
121 ARM_COMPUTE_RETURN_ON_ERROR(NEIm2Col::validate(input, &input_im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, has_bias));
122 ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, &weights_reshaped_info));
123 ARM_COMPUTE_RETURN_ON_ERROR(NELocallyConnectedMatrixMultiplyKernel::validate(&input_im2col_reshaped_info, &weights_reshaped_info, &gemm_output_info));
124 ARM_COMPUTE_RETURN_ON_ERROR(NECol2Im::validate(&gemm_output_info, output, Size2D(conv_w, conv_h)));
125
126 return Status{};
127 }
128
configure(const ITensor * input,const ITensor * weights,const ITensor * biases,ITensor * output,const PadStrideInfo & conv_info)129 void NELocallyConnectedLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info)
130 {
131 ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
132 ARM_COMPUTE_ERROR_THROW_ON(NELocallyConnectedLayer::validate(input->info(), weights->info(), biases == nullptr ? nullptr : biases->info(), output->info(), conv_info));
133
134 bool _has_bias = (biases != nullptr);
135 _is_prepared = false;
136 _original_weights = weights;
137
138 const unsigned int kernel_width = weights->info()->dimension(0);
139 const unsigned int kernel_height = weights->info()->dimension(1);
140
141 // Get convolved dimensions
142 unsigned int conv_w = 0;
143 unsigned int conv_h = 0;
144 std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
145 conv_info);
146
147 // Calculate intermediate buffer shapes
148 TensorShape shape_wr;
149 TensorShape shape_im2col;
150 TensorShape shape_gemm;
151 calculate_shapes(input->info(), weights->info(), biases == nullptr ? nullptr : biases->info(), output->info(), conv_info, shape_wr, shape_im2col, shape_gemm);
152
153 _weights_reshaped.allocator()->init(TensorInfo(shape_wr, 1, weights->info()->data_type()));
154 _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, input->info()->data_type()));
155 _gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, input->info()->data_type()));
156
157 // Manage intermediate buffers
158 _memory_group.manage(&_input_im2col_reshaped);
159 _memory_group.manage(&_gemm_output);
160
161 // Configure kernels
162 _input_im2col.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias);
163 _weights_reshape_kernel = arm_compute::support::cpp14::make_unique<NEWeightsReshapeKernel>();
164 _weights_reshape_kernel->configure(weights, biases, &_weights_reshaped);
165 _mm_kernel = arm_compute::support::cpp14::make_unique<NELocallyConnectedMatrixMultiplyKernel>();
166 _mm_kernel->configure(&_input_im2col_reshaped, &_weights_reshaped, &_gemm_output);
167 _output_col2im.configure(&_gemm_output, output, Size2D(conv_w, conv_h));
168
169 // Allocate intermediate tensors
170 _input_im2col_reshaped.allocator()->allocate();
171 _gemm_output.allocator()->allocate();
172 }
173
run()174 void NELocallyConnectedLayer::run()
175 {
176 prepare();
177
178 MemoryGroupResourceScope scope_mg(_memory_group);
179
180 // Run input reshaping
181 _input_im2col.run();
182
183 // Runs GEMM on reshaped matrices
184 NEScheduler::get().schedule(_mm_kernel.get(), Window::DimX);
185
186 // Reshape output matrix
187 _output_col2im.run();
188 }
189
prepare()190 void NELocallyConnectedLayer::prepare()
191 {
192 if(!_is_prepared)
193 {
194 ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
195
196 // Run weights reshaping and mark original weights tensor as unused
197 _weights_reshaped.allocator()->allocate();
198 NEScheduler::get().schedule(_weights_reshape_kernel.get(), 3);
199 _original_weights->mark_as_unused();
200
201 _is_prepared = true;
202 }
203 }
204 } // namespace arm_compute
205