1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
|
/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. 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.
*/
/*
* Copyright (c) 2017-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef __ARM_COMPUTE_CLFULLYCONNECTEDHYBRIDLAYER_H__
#define __ARM_COMPUTE_CLFULLYCONNECTEDHYBRIDLAYER_H__
#include "arm_compute/runtime/CL/ICLSimpleFunction.h"
#include "arm_compute/core/CL/kernels/CLGEMMMatrixAccumulateBiasesKernel.h"
#include "arm_compute/core/CL/kernels/CLMultiplyScaleFactorKernel.h"
#include "arm_compute/core/CL/kernels/CLQuantizationSymmetricKernel.h"
#include "arm_compute/core/CL/kernels/CLScaleFactorSymm8Kernel.h"
#include "arm_compute/core/CL/kernels/CLTransposeKernel.h"
#include "arm_compute/runtime/MemoryGroup.h"
#include "arm_compute/runtime/CL/CLTensor.h"
#include "arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h"
namespace arm_compute
{
/** Basic function to reshape the weights of Fully Connected layer with OpenCL. This function calls
* the following kernels:
*
* -# @ref CLTransposeKernel
*
* @note The fully connected layer accepts "weights" tensors only with 2 dimensions.
*/
class CLFullyConnectedHybridLayerReshapeWeights : public ICLSimpleFunction
{
public:
/** Set the input and output tensors.
*
* @param[in] input Weights tensor. The weights must be 2 dimensional. Data types supported:
* S8.
* @param[out] output Destination tensor which stores the transposed input tensor. Data type
* supported: Same as @p input.
*/
void configure(const ICLTensor *input, ICLTensor *output);
/** Static function to check if given info will lead to a valid configuration of @ref
* CLFullyConnectedHybridLayerReshapeWeights
*
* @param[in] input Weights tensor. The weights must be 2 dimensional. Data types supported:
* S8.
* @param[in] output Destination tensor which stores the transposed input tensor. Data type
* supported: Same as @p input.
*
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *output);
};
/** Basic function to compute a Fully Connected layer on OpenCL. This function calls the following
* OpenCL kernels:
*
* -# @ref CLIm2ColKernel (called when the input comes from a convolutional layer)
* -# @ref CLFullyConnectedHybridLayerReshapeWeights (if @p are_weights_reshaped is set to false
* and transpose_weights is set to true ) (called once)
* -# @ref CLGEMMLowpMatrixMultiplyCore (if quantized symmetric)
* -# @ref CLGEMMMatrixAccumulateBiasesKernel (if @p biases is not equal to nullptr)
*
* @note The fully connected layer accepts "weights" tensors only with 2 dimensions.
*/
class CLFullyConnectedHybridLayer : public IFunction
{
public:
/** Constructor */
CLFullyConnectedHybridLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
/** Prevent instances of this class from being copied (As this class contains pointers) */
CLFullyConnectedHybridLayer(const CLFullyConnectedHybridLayer &) = delete;
/** Default move constructor */
CLFullyConnectedHybridLayer(CLFullyConnectedHybridLayer &&) = default;
/** Prevent instances of this class from being copied (As this class contains pointers) */
CLFullyConnectedHybridLayer &operator=(const CLFullyConnectedHybridLayer &) = delete;
/** Default move assignment operator */
CLFullyConnectedHybridLayer &operator=(CLFullyConnectedHybridLayer &&) = default;
/** Set the input and output tensors.
*
* @param[in] input Source tensor. Data type supported: F16/F32.
* @param[in] weights Weights tensor. The weights must be 2 dimensional.
* If this function is called after a Convolution Layer, the (transposed)
* weights will have as many rows as the product of the first 3 input's dimensions.
* If it is called after another FullyConnected Layer, the (transposed)
* weights will have as many rows as the input's first dimension.
* Data type supported: S8.
* @param[in] biases Bias tensor. Can be nullptr. Data type supported:Same as @p input.
* @param[out] output Destination tensor. Its shape should be equal to the output of a matrix
* multiplication between:
* - The output of im2col on the input and the (transposed) 2D weights, if the
* function is called after a Convolution Layer
* - The input tensor and the (transposed) 2D weights, if the function is
* called after another FullyConnected Layer.
* Data type supported: Same as @p input.
* @param[in] fc_info (Optional) Fully connected layer additional info
*/
void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases,
ICLTensor *output, FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo());
/** Static function to check if given info will lead to a valid configuration of @ref
* CLFullyConnectedHybridLayer
*
* @param[in] input Source tensor info. Data type supported: F16/F32.
* @param[in] weights Weights tensor info. The weights must be 2 dimensional.
* If this function is called after a Convolution Layer, the (transposed)
* weights will have as many rows as the product of the first 3 input's dimensions.
* If it is called after another FullyConnected Layer, the (transposed)
* weights will have as many rows as the input's first dimension.
* Data type supported: S8.
* @param[in] biases Bias tensor info. Can be nullptr. Data type supported:Same as @p input.
* @param[out] output Destination tensor info. Its shape should be equal to the output of a
* matrix multiplication between:
* - The output of im2col on the input and the (transposed) 2D weights, if the
* function is called after a Convolution Layer
* - The input tensor and the (transposed) 2D weights, if the function is
* called after another FullyConnected Layer.
* Data type supported: Same as @p input.
* @param[in] fc_info (Optional) Fully connected layer additional info
*
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *weights,
const ITensorInfo *biases, const ITensorInfo *output,
FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo());
// Inherited methods override
void run() override;
void prepare() override;
private:
void configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output,
bool retain_internal_weights);
MemoryGroup _memory_group;
CLFullyConnectedHybridLayerReshapeWeights _reshape_weights_kernel;
CLScaleFactorSymm8Kernel _scale_factor_kernel;
CLQuantizationSymmetricKernel _quant_input_kernel;
CLGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
CLMultiplyScaleFactorKernel _multiply_scale_kernel;
CLGEMMMatrixAccumulateBiasesKernel _accumulate_biases_kernel; // TODO(COMPMID-1889): Use CLGEMM to
// add bias in
// CLFullyConnectedHybridLayer
CLTensor _reshape_weights_output;
CLTensor _quantized_input;
CLTensor _scale_factor;
CLTensor _gemmlowp_output;
bool _are_weights_reshaped;
bool _accumulate_biases;
bool _is_prepared;
const ICLTensor *_original_weights;
};
}
#endif /* __ARM_COMPUTE_CLFULLYCONNECTEDHYBRIDLAYER_H__ */
|
