Compute Library
 22.11
ClGemmLowpReductionKernel.h
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24 #ifndef ARM_COMPUTE_CL_GEMMLOWP_REDUCTION_KERNEL_H
25 #define ARM_COMPUTE_CL_GEMMLOWP_REDUCTION_KERNEL_H
26 
28 #include "src/core/common/Macros.h"
30 #include "src/gpu/cl/IClKernel.h"
31 
32 namespace arm_compute
33 {
34 namespace opencl
35 {
36 namespace kernels
37 {
38 /** Common interface for all OpenCL reduction kernels */
40 {
41 public:
44  /** Initialise the kernel's input and output.
45  *
46  * @param[in] compile_context The compile context to be used.
47  * @param[in] input Input tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8.
48  * @param[out] output Output row-vector of sums of all the entries in each row/col of input tensor. Data type supported: S32
49  * @param[in] info Kernel metadata:
50  * - k Number of matrix columns/rows depending on the type of reduction.
51  * - is_reshaped True if the matrix has been reshaped.
52  * - scalar Scalar value to multiply each reduced column/row by.
53  * - mul_byscalar True if each reduced column/row must be multiplied by a scalar value.
54  */
55  virtual void configure(const CLCompileContext &compile_context, const ITensorInfo *input, ITensorInfo *output, const GEMMLowpReductionKernelInfo &info) = 0;
56 };
57 
58 /** OpenCL kernel used to compute the row-vectors of sums of all the entries in each row of Matrix A.
59  *
60  * @note This stage is needed to handle the offset of matrix product
61  * https://github.com/google/gemmlowp/blob/master/doc/low-precision.md
62  */
64 {
65 public:
66  /** Initialise the kernel's input and output.
67  *
68  * @param[in] compile_context The compile context to be used.
69  * @param[in] mtx_a Input tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8.
70  * @param[out] vector_sum_row Output row-vector of sums of all the entries in each row of mtx_a. Data type supported: S32
71  * @param[in] info Kernel metadata:
72  * - k Number of matrix columns/rows depending on the type of reduction.
73  * - is_reshaped True if the matrix has been reshaped.
74  * - scalar Scalar value to multiply each reduced column/row by.
75  * - mul_byscalar True if each reduced column/row must be multiplied by a scalar value.
76  */
77  void configure(const CLCompileContext &compile_context, const ITensorInfo *mtx_a, ITensorInfo *vector_sum_row, const GEMMLowpReductionKernelInfo &info) override;
78  /** Static function to check if given info will lead to a valid configuration
79  *
80  * Similar to @ref ClGemmLowpQuantizeDownInt32ScaleByFixedPointKernel::configure()
81  *
82  * @return a status
83  */
84  static Status validate(const ITensorInfo *mtx_a, const ITensorInfo *vector_sum_row, const GEMMLowpReductionKernelInfo &info);
85 
86  // Inherited methods overridden:
87  void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
88 };
89 
90 /** OpenCL kernel used to compute the row-vectors of sums of all the entries in each column of Matrix B.
91  *
92  * @note This stage is needed to handle the offset of matrix product
93  * https://github.com/google/gemmlowp/blob/master/doc/low-precision.md
94  */
96 {
97 public:
98  /** Initialise the kernel's input and output.
99  *
100  * @param[in] compile_context The compile context to be used.
101  * @param[in] mtx_b Input tensor. Data type supported: Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL.
102  * @param[out] vector_sum_col Output row-vector of sums of all the entries in each column of mtx_b. Data type supported: S32
103  * @param[in] info Kernel metadata:
104  * - k Number of matrix columns/rows depending on the type of reduction.
105  * - is_reshaped True if the matrix has been reshaped.
106  * - scalar Scalar value to multiply each reduced column/row by.
107  * - mul_byscalar True if each reduced column/row must be multiplied by a scalar value.
108  */
109  void configure(const CLCompileContext &compile_context, const ITensorInfo *mtx_b, ITensorInfo *vector_sum_col, const GEMMLowpReductionKernelInfo &info) override;
110  /** Static function to check if given info will lead to a valid configuration
111  *
112  * Similar to @ref ClGemmLowpQuantizeDownInt32ScaleByFixedPointKernel::configure()
113  *
114  * @return a status
115  */
116  static Status validate(const ITensorInfo *mtx_b, const ITensorInfo *vector_sum_col, const GEMMLowpReductionKernelInfo &info);
117 
118  // Inherited methods overridden:
119  void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
120 };
121 } // namespace kernels
122 } // namespace opencl
123 } // namespace arm_compute
124 #endif /* ARM_COMPUTE_CL_GEMMLOWP_REDUCTION_KERNEL_H */
const Window & window() const
The maximum window the kernel can be executed on.
Definition: IKernel.cpp:28
Store the tensor's metadata.
Definition: ITensorInfo.h:40
Status class.
Definition: Error.h:52
Common interface for all the OpenCL kernels.
Definition: ICLKernel.h:80
Copyright (c) 2017-2022 Arm Limited.
Common interface for all OpenCL reduction kernels.
ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(IClGemmLowpReductionKernel)
OpenCL kernel used to compute the row-vectors of sums of all the entries in each column of Matrix B...
CLCompileContext class.
ScaleKernelInfo info(interpolation_policy, default_border_mode, PixelValue(), sampling_policy, false)
virtual void configure(const CLCompileContext &compile_context, const ITensorInfo *input, ITensorInfo *output, const GEMMLowpReductionKernelInfo &info)=0
Initialise the kernel's input and output.
Tensor packing service.
Definition: ITensorPack.h:39
OpenCL kernel used to compute the row-vectors of sums of all the entries in each row of Matrix A...
Describe a multidimensional execution window.
Definition: Window.h:39
virtual void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
Enqueue the OpenCL kernel to process the given window on the passed OpenCL command queue...
Definition: ICLKernel.h:344
Status validate(const ITensorInfo *scores_in, const ITensorInfo *boxes_in, const ITensorInfo *batch_splits_in, const ITensorInfo *scores_out, const ITensorInfo *boxes_out, const ITensorInfo *classes, const ITensorInfo *batch_splits_out, const ITensorInfo *keeps, const ITensorInfo *keeps_size, const BoxNMSLimitInfo info)