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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/GLES_COMPUTE/functions/GCFullyConnectedLayer.h"
25 
26 #include "arm_compute/core/Validate.h"
27 #include "arm_compute/runtime/GLES_COMPUTE/GCScheduler.h"
28 #include "support/MemorySupport.h"
29 
30 #include <algorithm>
31 
32 using namespace arm_compute;
33 
configure(const IGCTensor * input,IGCTensor * output)34 void GCFullyConnectedLayerReshapeWeights::configure(const IGCTensor *input, IGCTensor *output)
35 {
36     auto k = arm_compute::support::cpp14::make_unique<GCTransposeKernel>();
37     k->configure(input, output);
38     _kernel = std::move(k);
39 }
40 
GCFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager,IWeightsManager * weights_manager)41 GCFullyConnectedLayer::GCFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager)
42     : _memory_group(std::move(memory_manager)), _weights_manager(std::move(weights_manager)), _im2col_kernel(), _reshape_weights_kernel(), _mm_kernel(), _accumulate_biases_kernel(), _im2col_output(),
43       _reshape_weights_output(), _original_weights(nullptr), _are_weights_reshaped(true), _is_fc_after_conv(true), _accumulate_biases(false)
44 {
45 }
46 
configure_conv_fc(const IGCTensor * input,const IGCTensor * weights,IGCTensor * output)47 void GCFullyConnectedLayer::configure_conv_fc(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output)
48 {
49     ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))));
50 
51     const DataType dt = input->info()->data_type();
52 
53     // If the fully connected layer is called after a convolution layer, the input tensor must be linearized
54 
55     // Initialize output tensor for im2col
56     TensorShape shape_im2col;
57     shape_im2col.set(0, input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2));
58     shape_im2col.set(1, input->info()->dimension(3));
59     shape_im2col.set(2, input->info()->dimension(4));
60     shape_im2col.set(3, input->info()->dimension(5));
61     _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, dt));
62 
63     // Configure im2col kernel
64     _memory_group.manage(&_im2col_output);
65     _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);
66 
67     // Configure matrix multiply kernel
68     _mm_kernel.configure(&_im2col_output, weights, output, 1.0f, false);
69 
70     // Allocate the output tensor for im2col once all the configure methods have been called
71     _im2col_output.allocator()->allocate();
72 }
73 
configure_fc_fc(const IGCTensor * input,const IGCTensor * weights,IGCTensor * output)74 void GCFullyConnectedLayer::configure_fc_fc(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output)
75 {
76     ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));
77 
78     // Configure matrix multiply kernel
79     _mm_kernel.configure(input, weights, output, 1.0f, false);
80 }
81 
configure(const IGCTensor * input,const IGCTensor * weights,const IGCTensor * biases,IGCTensor * output,FullyConnectedLayerInfo fc_info)82 void GCFullyConnectedLayer::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output,
83                                       FullyConnectedLayerInfo fc_info)
84 {
85     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16);
86     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
87     ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 2);
88 
89     _original_weights     = weights;
90     _are_weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
91     _is_fc_after_conv     = true;
92     _accumulate_biases    = false;
93 
94     if(biases != nullptr)
95     {
96         ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
97 
98         _accumulate_biases = true;
99 
100         // Configure accumulate biases kernel
101         _accumulate_biases_kernel.configure(output, biases);
102     }
103 
104     // With the Fully Connected layer we can have 4 different cases:
105     //  1) Convolution layer -> Fully Connected layer without batches
106     //  2) Fully Connected layer -> Fully Connected layer without batches
107     //  3) Convolution layer -> Fully Connected layer with batches
108     //  4) Fully Connected layer -> Fully Connected layer with batches
109 
110     const IGCTensor *weights_to_use = weights;
111 
112     if(!_are_weights_reshaped)
113     {
114         weights_to_use = &_reshape_weights_output;
115 
116         // Reshape the weights
117         _reshape_weights_kernel.configure(weights, &_reshape_weights_output);
118     }
119 
120     // Check if we have a fully connected layer with batches
121     const bool is_batched_fc_layer = output->info()->dimension(1) > 1;
122 
123     if(is_batched_fc_layer)
124     {
125         _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->info()->tensor_shape().cbegin() + 3,
126                                                                                   input->info()->tensor_shape().cend(),
127                                                                                   output->info()->tensor_shape().cbegin() + 1));
128     }
129     else
130     {
131         _is_fc_after_conv = input->info()->num_dimensions() > 1;
132     }
133 
134     if(_is_fc_after_conv)
135     {
136         // Fully Connected layer after a Convolution Layer without batches
137         configure_conv_fc(input, weights_to_use, output);
138     }
139     else
140     {
141         // Fully Connected layer after a Fully Connected Layer without batches
142         configure_fc_fc(input, weights_to_use, output);
143     }
144 
145     ARM_COMPUTE_ERROR_ON(fc_info.retain_internal_weights && _reshape_weights_output.gc_buffer() == 0);
146     _are_weights_reshaped = _are_weights_reshaped || fc_info.retain_internal_weights;
147 }
148 
run()149 void GCFullyConnectedLayer::run()
150 {
151     prepare();
152 
153     MemoryGroupResourceScope scope_mg(_memory_group);
154 
155     // Linearize input if it comes from a convolutional layer
156     if(_is_fc_after_conv)
157     {
158         GCScheduler::get().dispatch(_im2col_kernel, false);
159     }
160 
161     if(!_are_weights_reshaped || _is_fc_after_conv)
162     {
163         GCScheduler::get().memory_barrier();
164     }
165 
166     // Run matrix multiply
167     GCScheduler::get().dispatch(_mm_kernel, !_accumulate_biases);
168 
169     // Accumulate biases if provided
170     if(_accumulate_biases)
171     {
172         GCScheduler::get().memory_barrier();
173 
174         GCScheduler::get().dispatch(_accumulate_biases_kernel);
175     }
176 }
177 
prepare()178 void GCFullyConnectedLayer::prepare()
179 {
180     // Reshape of the weights (happens only once)
181     if(!_are_weights_reshaped)
182     {
183         ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
184 
185         // Run reshape weights kernel and mark weights as unused
186         _reshape_weights_output.allocator()->allocate();
187         _reshape_weights_kernel.run();
188 
189         // Mark original weights tensor as unused
190         _original_weights->mark_as_unused();
191 
192         _are_weights_reshaped = true;
193     }
194 }
195