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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 #ifndef ARM_COMPUTE_CLCONVOLUTIONLAYER_H
25 #define ARM_COMPUTE_CLCONVOLUTIONLAYER_H
26 
27 #include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h"
28 #include "arm_compute/runtime/CL/functions/CLFFTConvolutionLayer.h"
29 #include "arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h"
30 #include "arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h"
31 #include "arm_compute/runtime/IFunction.h"
32 #include "arm_compute/runtime/IMemoryManager.h"
33 
34 #include <memory>
35 
36 namespace arm_compute
37 {
38 /** Basic function to compute the convolution layer. This function calls the following OpenCL kernels/functions:
39  *
40  * -# @ref CLGEMMConvolutionLayer
41  * -# @ref CLWinogradConvolutionLayer
42  * -# @ref CLDirectConvolutionLayer
43  * -# @ref CLFFTConvolutionLayer
44  *
45  * The function selects one of the algorithms mentioned above based on:
46  *      - The size of the kernel
47  *      - Number of input/output feature maps
48  *      - Amount of memory needed
49  *
50  * Generally GEMM-based convolution is executed when neither Winograd nor FFT nor Direct convolution can be performed.
51  *
52  * FP32 Algorithm| Filter Size                                                 |   Input/Output feature maps               |
53  * --------------|-------------------------------------------------------------|-------------------------------------------|
54  * Winograd      | 3x3 1x3 3x1 5x1 1x5 5x5(fast maths) 7x1 1x7                 |  Input channels is greater than 3         |
55  * FFT           | Squared kernels and greater than 9x9                        |  Input feature maps > Output feature maps |
56  * DirectConv    | 9x9                                                         |                                           |
57  * GEMM          | Any size                                                    |                                           |
58  *
59  * Winograd 5x5 requires fast maths enabled.
60  *
61  * FP16 Algorithm| Filter Size                |   Input/Output feature maps               |
62  * --------------|----------------------------|-------------------------------------------|
63  * Winograd      | 3x3 1x3 3x1 5x1 1x5 5x5    |  Input channels is greater than 3         |
64  * FFT           | Not supported              |                                           |
65  * DirectConv    | 9x9                        |                                           |
66  * GEMM          | Any size                   |                                           |
67  *
68  * Winograd FP16 requires fast maths enabled.
69  *
70  */
71 class CLConvolutionLayer : public IFunction
72 {
73 public:
74     /** Default constructor */
75     CLConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
76     /** Default Destructor */
77     ~CLConvolutionLayer();
78     /** Prevent instances of this class from being copied (As this class contains pointers) */
79     CLConvolutionLayer(const CLConvolutionLayer &) = delete;
80     /** Default move constructor */
81     CLConvolutionLayer(CLConvolutionLayer &&) = default;
82     /** Prevent instances of this class from being copied (As this class contains pointers) */
83     CLConvolutionLayer &operator=(const CLConvolutionLayer &) = delete;
84     /** Default move assignment operator */
85     CLConvolutionLayer &operator=(CLConvolutionLayer &&) = default;
86     /** Set the input and output tensors.
87      *
88      * @param[in]  input            Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
89      *                              while every optional dimension from 4 and above represent a batch of inputs.
90      *                              Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
91      * @param[in]  weights          Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
92      *                              Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
93      * @param[in]  biases           Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
94      *                              Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
95      * @param[out] output           Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
96      *                              Data types supported: Same as @p input.
97      * @param[in]  conv_info        Contains padding and stride information described in @ref PadStrideInfo.
98      * @param[in]  weights_info     Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel. Data type supported: Same as @p input.
99      * @param[in]  dilation         (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
100      * @param[in]  act_info         (Optional) Activation layer information in case of a fused activation.
101      * @param[in]  enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
102      *                              available which may introduce a drop of accuracy as well. Default is false
103      * @param[in]  num_groups       (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
104      */
105     void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(),
106                    const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false, unsigned int num_groups = 1);
107     /** Set the input and output tensors.
108      *
109      * @param[in]  compile_context  The compile context to be used.
110      * @param[in]  input            Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
111      *                              while every optional dimension from 4 and above represent a batch of inputs.
112      *                              Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
113      * @param[in]  weights          Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
114      *                              Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
115      * @param[in]  biases           Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
116      *                              Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
117      * @param[out] output           Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
118      *                              Data types supported: Same as @p input.
119      * @param[in]  conv_info        Contains padding and stride information described in @ref PadStrideInfo.
120      * @param[in]  weights_info     Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel. Data type supported: Same as @p input.
121      * @param[in]  dilation         (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
122      * @param[in]  act_info         (Optional) Activation layer information in case of a fused activation.
123      * @param[in]  enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
124      *                              available which may introduce a drop of accuracy as well. Default is false
125      * @param[in]  num_groups       (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
126      */
127     void configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info,
128                    const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false,
129                    unsigned int num_groups = 1);
130     /** Static function to check if given info will lead to a valid configuration of @ref CLConvolutionLayer
131      *
132      * @param[in] input            Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
133      *                             while every optional dimension from 4 and above represent a batch of inputs.
134      *                             Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
135      * @param[in] weights          Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
136      *                             Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
137      * @param[in] biases           Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported:Same as @p input.
138      * @param[in] output           Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
139      *                             Data types supported: Same as @p input.
140      * @param[in] conv_info        Contains padding and stride information described in @ref PadStrideInfo.
141      * @param[in] weights_info     Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel.
142      * @param[in] dilation         (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
143      * @param[in] act_info         (Optional) Activation layer information in case of a fused activation.
144      * @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
145      *                             available which may introduce a drop of accuracy as well. Default is false
146      * @param[in] num_groups       (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
147      *
148      * @return a status
149      */
150     static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
151                            const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false,
152                            unsigned int num_groups = 1);
153     /** Static function to check if given info will return the convolution called by @ref CLConvolutionLayer
154      *
155      * @param[in] input            Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
156      *                             while every optional dimension from 4 and above represent a batch of inputs.
157      *                             Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
158      * @param[in] weights          Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
159      *                             Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
160      * @param[in] output           Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
161      *                             Data types supported: Same as @p input.
162      * @param[in] conv_info        Contains padding and stride information described in @ref PadStrideInfo.
163      * @param[in] weights_info     Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel.
164      * @param[in] act_info         (Optional) Activation layer information in case of a fused activation.
165      * @param[in] gpu_target       Specifies the @p GPUTarget.
166      * @param[in] dilation         (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
167      * @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
168      *                             available which may introduce a drop of accuracy as well. Default is false
169      *
170      * @return a status
171      */
172     static ConvolutionMethod get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info,
173                                                     const WeightsInfo &weights_info, const ActivationLayerInfo &act_info, const GPUTarget gpu_target, const Size2D &dilation = Size2D(1U, 1U), bool enable_fast_math = false);
174     // Inherited methods overridden:
175     void run() override;
176     void prepare() override;
177 
178 private:
179     std::shared_ptr<IMemoryManager> _memory_manager;
180     std::unique_ptr<IFunction>      _function;
181 };
182 }
183 #endif /* ARM_COMPUTE_CLCONVOLUTIONLAYER_H */
184